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An IoT Based Predictive Connected Car Maintenance Approach

Authors:
  • CMR Institute of Technology Hyderabad TS India

Abstract and Figures

Internet of Things (IoT) is fast emerging and becoming an almost basic necessity in general life. The concepts of using technology in our daily life is not new, but with the advancements in technology, the impact of technology in daily activities of a person can be seen in almost all the aspects of life. Today, all aspects of our daily life, be it health of a person, his location, movement, etc. can be monitored and analyzed using information captured from various connected devices. This paper discusses one such use case, which can be implemented by the automobile industry, using technological advancements in the areas of IoT and Analytics. textquoteleftConnected Cartextquoteright is a terminology, often associated with cars and other passenger vehicles, which are capable of internet connectivity and sharing of various kinds of data with backend applications. The data being shared can be about the location and speed of the car, status of various parts/lubricants of the car, and if the car needs urgent service or not. Once data are transmitted to the backend services, various workflows can be created to take necessary actions, e.g. scheduling a service with the car service provider, or if large numbers of care are in the same location, then the traffic management system can take necessary action. textquoterightConnected carstextquoteright can also communicate with each other, and can send alerts to each other in certain scenarios like possible crash etc. This paper talks about how the concept of textquoteleftconnected carstextquoteright can be used to perform textquoteleftpredictive car maintenancetextquoteright. It also discusses how certain technology components, i.e., Eclipse Mosquito and Eclipse Paho can be used to implement a predictive connected car use case.
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International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 4, Nº3
- 16 - DOI: 10.9781/ijimai.2017.433
Abstract — Internet of Things (IoT) is fast emerging and
becoming an almost basic necessity in general life. The concepts
of using technology in our daily life is not new, but with the
advancements in technology, the impact of technology in daily
activities of a person can be seen in almost all the aspects of life.
Today, all aspects of our daily life, be it health of a person, his location,
movement, etc. can be monitored and analyzed using information
captured from various connected devices. This paper discusses
one such use case, which can be implemented by the automobile
industry, using technological advancements in the areas of IoT and
Analytics. ‘Connected Car’ is a terminology, often associated with
cars and other passenger vehicles, which are capable of internet
connectivity and sharing of various kinds of data with backend
applications. The data being shared can be about the location and
speed of the car, status of various parts/lubricants of the car, and
if the car needs urgent service or not. Once data are transmitted
to the backend services, various workflows can be created to take
necessary actions, e.g. scheduling a service with the car service
provider, or if large numbers of care are in the same location,
then the traffic management system can take necessary action.
’Connected cars’ can also communicate with each other, and can
send alerts to each other in certain scenarios like possible crash etc.
This paper talks about how the concept of ‘connected cars’ can be
used to perform ‘predictive car maintenance’. It also discusses how
certain technology components, i.e., Eclipse Mosquito and Eclipse
Paho can be used to implement a predictive connected car use case.
Keywords — Internet of Things, Connected Cars, Predictive
Maintenance, MQTT, Eclipse Mosquito, Eclipse Paho, Smart City.
I. InTROducTIOn
The automobile and fleet management industries, majority of
the consumers and the car service companies are following the
‘periodic maintenance’ for their automobiles. In periodic maintenance,
car owners are advised to take their cars for regular service and
maintenance either after certain specified time period or distance
covered. For example, it is generally advised to get car serviced
within three months of the last service date or after travelling 10000
kilometers, whichever comes first. Another instance where the car
can be taken out for emergency service/maintenance is after some
breakdown or malfunctioning of any part in the vehicle.
The way periodic maintenance works, is depicted in Fig. 1. [15]
A. Periodic Car Maintenance
Fig. 1 summarizes Periodic car maintenance. It can be explained
as a service/maintenance model, where a car undergoes a service/
maintenance either after a certain specified time period or on the
basis of distance covered, e.g. as shown in Fig. 1, during the lifetime
of a vehicle, regular services are carried out, as advised by the car
manufacturer. Similarly, a car can be serviced/repaired, if there is any
of the part gets faulty.
B. Drawbacks of Periodic Car Maintenance [15]
Some of the major drawbacks of periodic car maintenance are listed
below:
Higher cost of service, as vehicles are required to be get serviced
as per the schedule
Even if vehicle/parts are in perfect health, still service needs to
done and parts to be replaced
No way of knowing, if a part needs immediate attention, and can
result in breakdown of the vehicle
This breakdown could cost significant charges for the car owners
II. alTeRnaTe appROach TO peRIOdIc caR maInTenance
Instead of getting a car serviced periodically, if a system developed
using sensors and IoT [9] technology stack is used, which collect and
analyze fitness and running condition of different parts of the car, and
send this data to a centralized system. In this centralized system, data
received from these connected cars, can be analyzed further and if any
service is needed, a service request can be raised. This proposed system
can also generate emergency alerts, in case any part is about to break
down, thus avoiding car/part failure [15][17]
A. Advantage of Proposed system [18]
Reduction in service and maintenance costs, as only parts which
needs to be replaced or serviced
Real time alerts of possible part failure, thus avoiding breakdown
and costs associated with outages
Analytics and reporting dashboards can be used to view how the
car is performing over different periods of time and in different
locations
Driver’s driving habits can be analyzed and appropriate action can
be taken
Tour and cab providers can manage their fleet better, thus
maximizing profits
Target advertisements for monetization of data received from
connected cars (e.g. offering service discounts for car which needs
to be serviced etc.)
An IoT Based Predictive Connected Car Maintenance
Approach
Rohit Dhall1, Vijender Solanki2
1Enterprise Architect, HCL Technologies, Noida, India
2Vijender Solanki, Research Scholar, Anna University, Chennai, India
Fig. 1 Periodic Maintenance of Cars
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III. WhaT Is pRedIcTIVe caR maInTenance and Why We need IT?
A common practice, generally followed in automobile world is
‘periodic car maintenance’. In this, the car is supposed to undergo
periodic service and maintenance routine. When to get a car serviced, is
generally decided by either a specified time period or distance covered.
For example, it is generally advised to get the car serviced within six
months of the last service date or after travelling 10000 kilometers,
whichever happens first.
Now, the problem with ‘periodic car maintenance’ is that nobody is
sure if any part or lubricants really needs to be serviced/replaced. This
normally leads to parts/lubricants, which are in good condition, getting
changed/serviced. Another problem, which is generally faced is that
though scheduled ‘periodic car maintenance’ is still some time away,
there is some problem with a part, which needs immediate attention.
