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M2M Telematics & Predictive Analytics

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M2M Telematics & Predictive Analytics

Abstract and Figures

Take an example of electric vehicle (EV) that you drive home and connect to the electric grid, letting it figure out your next commute requirement from the calendar appointments on your hand-held or mobile, and your grid’s active load usage and non-peak hours from the energy provider, all summed up to auto-tune its own recharging schedule to reduce the load on grid while taking into account any emergency commutes that might be required based on your personal commute history and your associations – computed all without the need for slightest manual intervention, that’s M2M Telematics hand-in-hand with real-time analytics at work. This paper discusses the concepts M2M Telematics in relation with Predictive Analytics and presents a Predictive Maintenance case study.
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2013
Gopalakrishna Palem
2/24/2013
M2M Telematics &
Predictive Analytics
CONTENTS
Executive Summary ....................................................................................................................................................... 2
M2M Drivers .......................................................................................................................................................... 4
Embedded Telematics Trends ............................................................................................................................... 4
Mobile Device Telematics Trends .......................................................................................................................... 5
Predictive Maintenance ................................................................................................................................................. 7
Solution Enablers ....................................................................................................................................................... 9
The Methodology .................................................................................................................................................... 11
Best Practices Guidelines ......................................................................................................................................... 15
Concluding Remarks .................................................................................................................................................... 16
References ................................................................................................................................................................... 16
About Author ............................................................................................................................................................... 17
EXECUTIVE SUMMARY
Number of machine-to-machine (M2M) device connections world-wide is expected to
grow from 130 million in 2012 to 2.14 billion by 2021, while the total world-wide M2M
management market is expected to grow from USD459 million in 2012 to USD1.1 billion
in 2016 at 23% CAGR (Figure 1).
FIG URE 1 M2M MAN AGE MEN T M ARK ET FOR ECA ST (SOUR CE : ANA LY SYS MA SON , 20 12 )
CSPs are actively exploring opportunities for providing M2M solutions to industry
sectors such as automotive and transport, energy and utilities, security and surveillance,
public safety, financial services, retail, healthcare, and warehousing and distribution.
Automotive:
o Vehicle & Asset Tracking: Real-time GPS/GSM evaluation and tracking
o Fleet management: Real-time predictive maintenance, inventory
control, optimal work-schedules
o Eco-routing: Real-time traffic monitoring systems for vehicle efficiency
and passenger safety with cooperative M2M interactions
Healthcare:
o Telemedicine: Health monitoring and remote medicine administration
o Assisted Living: Supportive sensor aids and safety guides
Retail:
o Match sales: Real-time auctioning for buyer’s basket
o Supply chain monitoring: Real-time environment conditions of goods,
location tracking
o Connected cabinets: Real-time stock level display, revenue reports,
targeted advertising
Finance:
o Usage-based Insurance Services
Energy & Utilities:
397
459
574
724
895
1118
0
200
400
600
800
1000
1200
2011 2012 2013 2014 2015 2016
Revenue (USD Million)
o Smart Grid: Real-time network optimization with demand based
generation, load-based distribution and smart metering capabilities
o Monitoring & Control: Remote monitoring operational metrics, such as
pipeline pressure, temperature etc.
As the global energy usage continues to surge, the confluence of M2M telematics and
real-time analytics is the key for delivering green solutions.
Take an example of electric vehicle (EV) that you drive home and connect to the electric
grid, letting it figure out your next commute requirement from the calendar
appointments on your hand-held or mobile, and your grid’s active load usage and non-
peak hours from the energy provider, all summed up to auto-tune its own recharging
schedule to reduce the load on grid while taking into account any emergency commutes
that might be required based on your personal commute history and your associations
computed all without the need for slightest manual intervention, that’s M2M Telematics
hand-in-hand with real-time analytics at work.
Intelligent devices, such as the EV presented above, are the promise of M2M. On a
broader note, the machine-to-machine (M2M) communication operations include both,
intelligent control of remote machine parts (telematics) and remote measurement of
various sensor readings (telemetry). A typical definition of M2M in mobile space is,
machines using network resources to communicate with remote application
infrastructure for monitoring and control, either of the machine itself or its surrounding
environment.
