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Abstract

Vehicle maintenance is a critical and vital operational component that determines vehicle performance and service longevity. The severity of vehicle usage has been defined as one of the key factors that determine vehicle maintenance requirements. Nigeria is a tropical country with intense heat, poor road network and quality. These factors severely affect car performance and raises maintenance demands toward ensuring vehicle reliability and optimum performance. In this study, vehicle maintenance cost and fuel consumption data in terms of cost and volume, together with the mileage coverage as the vehicle usage data, for two corporate organizations were analyzed. The data was collected and systematically analyzed. The common faults were categorized and their frequency of occurrence was determined. An artificial Neural Network (ANN) model was developed for predicting future maintenance cost, given a set of anticipated vehicle usage inputs. The model has an overall correlation R-value of 0.76645.
IOP Conference Series: Materials Science and Engineering
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A data-based investigation of vehicle maintenance cost components
using ANN
To cite this article: Aderibigbe Israel Adekitan et al 2018 IOP Conf. Ser.: Mater. Sci. Eng. 413 012009
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ICESW IOP Publishing
IOP Conf. Series: Materials Science and Engineering 413 (2018) 012009 doi:10.1088/1757-899X/413/1/012009
A data-based investigation of vehicle maintenance cost
components using ANN
Aderibigbe Israel Adekitan1,*, Adetokun Bukola2 and Okokpujie Kennedy1
1Department of Electrical and Information Engineering, College of Engineering,
2Department of Electrical Engineering, Pan African University of Basic Sciences,
Technology and Innovation, hosted at Jomo Kenyatta University of Agriculture and
Abstract. Vehicle maintenance is a critical and vital operational component that determines
vehicle performance and service longevity. The severity of vehicle usage has been defined as
one of the key factors that determine vehicle maintenance requirements. Nigeria is a tropical
country with intense heat, poor road network and quality. These factors severely affect car
performance and raises maintenance demands toward ensuring vehicle reliability and optimum
performance. In this study, vehicle maintenance cost and fuel consumption data in terms of cost
and volume, together with the mileage coverage as the vehicle usage data, for two corporate
organizations were analyzed. The data was collected and systematically analyzed. The common
faults were categorized and their frequency of occurrence was determined. An artificial Neural
Network (ANN) model was developed for predicting future maintenance cost, given a set of
anticipated vehicle usage inputs. The model has an overall correlation R-value of 0.76645.
Keywords: ANN model, vehicle maintenance, predictive cost analysis, data pattern
recognition, road transportation, statistics
1. Introduction
Supply chain reliability is the back bone of many commercial operations, and it also ensures adequate
distribution and availability of various products, supplies and equipment for both domestic and industrial
uses. Product and material distribution can be via air, water, rail and road transportation. Transportation
is vital for socio-economic interactions. Freight transportation must be cost effective, efficient, safe, fast
and reliable to ensure itch-free national distribution of goods and economic development. According to
[1], road transportation is the most active mode for haulage transportation. It was estimated that 75% of
the total 2200 billion tonne-kilometres of freight transportation in Europe was via road. In the Nigerian
transport industry, the road transport sub-sector contributes about 90% to the Gross Domestic Product.
As at the end of 2017, the estimated number of vehicles in Nigeria was 11,583,331 [2, 3].
The transportation industry in Nigeria is plagued by many challenges due to poor planning, poor road
network and condition, scarce funds, corruption and defective policies, insufficient vehicle fleets, and so
forth. Vehicles daily ply poorly constructed, and poorly maintained roads with pot holes and many
unpaved road sections which create inherent hazards [4]. This negatively impacts vehicle reliability,
resulting in repetitive vehicle breakdowns with an attendant increase in maintenance cost. To ensure
vehicle availability for service, corporations need to dedicate a sizable amount of their budget towards
vehicle maintenance and overhaul. Most of the vehicles in Nigeria were imported. It is only in recent
times, due to new Government policies that both indigenous and foreign car assembling plants are
springing up again in the country,
after decades of zero production due to various economic challenges.
Foreign Exchange (FOREX) issue is a recurrent problem in Nigeria due to FOREX scarcity and price
fluctuations. Currently, one US dollar is around 365 Naira. This makes vehicle importation very
expensive, and as a result of the high cost, most of the cars and trucks imported into the country are
fairly-used, and they are rarely brand new. This implies that a good percentage of the service life of the
vehicle is used up before arrival in Nigeria, and upon arrival into the country, deterioration sets in due to
many factors and realities of the Nigerian society. The only way to ensure vehicle reliability and prolong
Covenant University, Ota, Nigeria
Technology, Nairobi, Kenya
*E-mail address: ade_kitan@yahoo.com
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IOP Conf. Series: Materials Science and Engineering 413 (2018) 012009 doi:10.1088/1757-899X/413/1/012009
vehicle service life is through adequate and timely vehicle maintenance [5], but this is often challenged
by the need to manage scarce resources.