‘Periodic car maintenance’ cannot solve this problem, and only way to
know about this is after break down. So, two problems, associated with
this model of car servicing can be summarized as:
Higher service costs, as parts which are fine, will also be replaced
Unable to generate any alert, if any part needs immediate attention/
service, resulting in breakdown/outages
This is where ‘Connected Cars’ and ‘Predictive car Maintenance’
can help [15][16]. Connected cars can collect data, from different
sensors installed in the car, related to the health status of different
parts, and send it over the internet to backend applications, for
analytical and decision making purposes. One of the backend
analytical application, based on the health status of different parts,
can invoke a workflow and schedule an appointment with the service
provider, if some part needs immediate attention. Similarly, real
time alerts can be sent to concerned parties, in case something need
immediate attention.
This can result in considerable savings in terms of service and
maintenance charges of the car [18]. Now, only the parts which
actually need replacement, will be serviced. This data will be collected
and transmitted by different sensors fitted on the car for performing
health check of different parts oil health check, tire and pressure health
check, filters health check and so on.
IV. hOW ThIs WORk dIffeRs fROm OTheR WORk dOne In ThIs aRea
Good amount of research work is done on the Predictive maintenance
topic [1][4][6]. Some of the work talk about how to collect or read
sensor data (from cars, from manufacturing industrial machines etc.),
or propose a model to perform predictive analysis and so on [2][3]
[5][7]. This work proposes an IoT based approach [15] to collect this
data, send it to the cloud and perform predictive analytics on this huge
amount of data. The proposed approach is based on industry proven
protocols and products, some of which are Eclipse IoT’s [11] Eclipse
Mosquitto [13], Eclipse Paho[12] and MQTT[10] protocol. High level
IT architecture is also provided, so that same can be referenced by
people to build, extend and further improve the system based on this
architecture.
Finally, a simulation of the proposed architecture model is also
given, where a client GUI utility simulates the car sensor, and sends
data to the cloud.
V. pROpOsed TechnOlOgy fOR ImplemenTIng pRedIcTIVe
cOnnecTed caR maInTenance
This section introduces some of the important technology
components, which will be used in the proposed implementation of
‘Connected car’ use case for predictive maintenance.
A. MQTT Protocol
Message Queue Telemetry Transport (MQTT) is a light-weight
messaging protocol based on publish-subscribe model. MQTT uses
a client-server architecture where the client (such as a sensor device
on cars) connects to the MQTT server (called a broker) and publishes
messages to server topics. The broker forwards the messages to the
clients subscribed to topics. MQTT is well suited for constrained
environments where the devices have limited processing and memory
resources and the network bandwidth is low.
B. Deeper look into MQTT
MQTT is an extremely lightweight messaging protocol. Its publish/
subscribe architecture is designed to be open and easy to implement.
Single MQTT server can support up to thousands of remote clients. These
characteristics make MQTT ideal for use in constrained environments
where network bandwidth is low or remote devices that might have
limited processing capabilities and memory, need to be supported. The
MQTT protocol is based on publish/subscribe model. Publishers can
send the messages to the topics, configured on the MQTT server (also
called MQTT broker). Clients can subscribe to these topics and receive
whatever messages are published on those topics.
Fig. 2 depicts the publish/subscribe model of MQTT
Though MQTT’s publish-subscribe model is identical to any existing
enterprise messaging systems, the main advantage of MQTT has over
fully blown “enterprise messaging” systems are that its low footprint
makes it ideal for developing IoT applications with small sensors,
devices and other low-capacity things. For example, Facebook uses
MQTT for its messenger product on the mobile platform, to ensure that
battery usage of this application is minimized.
Some of the major advantages of MQTT are listed below:
Publish Subscribe model provides one-to-many message delivery
Uses TCP/IP for network connectivity
Can work with SSL/TLS for security
MQTT offers three message delivery QoS: 1) at most once ,2) at
least once and 3) exactly once
These QoS are met even in case of network, publisher or client
failures
Very simple specification and APIs, making it easier for developers
to work with MQTT based products
Most important APIs are CONNECT, PUBLISH, SUBSCRIBE,
UNSUBSCRIBE, and DISCONNECT
As MQTT is specifically designed for constrained device, it
provides only the bare minimum features to support them.
The message header is short in MQTT and smallest packet size in 2
bytes, making it ideal for small and constrained devices
Fig. 2 Publish/Subscribe model of MQTT.
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As MQTT is a publisher/subscribe model, sender and receivers are
decoupled from each other
Doesn’t restrict the format of data to be in any particular format,
thus allowing flexibility
‘Last Will’ feature, which allows abnormal client/sensor
termination to be notified to all interested parties
Both commercial and open sources MQTT based broker products
are available. These include IBM WebSphere MQ v 7.1 onwards,
EclipseIoT Mosquitto, ActiveMQ and HiveMQ.
C. Eclipse Mosquitto
Eclipse Mosquitto is an open source MQTT broker/server. Based on
the lightweight MQTT protocol, Mosquitto is ideal for devices, sensors
and other ‘Internet of Things’ devices, with low processing capacity.
MQTT clients can connect to a given Mosquitto broker and publish/
subscribe the messages from a topic.
Eclipse Mosquitto’s main responsibility is to provide a
communication channel between publishers/senders and subscribers/
receivers. Any publisher, using the Eclipse Paho MQTT Client API can
publish the messages to an MQTT Broker. These MQTT clients should
specify the topic, on which they want to publish the message. These
topics are configured on MQTT broker. Any subscriber or receiver,
that want to receive the message, subscribe to that particular topic. It
is the responsibility of the broker to deliver all the messages arriving
on a topic to all interested clients. As different clients (both publishers
as well as subscribers) need to know only broker/topic details, both
are decoupled from each other. This architecture pattern has many
advantages, e.g. highly scalable solution, where subscribers needn’t to
be overwhelmed by publishers sending messages at a rate faster than
what a subscriber can process.
D. Eclipse Paho
Eclipse Paho is an EclipseIoT project and is implementation
of MQTT protocols. Eclipse Paho provides MQTT client libraries
in multiple languages including Java/C++, C#, .NET and Python.
Eclipse Paho also has utilities for MQTT-SN (sensor networks). Both
publishers and subscribers (as shown in Fig. 2) can use API’s provided
by Eclipse Paho MQTT Client library, and send/receive messages to/
from MQTT broker (e.g. Eclipse Mosquitto).