The possibility of having billions of devices being uniquely addressable with IPV6
(Internet Protocol version 6), the advent of seamless wireless communications with
improved standardization and broadband signal capabilities, and the onslaught of real-
time analytics on commodity hardware with large data sets being processed in parallel
all are contributing to the success of M2M telematics systems with real-time predictive
analytic capabilities. A typical architecture of such a system reads as shown in Figure 2.
FIG URE 2 M2M TE LEM ATI CS WI TH PRED ICT IVE ANA LYT ICS SYS TE M
Asset with Internal
Sensors
External
Sensors
Monitor & Control Center
Analytics
Processing
Stream Processing
M2M DR I V ER S
Key drivers for M2M initiatives are as below:
Telematics and telemetry are increasingly being perceived as sources of greater
operational efficiency and cost reduction.
o Automated monitoring of heating, ventilation and cooling are few
driving factors for smart buildings
o Street lights that operate based on traffic flow, alternate automatic
routing based on peak-hours etc. are driving factors for smart cities
o Predictive maintenance through improved system monitoring, remote
monitoring of farms and mining operations etc. are cost saving factors
Regulatory requirements for safety and energy efficiency are pushing the utility
sector and automotive industries to use innovative ways of M2M methods
o Emergency calling, accident alerts, cooperative driving etc. are
compelling factors for automotive industry
o Smart meters and energy demand response are driving factors for
Utilities industry
Standardization and adaptation of IPV6 across industry has opened the
possibility for billions of uniquely addressable IP devices
Wide rollout of 3G and LTE networks are providing devices with always on
connectivity and increased bandwidth enabling M2M segment applications
such as remote surveillance, asset tracker, health meters etc.
Smart application development is creating new opportunities for application
developers to build applications for every possible segment, right from smart
homes to smart amenities
Innovations in the retail and consumer electronics segment are pushing the
boundaries of connectivity, with applications such as wireless payments to
connected satellite navigation systems
The electronics and communications industry is rapidly moving towards intelligent,
addressable, embeddable devices enabling seamless communications between every
device. Following sections describe the embedded and mobile based telematics trends
and the potential revenue opportunities, followed by a sector-focus case study.
EMBEDDED TELEMATI C S TR E N D S
Key Information
Remarks
2.5G GPRS
- Dominant embedded
technology in Europe
- New systems should be
using 3G
3G CDMA
- CDMA2000 is dominant in
USA
- Europe is moving to WCDMA
- Also used in Japan, Korea
and China
- eCall may use either GPRS
or WCDMA
3.5G HSPA
- New systems are evolving
rapidly on HSPA
- Available in Europe for
Luxury cars
4G LTE
- Industry wide deployments
are expected to be available
by 2015
- May be year earlier for
Luxury brands
In summary, embedded telematics are likely to continue using 3G and 3.5G technologies
until 2015 in the developed countries, and even longer in other areas. LTE is expected to
become mainstream 4G technology for mobile phone industry and auto-industry.
WiMax missed its opportunity to become mainstream technology, but will become a
niche player in most regions.