VI. Why mQTT and OTheR pROpOsed TechnOlOgy
cOmpOnenTs In cOnnecTed caR ImplemenTaTIOn
Suitable for low capacity devices like sensors fitted on connected
cars
Provides Quality of services to handle connectivity and other errors,
which can be quite common in the case of cars and automobiles,
which are on the move, and n/w connectivity can be an issue
Supports wide variety of languages, so compatibility will not be an
issue for any existing technology platform of a car manufacturer
Also integrated with proven and well adopted industry leading
messaging systems like WebSphere MQ and ActiveMQ
Message formats can be customized, allowing manufacturer to
customize and innovate the solutions
Details of MQTT protocol and its specification can be found on the
MQTT site, given in the reference section.
VII. pROpOsed aRchITecTuRe Of ‘pRedIcTIVe caR
maInTenanceusIng eclIpse mOsQuITTO and eclIpse pahO
Fig. 3 shows the simplistic high level architecture context diagram
of a system implementation of ‘predictive car maintenance’ using
Eclipse Mosquitto and Eclipse Paho.
Flow of context diagram (Fig. 3) can be explained as follow:
‘Connected Cars’ send data in predefined format to IoT Gateways
like Eclipse Kura.
Cars can use any possible way to send the data, i.e. via Wi-Fi, Telco
services etc.
IoT Gateway would send this data to MQTT based Eclipse
Mosquitto Broker hosted in a cloud environment
In many scenarios, there can be additional components like
coordinator/controller nodes in the architecture, which do some
kind of pre-processing/aggregation of data collected from various
devices, before sending it to the cloud
Once data is received by Eclipse Mosquitto, subscribers will
receive this message, using Eclipse Paho API
After doing basic validations and any data conversion, subscribers
can send this message to downstream systems, using services
exposed by the Data Integration component of the architecture
It could be that these messages are sent to a messaging system, e.g.
Apache Kafka, where these messages can be consumed by the workers
These workers can push these messages to Apache Hadoop or other
such data processing service, for analytical purpose
These messages can also be consumed by data processing services,
handling real time stream of messages e.g. Apache Storm
These real time stream of messages can be used for real time
analytics purpose e.g. sending alerts in case something needs
immediate attention
‘Workflow’ component can be used to define and execute different
workflows, based on some conditions
For example, to schedule a service appointment, invoke a REST
based service of the car service provider, in case some parts need to
be replaced.
There can be various visualization tools to view reports of
summarized data and perform analytical queries.
Fig. 3 Architecture diagram of ‘Connected car’ ecosystem.
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Note that, in real life complex scenarios, there can be many more
components involved in the architecture (e.g. configuration services,
policy manager, rule engine and so on), but for simplicity’s sake, those
have not been included in this paper.
Fig. 4 summarize the high level data flow of the proposed system.
VIII. sample ImplemenTaTIOn Of pRedIcTIVe caR maInTenance
use case WITh eclIpse mOsQuITTO and eclIpse pahO clIenT uTIlITy
In this section will use Eclipse Paho Client utility to simulate a
connected car, which will send city where the car is (can send exact
location also), speed and current car health check, including if any part
needs to be replaced or not.
In the real world, devices like connected cars might be sending data
first to a IoT gateway, as shown in architecture diagram in Fig. 3 in last
section, where this data will be processed and aggregated, before being
fed to further downstream applications. Depending upon the actual
requirement, applications can be designed to take appropriate actions
based on the data being received e.g. Send alerts if speed is too high
or schedule an appointment with the car service agency, if some part/
lubricants needs to be replaced.
Sample format of the MQTT message, transmitted by sensor fitted
on the car is shown on Fig. 5.
Fig. 5 Sample MQTT message format, sent by connected car
Some of the information, which can be sent by the connected car is
shown in Table 1.
TABLE I.
LIST OF SAMPLE PARAMETERS WHICH ARE MONITORED
Parameters
Location
Mileage per litre
Quantity of fuel consumed
Total Distance Covered
Trip Distance
Distance covered in top gear
Top Speed
Fuel Level
Current temperature
Coolant Level
Engine Oil level
Fuel Level
In this paper, we will be simulating the behavior of connected
car, using Eclipse Paho MQTT Utility, a Java Swing based GUI
application, to connect to a Mosquitto server, publishing message to a
topic on Mosquitto server. The client will receive this message and for
simplicity, will display this message content in the GUI.
Once the utility is launched, screen as shown in Fig. 6 will appear.
Specify the address of the MQTT Mosquitto server and port and
connect to the server. We are using a cloud based test Mosquitto server,
available for public, at the address “test.mosquitto.org”, port 1883.
After specifying the address, click “Connect”. If everything goes
right, you will be connected to server, else you will get error message.
Once connected, enter the name of the topic, and click subscribe.
Now, any message, published on this topic will be displayed in the text
area of the subscriber. Fig. 7 shows a subscriber, connected to a topic
named ‘CarHealth’.
Fig. 4 Sample data ow for connected car.
CarId=xxxxxx;Location=XXXX;ItemName=<<Value>>;currentS
tatus=<<value>>
Fig. 6 Connecting to MQTT server using Paho MQTT utility.
Fig. 7 Subscribing to a MQTT topic.
Fig. 8 Publishing a message to MQTT topic.
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Enter the message payload in the ‘Publish Messages – text display’
area and click publish. Fig. 8 shows the step to publish a message onto
MQTT topic.
Once the message is published, all clients who have subscribed
to this topic, will receive this message. In our scenario, this will be
displayed in the utility GUI. Fig. 9 shows the scenario of a subscriber
receiving the message.
Ix. cOsT benefIT Of pRedIcTIVe caR maInTenance
Predictive car maintenance can help address the issues of traditional
periodic car maintenance approach. Some of the advantages it provides
are
Reduction in service and maintenance costs, as only parts which
needs to be replaced are serviced
Real time alerts of possible part failure, thus avoiding breakdown
and costs associated with outages
Analytics and reporting dashboards can be used to view how the
car is performing over different periods of time and in different
locations
Driver’s driving habits can be analyzed and appropriate action can
be taken
Tour and cab providers can manage their fleet better, thus
maximizing profits
Target advertisements for monetization of data received from
connected cars (e.g. offering service discounts for car which needs
to be serviced etc.)
Table 2 shows the cost comparisons of periodic vs predictive
maintenance for a medium sedan car. Periodic service cost figures are
taken from a leading automobile web site (see reference). As most of
the car vendors provide first two services as free services, zero cost is
taken for these two services. For predictive maintenance, it is assumed
that service cost will go down by 30%.
TABLE II. SERVICE COST COMPARISON OF PERIODIC VS
PREDICTIVE CAR MAINTENANCE
Service
Cost (Periodic
Maintenance) in
INR
Cost
(Predictive Maintenance) in
INR
1st Service 0 0
2nd service 0 0
3rd service 2465 1726
4th service 6455 4519
5th service 3835 2685
6th service 6455 4519
7th service 3835 2685
8th service 6455 4519
Total 29500 20653
Sample calculation for 3rd service is as follow
Cost of 3rd periodic service – 2465 INR [21]
Cost with predictive maintenance with 30% saving = 2465 *((100-
30)/100) = 1726 INR
Fig. 10 represents another view of the service cost comparison data
of Table 2. During the first eight services (scattered across 5 years) of
the car, total costs incurred on periodic maintenance is 29500 INR.
This cost will come down to 20653 INR (assuming 30% reduction in
service cost), with the help of predictive car maintenance.
To understand the outage costs of a fleet/transport organization, let
us take an example of a company with a fleet strength of 100 cars.
If a car of such commercial organization is out of service because of
breakdown or faulty part, there will be various costs associated with it
e.g. pay for an unutilized driver and support staff, rental of the car for
that day, fixing and service cost, need to ensure alternate vehicle for the
customer to ensure company’s commitment, else loses on reputation
part in the consumer market and so on.
Assume that the total outage cost for one vehicle going out of
service is Rs. 5000 per day. So, for a company with one vehicle out of
service, associated costs can be calculated as follows
Outage cost of one vehicle going out of service – 5000 INR (A)
Annual cost of one vehicle going out of service – 5000(A) * 365 =
1825000 INR (B)
Table 3 shows the outage cost per year for multiple number of
vehicles going out of service on a given day. For a company with a
fleet size of 100, number of vehicles going out of service can be much
higher, but for simplicity, table 3 shows costs for maximum three
vehicles going out of service.
TABLE III. OUTAGE/BREAKDOWN
COST FOR A FLEET/TRANSPORT COMPANY
No of Vehicles No. of out of service
vehicles
Outage Cost
per year(in INR)
100 1 1825000
100 2 3650000
100 3 5475000
Fig. 11 represents another view of the outage/breakdown cost data of
Table 3. Assuming that per day cost of a vehicle going out of service is
5000 INR. As shown, on average with only one vehicle out of service,
cost per year is 1.8 million INR and with three vehicles going out of
service, this will increase to 5.4 million INR per annum.
Fig. 10 Service cost comparison of periodic vs predictive car maintenance.
Fig. 9 Subscriber receiving the message
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Table 4 shows the outage cost comparison with 30% improvement
in outage scenarios.
Original Outage cost of 1 vehicle out of service = 1825000 Rs ( B)
Cost after 30% improvement in outage situations = 1825000 *
((100-30)/100) = 1277500 Rs.
TABLE IV BREAKDOWN/OUTAGE COST COMPARISON
No of
Vehicles
No. of out of
service vehicles
Outage Cost per
year(in INR)
Outage cost
@30%
improvement
100 1 1825000 1277500
100 2 3650000 2555000
100 3 5475000 3832500
Fig. 12 represents another view of the outage/breakdown cost
comparison data of Table 4. Even 30% assumed reduction in downtime
will provide considerable savings for the organization. For three
vehicles out of service, cost of breakdown will come down from 5.4
million INR to 3.8 million INR.
Fig. 13 depicts a sample portal/dashboard to view the status of a
given car. Any authorized person can view, whether any part needs
replacement or not, current status of different parts, historical
information, including details of any alert that was sent.
For example, the second row in this dashboard shows that an alert was
raised for a particular part. Though the current value of this part is
within a valid range (between 0-1), but as it is almost on the threshold
to breach the value, a pro-active alert was sent, thus avoiding any
possible outage and associated costs
This dashboard can also be used to trigger additional workflows.
For example, a request to book a service appointment with the car
service provider can be raised using this portal.
x. challenges In The pRedIcTIVe maInTenance Of cOnnecTed caRs
Some of the challenges in successful implementation of predictive
maintenance and connected cars are as follows [8]:
Govt. and Regulatory policies, restricting on what kind of sensitive
data can be transferred
Security concerns related to location and other sensitive data being
shared and transmitted
Lack of industry standards. Right now, most of the work done is
vendor specific/proprietary
Need to have a proper IT Analytics system in place. Can involve
huge costs upfront
Need better connectivity in terms of telecom, Bluetooth, Wi-Fi and
other such networks for transmission of real time data from sensors
Associated business use cases are still evolving, so justifying initial
costs can be difficult
With higher number of sensors needed on the car, cost of buying a
new car can go up
xI. cOnclusIOn
‘Connected car’ concept is getting lots of traction with automobile
companies these days. There are multiple benefits of ‘Connected
Car’ ecosystem, and one such benefit is Predictive Car Maintenance.
This paper talked about what predictive car maintenance is all about,
which problems it could solve. MQTT, a popular protocol for IoT is
also discussed, followed by an introduction to Eclipse Mosquitto and
Eclipse Paho, an implementation of MQTT.
This paper also presented a high level architecture of how
Connected car use can be implemented, using Eclipse Paho and Eclipse
Mosquitto. A simulation of ‘connected car’ sending sensor’s data to
the cloud is also discussed. Finally, cost saving of a predictive car
maintenance system over a traditional periodic car maintenance system
is shown. This paper concluded by sharing of some of the challenges in
implementing predictive maintenance of connected cars.
RefeRences
[1] Kevin A. Kaiser ; Cerner Corp., Kansas City, MO ; Nagi Z. Gebraeel
,Predictive Maintenance Management Using Sensor-Based Degradation
Models, IEEE Transactions on Systems, Man, and Cybernetics - Part A:
Systems and Humans (Volume:39,Issue: 4),pp 840-849 ,2009
[2] D. C. Swanson ; Appl. Res. Lab., Pennsylvania State Univ., University
Park, PA, USA ,A general prognostic tracking algorithm for predictive
maintenance , Aerospace Conference, 2001, IEEE Proceedings.