MOBILE DEVICE TELEMATICS TR E N D S
Mobile device telematics leverage operator’s mobile phone to establish communication
between instrument and the monitoring station. Predominantly used in vehicles and
automobiles, driven by the need for hands-free interface (HFI). The trends are:
Key Information
Remarks
Mobile
Phone HFI
- Available for long time by OEMs
- Initially wired, then
Bluetooth
Telematics
Service
- Evolving as a popular telematics
approach
- Primarily for infotainment
services
- Popularized by Ford
SYNC success
- Lower reliability than
embedded link
Telematics
Service &
Smartphone
Apps
- Leverage wealth of auto-related
smart Apps
- Smart phone to H-U apps
integration emerging
- Likely to become
necessity by 2015
timeframe
- Need for technology
solutions to lower
driver distraction
Some of the prominent market implementations for automobile telematics in North
America segment are:
OEM
Service
Features
Launch
Technology
GM
OnStar/MyLink/Inte
lliLink/CUE
eCall/bCall, SVR, Vehicle
Slow Down, Google, Mobile
App
1996
Embedded/
Mobile
Device
BMW
ConnectDrive /
BMW Assist
eCall/bCall, Concierge,
Lock/Unlock, Diagnostics,
Google Services, Tracking,
Full Browsing, Mobile Apps,
Traffic
2001
Embedded/
Mobile
Device
Ford
Sync/ MyFord
Touch / MyFord
Mobile
eCall/bCall, Navigation,
Weather, Traffic, Mobile
Apps, Live Operator
2007
Embedded/
Mobile
Device
Toyota
Safety Connect /
Entune
SOS/ACN, Roadside
Assistance, Stolen Vehicle
Location, Entune
2009
Embedded/
Mobile
Device
Audi
Audi Connect
Navigation, Weather, Gas
prices, Travel Info, News,
Wi-Fi Hotspot
2011
Embedded
Mercedes
mBrace
eCall/bCall, SVR, Crisis Assist,
Google Send to Car, Traffic,
Weather, Concierge,
Lock/Unlock, Car Finder,
Diagnostics, Mobile App
2011
Embedded
Nissan
Nissan CarWings
Charge Levels, Charging
Stations, Comparison of
Drivers, Google Send to Car
2011
Embedded
Hyundai
Blue Link
Navi, eCall/bCall, Weather,
2011
Embedded
Traffic, POI Voice Rec, Geo
Fence, SVR, Diagnostics,
Mobile App
Despite the present and emerging mobile connectivity trend, embedded wireless
modules are expected to grow significantly, mainly due to the stringent quality
requirements and reliability standards that hand-held devices fail to meet adequately.
Tight automotive requirements are also expected to be in place to sustain the growth of
safety and security critical equipment that will be mandated by government regulations,
such as eCall in Europe. The European eCall regulations will in fact force all new vehicles
by 2015 to adapt a connectivity link for emergency call.
The reality is that regulations such as eCall will give raise to further significant demand
for telematics, enabling several additional services to be layered on top of basic
emergency call. Some of the potential applications on those lines are:
Remote Diagnostics
Remote Vehicle Control
Vehicle Software Upgrade
Electronic Toll Collection
Eco-Driving
Off-Board Navigation
Smart Traffic Control
Usage-based Insurance Services
Condition-based Maintenance etc.
It is the combination of Telematics with Predictive Analytics on Real-time Big Data that
makes many of these innovations, such as smart traffic control, usage-based insurance,
condition-based maintenance etc… possible, and smart players are already reaping
enormous benefits by employing these in their strategic offerings.
The following sections present an in-depth review on a sector-focus case-study, namely
Condition-based maintenance, describing how predictive analytics and telematics are
working together to reduce maintenance costs.
PREDICTIVE MAINTENANCE
aintenance, considered as a non-value add function, is ever more requested
to contribute higher and higher for costs reduction, keeping the machines in
excellent working condition, while satisfying the stringent safety and
operational requirements. Manufacturers and operators employ array of maintenance
strategies to address that, all of which and can be broadly categorized as below:
Corrective Maintenance
Preventive Maintenance
Predictive Maintenance
Corrective maintenance is the classic Run-to-Failure reactive maintenance that has no
special maintenance plan in place. The machine is assumed to be fit unless proven
otherwise.
Cons:
o High risk of collateral damage and secondary failure
o High production downtime
o Overtime labor and high cost of spare parts
Pros:
o Machines are not over-maintained
o No overhead of condition monitoring or planning costs
Preventive maintenance (PM) is the popular periodic maintenance strategy that is
actively employed by all manufacturers and operators in the industry today. An optimal
breakdown window is pre-calculated (at the time of component design or installation,
based on a wide range of models describing the degradation process of equipment, cost
structure and admissible maintenance etc.), and a preventive maintenance schedule is
laid out. Maintenance is carried-out on those periodic intervals, assuming that the
machine is going to break otherwise.