(Volume:6 ) pp 2971 - 2977 vol.6 ,(2001)
[3] A. Grall ; Lab. de Modelization et Surete des Systemes, Univ. de
Fig. 11 Outage/Breakdown cost for a eet/transport company.
Fig. 12 Breakdown/outage cost comparison.
Fig. 13 Sample dashboard for viewing parts/items health.
International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 4, Nº3
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Technologie de Troyes, France ,L. Dieulle , C. Berenguer and M.
Roussignol ,Continuous-time predictive-maintenance scheduling for a
deteriorating system,IEEE Transactions on Reliability(Volume:51 ,Issue:
2 ),pp 141 - 150 , 2002
[4] Stabler, L,Hawthorne, K(2004) FIX IT BEFORE IT’S BROKE, Railway
Age , Volume: 205, Issue Number: 9 ,pp. 83-84,2004-9
[5] J. Endrenyi , Ontario Power Technol., S. Aboresheid , R. N. Allan
, G. J. Anders , The present status of maintenance strategies and the
impact of maintenance on reliability,IEEE Transactions on Power
Systems(Volume:16 ,Issue: 4),pp 638 - 646 ,2001
[6] Joel Levitt,Complete Guide to Predictive and Preventive Maintenance
,Industrial Press, Inc.; 2 edition (June 15, 2011)
[7] Joseph S. Ng,Automated wireless preventive maintenance monitoring
system for magnetic levitation (MAGLEV) trains and other vehicles
http://www.google.com/patents/US5445347, 1995
[8] Why Do Predictive Maintenance Programs Fail? - http://reliabilityweb.
com/articles/entry/why_do_predictive_maintenance_programs_fail/
[9] What is Internet of things - http://whatis.techtarget.com/definition/
Internet-of-Things
[10] Details about MQTT protocol - http://mqtt.org/
[11] Eclipse IoT - http://iot.eclipse.org/
[12] Eclipse Paho - http://www.eclipse.org/paho/
[13] Eclipse Mosquitto - http://projects.eclipse.org/projects/technology.
mosquitto
[14] Eclipse Paho MQTT Client API – http://www.eclipse.org/paho/files/
javadoc/index.html
[15] IoT, Analytics & Cars – Joe Speed - https://mobilebit.wordpress.com/
[16] Practical MQTT with Paho- http://www.infoq.com/articles/practical-mqtt-
with-paho
[17] IoT and Predictive Maintenance- http://blog.bosch-si.com/categories/
manufacturing/2013/02/iot-and-predictive-maintenance/
[18] The Smart and Connected Vehicle and the Internet of Things - http://tf.nist.
gov/seminars/WSTS/PDFs/1-0_Cisco_FBonomi_ConnectedVehicles.pdf
[19] IBM Predictive Maintenance and Quality for automotive - http://www.
novemba.de/wp-content/uploads/IBM-Predictive-maintenance-and-
Auality-for-automotive.pdf
[20] The advantages of implementing a predictive management within the
maintaining and equipment repair at an enterprise which produces
components for the automotive including technical assistance and services
- http://eccsf.ulbsibiu.ro/repec/blg/journl/5317sima.pdfconnected cars
- use cases for Indian scenario - http://www.hcltech.com/white-papers/
engineering-and-rd-services/connected-cars-use-cases-indian-scenario
[21] Maruti Swift Diesel Estimated Maintenance Cost - https://www.cardekho.
com/maruti-swift/service-cost.htm
Rohit Dhall is working as an Enterprise Architect with
Engineering and R & D Services,HCL Technologies,India.
He has over 19 years of software industry experience.
He helps global clients build technical solutions to solve
their complex business problems. His main area of
expertise is architecting, designing and implementing high
performance, fault tolerant and highly available solutions
for leading Telco and BFSI organizations. He has worked
on diverse technologies like java/J2ee, client-server,P2P ,DWH,SOA, BigData
and IoT etc. He regularly writes articles, blogs and white papers for various IT
forums, portals and events. He is also a coauthor of IBM Redbook and Redpaper
on ‘ITCAM for WebSphere’.
Vijender Kr. Solanki, Ph.D is a research scholar in the
department of Computer Science and Engineering at Anna
University, Chennai. He has completed his graduation
and postgraduation (B.Sc., M.C.A and M.E) from
institution afliated with Maharishi Dayanand University,
Rohtak (MDU) Haryana, India in 2001, 2004 and 2007
respectively. He has attended an orientation program at
UGC-Academic Staff College, University of Kerala and
a refresher course at IIIT-Allahabad. He has participated in more than 15
seminars, summits and conferences at various national & international levels,
including IIT-Delhi, Bharathiar University, Coimbatore and Anna University,
Chennai. He has published more than 10 technical papers with IEEE, Springer
and Elsevier- Science-Direct library. His research interest includes smart city,
network security and network management. He is having 08 Years of rich
academic experience. He has delivered many technical lectures in various
institutions including AICTE Sponsored SDP-FDP Lectures at SKNCOE, Pune,
SNS College, Coimbatore, ITS Ghaziabad and DAVIM, Faridabad. He was an
invitee as key note speaker in DST Sponsored seminar at RCEW, Jaipur. He
has chaired session in many conferences. He is reviewer of some of the IEEE,
Springer and Elsevier Journals and Conferences which are indexed in Scopus,
DBLP, ACM Digital library. He is also a book editor with Universities Press.
... Highlights of Thestudies [23] 2017 Proposed an intelligent system for maintaining vibration and temperature in an electricity power plant [24] 2015 Predicted electric power transformer failure by monitoring dissolved gases in oil [25] 2017 Analyzed industrial data to predict the remaining life of important components of machining equipment [26] 2015 Proposed a cost evaluation model for optimizing maintenance decision variables [27] 2017 Predicted fault diagnosis and remaining useful life and implemented a maintenance schedule based on the proposed system [28] 2017 Surveyed PdM-related trends and techniques and provided suggestions for implementing factory PdM [29] 2016 Reported that intelligent PdM can satisfy customers' needs and change global markets in the manufacturing industry [30] 2017 Discussed vehicular IoT and car PdM with connected technologies [31] 2018 Used data analytics to predict airline maintenance scheduling [32] 2017 Reported the evolution of PdM-related solutions in a Big Data environment PdM-related studies focused on equipment vibration and temperature monitoring [23], systemic failure of electric power transformers [24], remaining useful life prediction for machining equipment [25], optimization of maintenance decision variables [26], fault diagnosis [27], car maintenance [30], airline maintenance scheduling [31], and wind turbine maintenance [32]. Moreover, we examined existing cloud solutions for implementing PdM in a cloud-based ML module. ...
... Highlights of Thestudies [23] 2017 Proposed an intelligent system for maintaining vibration and temperature in an electricity power plant [24] 2015 Predicted electric power transformer failure by monitoring dissolved gases in oil [25] 2017 Analyzed industrial data to predict the remaining life of important components of machining equipment [26] 2015 Proposed a cost evaluation model for optimizing maintenance decision variables [27] 2017 Predicted fault diagnosis and remaining useful life and implemented a maintenance schedule based on the proposed system [28] 2017 Surveyed PdM-related trends and techniques and provided suggestions for implementing factory PdM [29] 2016 Reported that intelligent PdM can satisfy customers' needs and change global markets in the manufacturing industry [30] 2017 Discussed vehicular IoT and car PdM with connected technologies [31] 2018 Used data analytics to predict airline maintenance scheduling [32] 2017 Reported the evolution of PdM-related solutions in a Big Data environment PdM-related studies focused on equipment vibration and temperature monitoring [23], systemic failure of electric power transformers [24], remaining useful life prediction for machining equipment [25], optimization of maintenance decision variables [26], fault diagnosis [27], car maintenance [30], airline maintenance scheduling [31], and wind turbine maintenance [32]. Moreover, we examined existing cloud solutions for implementing PdM in a cloud-based ML module. ...
Article
Full-text available
Industry 4.0 has remarkably transformed many industries. Supervisory control and data acquisition (SCADA) architecture is important to enable an intelligent and connected manufacturing factory. SCADA is extensively used in many Internet of Things (IoT) applications, including data analytics and data visualization. Product quality management is important across most manufacturing industries. In this study, we extensively used SCADA to develop a cloud-based analytics module for production quality predictive maintenance (PdM) in Industry 4.0, thus targeting textile manufacturing processes. The proposed module incorporates a complete knowledge discovery in database process. Machine learning algorithms were employed to analyze preprocessed data and provide predictive suggestions for production quality management. Equipment data were analyzed using the proposed system with an average mean-squared error of ~0.0005. The trained module was implemented as an application programming interface for use in IoT applications and third-party systems. This study provides a basis for improving production quality by predicting optimized equipment settings in manufacturing processes in the textile industry.
... Instead, only a minimal selection of parameters (such as time since the last inspection or kilometers driven for vehicles) is considered. Predictive maintenance has emerged as a promising approach to ensure availability while reducing the number of unnecessary reviews [23]. Predictive maintenance considers the individual wear of parts based on a variety of different sensor data. ...
Conference Paper
Full-text available
This paper describes an artificial intelligence (AI) based failure management approach across the value chain for the commercial vehicle industry by integrating and utilizing lifecycle data for product and production optimization. The amount of available data throughout a product lifecycle has increased significantly in previous years, primarily driven by the development and deployment of cyber-physical systems. While data from a single entity in the value chain already enables failure management-related analysis and services, including AI-based methods such as predictive maintenance, there remains a lack of systematic approaches to utilize data across the entire value chain. This paper proposes an AI-based failure management approach, which relies on integrating a variety of diverse data sources along the value chain. At first, three so-called application areas were defined: process and product optimization, availability optimization, and performance optimization. Consequently, practice-relevant use cases are identified for each area, for which it is shown how failures in the value chain can be proactively eliminated with the support of AI. Methods for predictive analytics are adapted for cross-value chain failure management to derive correlations between different stages of the production process and product usage. Based on these results and human expert knowledge, proactive measures are recommended by a decision support system (DSS) to resolve failures before arising. The commercial vehicle industry serves as an overarching validation case study for the practice-relevant verification of the targeted applications. The paper gives an outlook on the envisaged research work for the realization of holistic failure management.
... In some applications, ensuring a reliable packet reception by a specific node is a key factor. For example, periodic car maintenance can be explained as a service/maintenance model, where a car undergoes a service/maintenance either after a certain specified time period or on the basis of a part getting faulty [16]. Car parts (brake, motor, etc. . . ) are equipped with a sensor for monitoring and data collecting purposes. ...
... In some applications, ensuring a reliable packet reception by a specific node is a key factor. For example, periodic car maintenance can be explained as a service/maintenance model, where a car undergoes a service/maintenance either after a certain specified time period or on the basis of a part getting faulty [16]. Car parts (brake, motor, etc. . . ) are equipped with a sensor for monitoring and data collecting purposes. ...
Chapter
Full-text available
The routing protocol plays a key role in allowing packets to reach their intended destination. We are interested in wireless nanonetworks (WNNs), which totally differ from traditional wireless networks in terms of node density and size, routing protocol used, and hardware limitations. This paper presents an enhanced retransmission algorithm used by the nodes in the destination zone, in combination with our previously proposed nanosleeping mechanism. This algorithm increases the chance of a destination node to capture the intended packet, while decreasing the number of participating nodes in the retransmission process. We evaluate the enhanced retransmission algorithm and show its effectiveness in reducing node resource usage while maintaining a high packet delivery to the destination node.
... Machine Learning is the realization of artificial intelligence by learning only the data obtained without the programming of computers.The machine learning and artificial intelligence, which are increasing in importance in recent years, are being implemented in many areas.With the emergence of new concepts such as smart systems, distributed systems, big data analysis, their working and application areas are increasing. Smart cities [1], IoT [2] and mixed of them [3,4], big data [5] can be pointed from these application fields.Therefore, the importance of machine learning is increasing day by day and the workings in this area continue with the latest speed.So machine-learning methods are described in this section.Machine learning algorithms are generally classified into three groups [6]. ...