Cons:
o Calendar-based maintenance: Machines are repaired when there are
no faults
o There will still be unscheduled breakdowns
Pros:
o Fewer catastrophic failures and lesser collateral damage
o Greater control over spare-parts and inventory
o Maintenance is performed in controlled manner, with a rough
estimate of costs well-known ahead of time
Predictive Maintenance (PdM) is an emerging alternative to the above two that employs
predictive analytics over real-time data collected (streamed) from parts of the machine
to a centralized processor that detects variations in the functional parameters and
detects anomalies that can potentially lead to breakdowns. The real-time nature of the
analytics helps identify the functional breakdowns long before they happen but soon
after their potential cause arises.
M
“… In many cases,
scheduled overhaul
increases the overall
failure rate by
introducing a high
infant mortality rate
into an otherwise
stable system
- RCM Guide, NASA
Pros:
o Unexpected breakdown is reduced or even completely eliminated
o Parts are ordered when needed and maintenance performed when
convenient
o Equipment life is maximized
Cons:
o High investment costs
o Additional skills might be required
Predictive maintenance, also known as Condition Based Maintenance (CBM) differs
from preventive maintenance by basing maintenance need on the actual condition of
the machine rather than on some preset schedule.
For example, a typical preventive maintenance strategy demands automobile operators
to change the engine oil after every 3,000 to 5,000 Miles traveled. No concern is given
to the actual condition of vehicle or performance capability of the oil.
If on the other hand, the operator has some way of knowing or somehow measuring the
actual condition of the vehicle and the oil lubrication properties, he/she gains the
potential to extend the vehicle usage and postpone oil change until the vehicle has
traveled 10,000 Miles, or perhaps pre-pone the oil change in case of any abnormality.
Predictive analytics with M2M telematics provides such deep insights into the machine
operations and full functionality status giving rise to optimal maintenance schedules
with improved machine availability.
Underlying preventive maintenance is the popular belief that machine failures are
directly related to machine operating age, which studies indicate not to be true always.
Failures are not always linear in nature. Studies indicate that 89% of the problems are
random with no direct relation to the age. Table 1 showcases some of these well-known
failure patterns and their conditional probability (Y-axis) with respect to Time (X-axis).
TAB LE 1 FAIL URE CON DIT ION AL PRO BAB ILIT Y CUR VES (SO UR C E : JO HN MOUB RA Y, NO WL AN & HEA P)
Age-Related = 11%
Random = 89%
Wear out
Type A = 2%
Infant Mortality
Type B = 68%
Bathtub
Type E = 4%
Initial Break-in Period
Type C = 7%
Fatigue
Type F = 5%
Relatively Constant
Type D = 14%
“… Preventive
maintenance is how
fleets attempt to
avoid breakdowns
today. But new
thinking is moving
towards predictive
repairs, transforming
time-or mileage-
based model with one
that is based on
evidence of need…”
Complex items frequently demonstrate some infant mortality, after which their failure
probability either increases gradually or remains constant, and a marked wear-out age is
not common. Considering this fact, the chance of a preventive maintenance avoiding a
potential failure is low, as there is every possibility that the system can fail right after a
scheduled maintenance. Thus, preventive maintenance imposes additional costs of
repair. Predictive Maintenance reduces such additional costs by scheduling
maintenance if and only when a potential breakdown symptom is identified.
FIG URE 3 PRE DIC TIV E M AINT EN AN CE SC HED ULE S A RE SMA RTE R A ND CON VEN IEN T
However, the costs of monitoring equipment and monitoring operations should not
exceed the original asset replacement costs; lest the whole point of Predictive
Maintenance becomes moot. Studies have estimated that a properly functioning CBM
program can provide savings of 8% to 12% over the traditional maintenance schemes.
Independent surveys indicate the following industrial average savings resultant from
initiation of a functional predictive maintenance program:
Reduction in maintenance costs: 25% to 30%
Elimination of breakdowns: 70% to 75%
Reduction in equipment or process downtime: 35% to 45%
Increase in production: 20% to 25%
Apart from the above, improved worker and environment safety, increased component
availability, better product quality etc. are making more and more manufacturers and
operators embrace CBM based management solutions.