Article
Full-text available
a omerkucuk2000@hotmail.com, https://orcid.org/0000-0003-3180-5250 Özet: Günümüzde işletmelerin en büyük gider kalemlerinden biri hammadde ve stok miktarlarıdır. Bu nedenle doğru stok yönetimi işletmelerin karlılığı için çok önemlidir. Zamanında satın alınmayan ürünler üretimde aksamalara neden olur ve son kullanma tarihi geçtiği için ürün artığı da işletmeler için kayıplara neden olur. Bu nedenle, işletmelerin kâr/zarar durumları için doğru stok yönetimi kritik öneme sahiptir. Bu yazıda, bir regresyon modeli kullanarak belirli stok kalemlerinin talebini tahmin etmek için bir model sunduk. Modelimiz, verilen veri kümesindeki tahmin sonuçlarını analiz edebilir ve bilgisayarlayabilir. Modelimizi örnek bir veri seti üzerinde değerlendiriyor ve mevcut envanter üzerinden analizlerin yanı sıra hesaplamaları da sağlıyoruz. Stok tüketiminin doğru analizi, gelecekte tüketilecek stok miktarının doğru tahmin edilmesini sağlar. Stok tüketiminin doğru tahmin edilmesi, karar vermede düzeltici adımların atılmasına yardımcı olur. Yani sadece gerektiğinde yeterli miktarda alım yapmanızı sağlar. Bu aşamalar ekonomik stok yönetimi için kritik öneme sahiptir. Bu nedenle model sağlayabilecek sağlam ve uyarlanabilir yaklaşımlar, stok tüketiminin doğru yönetilebilmesini sağlar. Hisse senedi hareketlerinin yönünü tahmin etmek için daha önce yazılmış kaynakları bulmak zordur. Bunun en önemli nedenlerinden biri akademik literatürde bu tür çalışmaların yapılmasına yönelik teşviklerin olmamasıdır. Sonuç olarak konuyla ilgili yazılan yazılar ve yapılan çalışmalar sınırlı kalmış, sonuçlar tekrarlanabilir düzeye ulaşmamıştır. Abstract: Today one of the biggest expense items of the enterprises is raw material and stock amounts. Therefore, proper inventory management is very important for the profitability of enterprises. Products that are not purchased on time cause interruptions in production and products leftover because the expiration date has passed will also cause losses for businesses. Therefore, proper inventory management is critical for the profit/loss situations of businesses. In this paper, we presented a model to predict the demand of certain stock items by using a regression model. Our model can analyze and computer the prediction results on the given dataset. We evaluate our model on a sample dataset and provide the analysis as well calculations over the existing inventory. Accurate analysis of stock consumption enables accurate estimation of the amount of stock to be consumed in the future. Accurate forecasting of stock consumption helps to take corrective steps in decision-making. That is, it only allows you to buy in sufficient quantity when necessary. These stages are critical for economic stock management. For this reason, robust and adaptable approaches that can provide models ensure that stock consumption can be managed properly. It is difficult to find previously written sources on estimating the direction of stock movements. One of the most important reasons for this is the lack of incentive to make such studies in the academic literature. As a result, articles written about the subject and the work done have been limited, the results have not reached the reproducible level.
... In this research work [9], an intelligent microclimate control system based on IoT was designed to solve the problem of climatic conditions in an environment, air filtering, and high energy consumption. Air conditioning is beyond a mere lowering of the temperature of the air, it also involves filtering of the air, dehumidifying and also circulating the air all over the environment for a healthy life [10]. Home monitoring and automation are important in this present world and steps to have been taken to make it feasible and a reality. ...
Article
Full-text available
The traditional chemistry laboratory in most tertiaryinstitutions has failed to look into the present chemical substance,its temperature, and reactions to certain optimal scales. Thetemperature hazards of chemicals in many chemistry or industrialsectors has gone out of measure. To this effect, most chemicalsubstances in the laboratory due to fluctuation in temperaturesometimes causes laboratory hazards. The existing system hasissues ranging from reporting style, the temperature of thechemical substance either in use or not in use, and error based onhuman system. Chemical substances in the laboratories haveimposed tremendous hazards on lives and properties. In this paper,we built an Message Queuing Telemetry Transport (MQTT)broker to monitor, report, and control the influence of temperatureof a chemical substance in the laboratory to avoid hazards andensure safety. The platform employed both hardware devices thatcan read the change in dynamic temperature and softwareprogram that presents the temperature to both the mobile platformand desktop system. MCUESP8266 node, temperature sensorDHT11/ 22 and Python 3 flask framework were employed. Thesystem is a cloud-based system displays the temperature of achemical substance in a real-time manner. Temperaturelaboratory hazards are avoided, controlled, and reduced to itsbarest minimum with this system. The laboratory system of testingthe rate of reaction of chemical, relative to the temperature range,or the self-reacting chemical substances at particular roomtemperature is the main focus of this paper/ article. The systemauto-detects the regular change in temperature reading of achemical substance in the laboratory.
... Further, they used MQTT-SN to improve the average packet delivery rate by approximately 30%, confirming an energy efficient approach. In Ref. [146], Dhall et al. proposed a connected car maintenance approach based on the IoT. The authors used MQTT for efficient data exchanges between cars to help owners to schedule service, analyze traffic, and receive vehicle crash data. ...
Article
The Internet of Things (IoT) has been growing over the past few years due to its flexibility and ease of use in real-time applications. The IoT's foremost task is ensuring that there is proper communication between different types of applications and devices, and that the application layer protocols fulfill this necessity. However, as the number of applications grows, it is necessary to modify or enhance the application layer protocols according to specific IoT applications, allowing specific issues to be addressed, such as dynamic adaption to network conditions and interoperability. Recently, several IoT application layer protocols have been enhanced and modified according to application requirements. However, no existing survey articles have focused on these protocols. In this article, we survey traditional and recent advances in IoT application layer protocols, as well as relevant real-time applications and their adapted application layer protocols for improving performance. As changing the nature of protocols for each application is unrealistic, machine learning offers means of making protocols intelligent and able to adapt dynamically. In this context, we focus on providing open challenges to drive IoT application layer protocols in such a direction.
Chapter
Autonomous Vehicles have a bigger role in the future. In a real-life application, an autonomous vehicle can be used by a passenger to reach its destination autonomously with necessary maneuverings. The autonomous vehicle includes multiple computing devices which can be easily operated by users using input buttons. Thus, communicating requests from human to machine and initiate a trip to the destination would be much convenient, secure, and easy. The human-to-machine instructions provide multiple facilities to the users including emergency stop, start, speed control, emergency message convener, and timed operations. However, the user inputs and control to steering, acceleration, and de-accelerations are very limited. In this chapter, the importance of Blockchain technology for autonomous vehicles is explored. This study analyses the data received from system perception, planning, movement, and decision-making for autonomous vehicles. Blockchain technology provides immutability, transparency and security measurements that can guarantee performance and safety under all driving circumstances. For example, data collection and storage in Blockchain network can provide a planned method that can further ensure safe and system-compliant performance in complex and dynamic environments. This work emphasizes the recent Blockchain-based approaches for integrating perception and planning for end-to-end communication, verification, and safety processes and managing fleets of autonomous vehicles. Further, Blockchain integrated autonomous vehicle’s movement-based use cases are presented for multiple applications. These real-time applications realize the importance of autonomous vehicles in-vehicle networks. These use cases present the requirements of advancements in many aspects of vehicle autonomy. For example, vehicle’s design to control the operations, plan the movement, coordinate with other vehicles, human to machine instructions and interactions, human perceptions in real-time implementation, and vehicle state driving. Finally, future research directions and open challenges are highlighted for future readers.
Chapter
The world is growing and energy conservation is a very important challenge for the engineering domain. The emergence of smart cities is one possible solution for the same, as it claims that energy and resources are saved in the smart city infrastructure. This chapter is divided into five sections. Section 1 gives the past, present, and future of the living style. It gives the representation from rural, urban, to smart city. Section 2 gives the explanations of four pillars of big data, and through grid, a big data analysis is presented in the chapter. Section 3 started with the case study on smart grid. It comprises traffic congestion and their prospective solution through big data analytics. Section 4 starts from the mobile crowd sensing. It discusses a good elaboration on crowd sensing whereas Section 5 discusses the smart city approach. Important issues like lighting, parking, and traffic were taken into consideration.
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In this paper, the most frequently used power system maintenance strategies are reviewed. Distinction is made between strategies where maintenance consists of replacement by a new (or “good as new”) component and where it is represented by a less costly activity resulting in a limited improvement of the component's condition. Methods are also divided into categories where maintenance is performed at fixed intervals and where it is carried out as needed. A further distinction is made between heuristic methods and those based on mathematical models; the models themselves can be deterministic or probabilistic. From a review of present maintenance policies in electric utilities, it is concluded that maintenance at fixed intervals is the most frequently used approach, often augmented by additional corrections. Newer “as needed”-type methods, such as reliability-centered maintenance (RCM), are increasingly considered for application in North America, but methods based on mathematical models are hardly ever used or even considered. Yet only mathematical approaches where component deterioration and condition improvement by maintenance are quantitatively linked can determine the effect of maintenance on reliability. Although more complex, probabilistic models have advantages over deterministic ones: they are capable of describing actual processes more realistically, and also facilitate optimization for maximal reliability or minimal costs
Conference Paper
Prognostic health management (PHIM) is a technology that uses objective measurements of condition and failure hazard to adaptively optimize a combination of availability, reliability, and total cost of ownership of a particular asset. Prognostic utility for the signature features are determined by transitional failure experiments. Such experiments provide evidence for the failure alert threshold and of the likely advance warning one can expect by tracking the feature(s) continuously. Kalman filters are used to track changes in features like vibration levels, mode frequencies, or other waveform signature features. This information is then functionally associated with load conditions using fuzzy logic and expert human knowledge of the physics and the underlying mechanical systems. Herein is the greatest challenge to engineering. However, it is straightforward to track the progress of relevant features over time using techniques such as Kalman filtering. Using the predicted states, one can then estimate the future failure hazard, probability of survival, and remaining useful life in an automated and objective methodology
Article
The most frequently used maintenance strategies are reviewed. Distinction is made between strategies where maintenance consists of replacement by a new (or "good as new") component and where it is represented by a less costly activity resulting in a limited improvement of the component's condition. Methods are also divided into categories where maintenance is performed at fixed intervals and where it is carried out as needed. A further distinction is made between heuristic methods and those based on mathematical models; the models themselves can be deterministic or probabilistic. From a review of present maintenance policies in electric utilities it is concluded that maintenance at fixed intervals is the most frequently used approach, often augmented by additional corrections. Newer "as needed"-type methods, such as reliability-centered maintenance (RCM), are increasingly considered for application in North America, but methods based on mathematical models are hardly ever used or even considered. Yet only mathematical approaches where component deterioration and condition improvement by maintenance are quantitatively linked can determine the effect of maintenance on reliability. Although more complex, probabilistic models have advantages over deterministic ones, they are capable of describing actual processes more realistically, and also facilitate optimization for maximal reliability or minimal costs.
Article
A predictive-maintenance structure for a gradually deteriorating single-unit system (continuous time/continuous state) is presented in this paper. The proposed decision model enables optimal inspection and replacement decision in order to balance the cost engaged by failure and unavailability on an infinite horizon. Two maintenance decision variables are considered: the preventive replacement threshold and the inspection schedule based on the system state. In order to assess the performance of the proposed maintenance structure, a mathematical model for the maintained system cost is developed using regenerative and semi-regenerative processes theory. Numerical experiments show that the s-expected maintenance cost rate on an infinite horizon can be minimized by a joint optimization of the replacement threshold and the a periodic inspection times. The proposed maintenance structure performs better than classical preventive maintenance policies which can be treated as particular cases. Using the proposed maintenance structure, a well-adapted strategy can automatically be selected for the maintenance decision-maker depending on the characteristics of the wear process and on the different unit costs. Even limit cases can be reached: for example, in the case of expensive inspection and costly preventive replacement, the optimal policy becomes close to a systematic periodic replacement policy. Most of the classical maintenance strategies (periodic inspection/replacement policy, systematic periodic replacement, corrective policy) can be emulated by adopting some specific inspection scheduling rules and replacement thresholds. In a more general way, the proposed maintenance structure shows its adaptability to different possible characteristics of the maintained single-unit system
Automated wireless preventive maintenance monitoring system for magnetic levitation (MAGLEV) trains and other vehicles
  • Joseph S Ng
Joseph S. Ng,Automated wireless preventive maintenance monitoring system for magnetic levitation (MAGLEV) trains and other vehicles http://www.google.com/patents/US5445347, 1995
Complete Guide to Predictive and Preventive Maintenance
  • Joel Levitt
Joel Levitt,Complete Guide to Predictive and Preventive Maintenance ,Industrial Press, Inc.; 2 edition (June 15, 2011)
Analytics & Cars – Joe Speed -https://mobilebit.wordpress.com/ [16] Practical MQTT with Paho-http
  • Iot
IoT, Analytics & Cars – Joe Speed -https://mobilebit.wordpress.com/ [16] Practical MQTT with Paho-http://www.infoq.com/articles/practical-mqtt- with-paho