SOLUTION ENABLER S
Predictive Maintenance or Condition-Based-Maintenance Management (CBMM) solution
is enabled by three major technology enhancements over a traditional maintenance
schedule:
1. Remote Sensor Monitoring & Data Capturing
2. Real-time Stream Processing of Sensor Data
3. Predictive Analytics
Preventive Maintenance
Predictive Maintenance
Predictive analytics
help reduce over-
maintenance,
decrease operational
costs and maximize
equipment
availability
CBMM solutions essentially operate by having sensors attached to remote assets
(mobile or stationary) that send continuous streams of data about the assets’
operational conditions to a monitoring station that then analyzes them in real-time
using predictive analytic models and detects any problems in the behavior or state of
the asset. Once a problem is detected, appropriate pre-configured action is taken to
notify the operator or manufacture for corrective action. The monitoring station in
question can be on the same network as that of the sensors or it could be in a remote
location far away from them, connected through wide area networks or satellite
networks.
Nature of the sensors being monitored, frequency of the data getting collected and
precision of the analytic models being used all affect the quality of the prediction
results. Thus, it is imperative that manufacturers and operators define all these
parameters with utmost care while deploying a condition-based-maintenance
management system. This, however, entails a thorough understanding of the system
under operation and expects a clear-cut answer as to what is being monitored and what
is expected out of such monitoring. Some of the questions that can help manufacturers
and operators along those lines are:
For monitoring:
Which parts of the system or asset are expected to be monitored?
What type of data is expected to be collected and which type of sensors give
such data? For example, visual data, thermal data etc.
What is the expected frequency for the data collection?
How should any failures in the sensors be handled?
For real-time stream collection and processing at the monitoring station:
What is the acceptable data processing latency?
How to deal with imperfections in the received data? For example, a faulty
sensor sending incorrect data
What should be done with the collected data after processing?
For Analytics sub-system:
Which analysis technique accurately models the asset/system behavior?
What is the definition of acceptable behavior and anomaly?
What should be the response in case of any anomaly detection?
What should be the reasonable timeframe between anomaly detection and
corrective action?
How to deal with situations where there are multiple anomalies detected at the
same time?
Generic and security related questions:
Who should be allowed to access the collected data and analysis results?
“…Predictive
technology does not
avoid failures rather
what it avoids is the
high-costs associated
with the failures, by
providing early
warnings of the
failures so that
operators can decide
when and where to
address them before
they actually happen”
What is the change management process required in case one wants to tune
the tracking and analysis parameters?
The following section briefly summarizes some of the industry standard methods used
in CBMM systems and can help in answering the above questions.
THE METHODOLOG Y
The primary component in a condition-based-monitoring management solution is a
sensor array and the measurements it provide. Some of the widely used measurement
techniques in the industry are:
Temperature Measurement: Thermal indicators, such as temperature-sensitive
paint, thermography etc., help detect potential failures arising out of
temperature changes in the equipment. Excessive mechanical friction,
degraded heat transfer, poor electrical connections are some of the problems
that can be detected with this type of measurement.
Method
Description
Applications
Point Temperature
A thermocouple or RTD
Can be used on all accessible
surfaces
Area Pyrometer
IR radiation measured from a
surface, often with laser sight
Good for walk around
temperature checks on
machines and panels
Temperature Paint
Chemical indicators calibrated to
change colors at specified
temperature
Works great for inspection
rounds
Thermography
Handheld still or video camera
sensitive to emitted IR
Best for remote monitoring.
Requires good training
Dynamic Monitoring: Spectrum analysis, shock pulse analysis are some of the
dynamic monitoring methods that measure and analyze energy emitted from
mechanical equipment in the form of waves, vibration, pulses and acoustic
effects. Wear and tear, imbalance, misalignment and internal surface damage
are some of the problems that can be detected with this type of measurement.
Method
Description
Applications
ISO Filtered velocity
2Hz-1kHz filtered velocity
A general condition indicator
SPM
Carpet and Peak related to
demodulation of sensor
resonance around 30kHz
Single value bearing
indicator method
Acoustic Emission
Distress & dB, demodulates a
100kHz carrier sensitive to
stress waves
Better indicator than ISO
velocity, without the ISO
comfort zone
Vibration Meters
Combine velocity, bearing and
acceleration techniques
ISO Velocity, envelope and
high frequency acceleration
give best performance
4-20mA sensors
Filter data converted to
DCS/PLC compatible signal
Useful to home-in on specific
problems by special order
Fluid Analysis: Ferrography, particle counter testing are some of the fluid
analysis methods performed on different types of oils, such as lubrication,
hydraulic, insulation oil etc., to identify any potential problems of wear and
tear in the machines. Machine degradation, oil contamination, improper oil
consistency, oil deterioration are some of the problems that can be detected
with this method. The main areas of analysis in this are:
o Fluid physical properties: Viscosity, appearance
o Fluid chemical properties: TBN, TAN, additives, contamination, % water
o Fluid contamination: ISO cleanliness, Ferrography, Spectroscopy,
dissolved gases
o Machine health: Wear metals associated with plant components
Corrosion Monitoring: Methods such as Coupon testing, Corrometer testing
help identify the extent of corrosion, corrosion rate and state (active/passive
corrosion) for the materials used in the asset.
Non-destructive Testing: Involves using non-destructive methods, such as X-
Rays, ultrasonic etc., to detect any potential anomalies arising internal to the
asset structure. Most of these tests can be performed while the asset is online
and being used
Electrical testing and Monitoring: High potential testing, power signal analysis
are some of the prominent electrical condition monitoring mechanisms that try
to identify any changes in the system properties, such as resistance,
conductivity, dielectric strength and potential. Electrical insulation
deterioration, broken motor rotor bars and shorted motor stator lamination
etc. are some of the problems that can be detected with this type of
mechanism
Observation and Surveillance: Visual, audio and touch inspection criteria are
some of the surveillance condition monitoring techniques based on the human
sensory capabilities. They act as supplement to other condition-monitoring
techniques and help detect problems such as loose/worn parts, leaking
equipment, poor electrical and pipe connections, stream leaks, pressure relief
valve leaks and surface roughness changes etc.
Once the appropriate measurement mechanisms are in place, the next step is the event
definition phase: to define what constitutes an acceptable system behavior and what is
to be considered as anomaly. It is useless to put costly monitoring equipment in place,
without knowing what to expect out of it. Expert opinion and judgment (such as
manufacturer’s recommendations), published information (such as case studies),
historical data etc. are some of the good sources that can help in this task. The
definition of anomaly should be unambiguous and easy to detect. If the cost of anomaly
detection far exceeds the costs of consequences of that anomaly, then it is not a valid
scenario for implementing CBM.
The next step that follows the event definition phase is, determining event inspection
frequency. Frequency of any of form of condition-based-maintenance is based on the
fact that most failures do not occur instantaneously, and that it is often possible to
detect them during their final stages of deterioration. If evidence can be found that
FIG URE 4 MEA SUR EME NT FREQ UEN CIE S C AN BE D ETE RMI NED WI TH P-F I NTE RV AL
something is in the final stages of failure, it is possible to take action for preventing it
from failing completely and/or avoid the consequences.
The failure behavior typically exhibited by
majority of the systems in operation is as
showcased in Figure 4. During operation,
over a period of time, the systems enter a
phase of potential failure (P), and start
displaying few early signs of wear & tear and
other stressful behaviors that if neglected
finally lead to full functional failure (F). For
most of the systems the time interval
between the potential failure point (P) and
full function failure (F) is large enough to
allow detection and prevention of the
failure.
This time gap between P and F is what is
popularly known as the P-F Interval in the
literature, and any cost-effective maintenance strategy should try to maximize on it. The
P-F Interval could be in hours or days, or even weeks or months, based on the
complexity of the system and the unit of measurement - for it is not uncommon to see
the P-F Interval being measured in non-time units, such as stop-start cycles or units of
output etc. Based on the failure mode and the unit of measurement, the P-F Interval can
end up varying from fractions of a second to several decades (on temporal scale).
Be whatever the unit of measurement and the P-F interval, a successful CBMM system
should be capable of detecting the early signals after P and respond to them long before
F. The response action typically consists of multiple steps (as laid out below) and should
all be accompanied within the P-F interval.
1. Analyzing the root-cause based on detected early signals
2. Planning corrective action based on the analyzed root-cause
3. Organizing the resources to implement the laid out plan
4. Actual implementation of the corrective action plan
The amount of time needed for these response actions usually vary, from a matter of
hours (e.g. until the end of operating cycle or end of shift), minutes (e.g. to clear people
from a failing building), to weeks or even months (e.g. until a major shutdown).
Thus, it is a common practice to use the inspection interval to be half the P-F interval.
This will ensure that there is at-least half the P-F interval remaining after the potential-
failure detection for corrective action plan. However, it should be noted that most of
the times earlier the corrective action plans are implemented, lower the cost in which
cases, some other smaller fraction of P-F interval can be used as the inspection interval,
so that potential problems can be detected as early as possible and rectified.
However, an important point to be remembered is P-F interval is not an easy metric to
be computed. It varies from asset type to asset type, environment to environment and
even from one asset to another with in the same asset type (based on its previous fault
history and working conditions). Understanding the failure patterns, and identifying the
class of pattern to which the asset belongs, its past fault history, manufacturer’s
recommendations, operating conditions, expert judgment etc. are some of the sources
that can help in arriving at an accurate P-F interval for any given asset/system.
At each inspection interval, the CBMM system collects data from sensors and uses one
of the following methods to determine the condition of the asset being monitored:
Trend Analysis: Reviews the data to find if the asset being monitored is on an
obvious and immediate downward slide toward failure. Typically a minimum of
three monitoring points are recommended for arriving at a trend accurately as
a reliable measure to find if the condition is deprecating linearly.
Pattern recognition: Decodes the causal relations between certain type of
events and machine failures. For example, after being used for a certain
product run, one of the components used in the asset fails due to stresses that
are unique to that run
Critical range and limits: Tests to verify if the data is within a critical range limit
(set by professional intuition)
Statistical process analysis: Existing failure record data (retrieved from
warranty claims, data archives and case-study histories) is driven through
analytical procedures to find an accurate model for the failure curves and the
new data is compared against those models to identify any potential failures.
Based on the failure mode and asset class the right method for the prediction can vary.
For example, assets that fall into type E class (bathtub pattern) usually benefit from
Weibull distribution, while split system approach is used for complex systems with
multiple sub-systems.
Stream processing the arriving data can help build the trend analysis and critical range
limits, but to accurately process pattern recognition and statistical model building
methods, past history data is as important as the new arriving data. Thus, typically CBM
management systems should keep record of old data for some reasonable amount of
time before they are archived or destroyed. This time period varies from domain to
domain and may even be regulated by local country laws. For example, financial fraud
records may need to be kept active for longer time, in the range of 7 to 15 years per se,
while flight records generated from airplane internal sensors are typically discarded
after the journey completion (primarily due to it being voluminous, though this trend
could soon change as the big data warehousing gets more prominent).
Another reason the old data streams become important is to identify any potential
outliers in the streamed-in data from the sensors. While monitoring for the faults in the
assets, it is possible that the sensor that is taking the readings, being a machine itself,
could fail and start sending faulty records. Intelligent CBM management systems
capable of detecting such outliers will try to isolate these faulty sensors and notify the
FIG URE 5 CB M MA NAG EME NT SYST EM ARCH IT EC TU RE
appropriate personal for corrective action, or substitute it with proper estimated data
based on previous records. In either case, human inspection is as much necessary as a
completely automated monitoring system for automation only complements the
human surveillance efforts, not replace them. Thus, many automated monitoring
systems provide a way for manual
override for configurable parts of
their functionality.
Typically a CBM management
solution will have an administrative
console that lets the operators define
and update various parameters, such
as the critical limits, response
notifications, default corrective
actions etc. Advanced management
systems also allow the administrators
to access the monitoring solution
functionality remotely through web
UI and mobile UI, capable of sending
periodic digests weekly or monthly
for pre-configured stake-holders
reporting the status of the asset
being monitored on a regular basis.
With architectures like the one
shown in Figure 5 and common
interface standards such as IEEE 1451, IEEE 1232, MIMOSA and OSA-CBM, advanced
management systems integrations become possible among disparate software and
hardware components from different vendors, all working hard together just for one
single purpose to provide the operators maximum usage out of their assets.
BEST PRACTICE S GU I D E LINES
Here are few guidelines that can help in formulating the best practices for maintenance
strategies:
Try organizing and reviewing repair tasks in cost descending order.
Since planned component replacement is less costly than unscheduled repair
visits, review maintenance costs by apparatus class and repair task for
improvement.
In case of fleet management, read work order comments for specifics regarding
repairs and note the mileage. Take into consideration the environment where
the vehicle works as the terrain or weather may affect maintenance.
Review current component brands performance. A different brand or a higher
quality of the same brand may last longer.
Investigate each repair task in detail before taking action. Ensure that every
action taken will further reduce maintenance costs and downtime.
After instituting changes, track repairs to validate anticipated results and
document the cost savings. Share the cost savings with all involved personnel
to improve relationships and foster a team approach for a good management
practice.
Look for similarities on specific components that may indicate design flaws or
the need for additional technician training. Frequent breakdowns on specific
units can indicate abuse or poor operator practices. Look for opportunities to
lower maintenance costs and at the same time improve repair practices and
operator care.
One of the highest maintenance costs for fleets is tires; therefore, take time
researching this expense. The right quality tire for the job function will make a
difference. Make sure tire ratings are correct for the weight of the vehicle fully
loaded. Consider the tread design, ply rating, composition, and heat rating.
Review service calls, breakdowns, and towing expenses for possible predictive
maintenance by unit and cause by month. Note that it incurs additional costs to
dispatch a vendor or an employee to perform on -site repairs or towing units to
a repair facility. As instances are reviewed and appropriate action is taken, the
number of such instances should decrease, thereby reducing maintenance
costs.
CONCLUDING REMARKS
Emergence of uniquely addressable embeddable devices has raised the bar on M2M
capabilities. Though the technology itself is not new, its application has been quite
limited until now. M2M technologies generate volumes of data that are orders of
magnitude larger than what operators have dealt with previously. Real-time big data
computation capabilities have opened the flood gates for creating new predictive
analytics capabilities into an otherwise simple data log systems, enabling real-time
control and monitoring to take preventive action in case of any anomalies. Condition-
based-maintenance, usage-based-insurance, smart metering and demand-based load
generation etc. are some of the predictive analytics use cases for M2M. The possibilities
are rich and early players are reaping the benefits through cost-savings and innovative
service offerings.
REFERENCES
Moubray, John. Reliability-Centered Maintenance. Industrial Press. New York,
NY. 1997
Nowlan, F. Stanley, and Howard F. Heap. Reliability-Centered Maintenance.
Department of Defense, Washington, D.C. 1978. Report Number AD-A066579
NFPA 1911, Standard for the Inspection, Maintenance, Testing, and Retirement
of In-Service Automotive Fire Apparatus, 2007 Edition, 6.1.5.1, p1911-14
Weibull, W. "A statistical distribution function of wide applicability", Journal of
Applied Mechanics-Trans. ASME 18 (3): 293297. 1951
ABOUT AUTHOR
Gopalakrishna Palem is a Corporate Technology Strategist specialized in Distributed Computing systems
and Cloud operations. During his 12+ year tenure at Microsoft and Oracle, he helped many customers
build their high volume transactional systems, distributed render pipelines, advanced visualization &
modeling tools, real-time dataflow dependency-graph architectures, and Single-sign-on implementations
for M2M telematics.
When he is not busy working, he is actively engaged in driving open-source efforts and guiding
researchers on Algorithmic Information Theory, Systems Control and Automata, Poincare recurrences for
finite-state machines, Knowledge modeling in data-dependent systems and Natural Language Processing.
He can be reached at Gopalakrishna.Palem@Yahoo.com
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This paper discusses the applicability of statistics to a wide field of problems. Examples of simple and complex distributions are given.