A visit to the car pack and maintenance warehouse of some organizations will reveal run-down and
abandoned vehicles, with the tyres of some of the vehicles already removed, and the chassis supported by
stone slabs. A number of times, this occurs as a result of scarce spare part, low maintenance budget and
excessive car breakdowns leading to abandonment. Vehicles abandoned in this way end up deteriorating
further with time, and this increases the cost of restoration, and reduces the sale value, if the vehicle is
sold off in that state. If such cars are parked in open and unfenced locations in front of factories without
adequate security, the car parts end up being stolen by scavengers. Cases of stolen head lights, tyres,
batteries and so forth, have been reported.
In maintenance engineering and management, a number of maintenance components and parameters can
be extracted for objective analysis [6]. This study presents and examines the maintenance data of two
corporate organizations for fault frequency trending and for data parameter relationship identification.
An ANN model was developed using the fuel consumption, cost of fuel, mileage covered and the
maintenance cost data for the sampled period as model parameters.
2. Case study: haulage company truck and corporate pool car maintenance
The effect of the harsh economic conditions and poor infrastructural development is quite severe on
haulage company vehicles that need to move bulk load from one point to another [7]. The trucks are the
lifeline of the companies and must be operational to deliver value. According to the study in 2009 by
AAA Car Care, more than 62% of vehicles are operated on severe service conditions, some of these
conditions include:
Driving in hot weather under frequent stop and start traffic
Driving on dusty or muddy roads and on roads with gravel spread
Driving below 50 miles per hour over long distances
Transporting heavy loads or towing a trailer
Nigeria is a tropical country, with an average temperature of about 28C, which could rise to about 35C
in the northern region. When haulage trucks travel between local communities, a number of unpaved
roads will be encountered. Most of these roads are unpaved, dusty and muddy, and the pot holes in some
road sections are filled with stones and pebbles. Poor roads and insufficient road network creates heavy
traffic along major roads, and this induces stop and start vehicle movement. Since there is no dedicated
lane for trucks and bulk vehicles, heavy loaded trucks caught up in traffic are also forced to stop and start
with the slow moving traffic. By these realities, most vehicles in Nigeria are operating under severe
conditions, and this requires frequent maintenance and oil change to limit damage, and prolong vehicle
service life. Frequent maintenance has an associated cost implication which may result in non-
compliance in a bid to reduce expenses. In Europe, road transport accounts for 17.5% of total greenhouse
gas emissions [8], and this will be aggravated in developing countries by poorly maintained vehicles with
sooty vehicle exhausts.
A. The haulage company truck maintenance analysis
Commercial trucks travel thousands of kilometres with heavy loads every year, and this journey is most
times under severe conditions. Prior to embarking on a journey, trucks ought to meet the minimum safety
and maintenance requirement to prevent sudden breakdown en route to their destination. This is often not
the case; a number of broken down trucks can be seen on major highways and on interstate routes in
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The performance of the model can be analysed using the MSE and the correlation coefficient (R-value)
of the regression, which indicates how close the model output prediction is, to the target (maintenance
cost). As shown in Figure 8, for the training data set, the R-value is 0.76833, it is 0.76893 for the
validation, and 0.80774 for the test. The overall R-value for the three analyses is 0.76645. As shown in
Figure 8, the dashed lines in the regression plot represent a perfect line of fit, when the output is exactly
equal to the target; this is an ideal situation. The solid lines in the regression plot stands for the real
regression line showing the true relationship between the target and the predicted maintenance cost. The
R-value can be increased by using more data, say for a period of two years in order to increase the
training dataset. The best validation performance of the model as measured using the MSE occurred at
epoch (iteration) 7, as shown in Figure 9. The MSE gradually reduced from the peak value until the
model execution was terminated after 6 consecutive validation failure checks. Figure 10 shows the
variation of the gradient coefficient, the Marquardt adjustment parameter (Mu), the total validation fails
and the number of iterations.
Fig.8 Regression plot
Fig.9 ANN model validation performance plot using mean squared error
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Figure 11 shows the output of the model and the desired target (MC) for the training, validation and
model test analysis. The error histogram is shown in Figure 12, and it reveals that the maximum model
error for the maintenance cost prediction is 2397 Naira. The error is computed as the difference between
the target and the model output. . The magnitude of the error can be reduced by using more training data
to improve the accuracy of the model.
Fig.10 Network Training State
Fig.11 Output element response plot
Fig.12 Error Histogram
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IOP Conf. Series: Materials Science and Engineering 413 (2018) 012009 doi:10.1088/1757-899X/413/1/012009
... A basic model of an artificial neuron is shown in Fig. 2. ANN has been applied in different fields of study. ANN has been used for forecasting, prediction, and data classification [12], optimization [13], maintenance data analysis [14], load forecasting [15], educational data studies [11], climate studies [16], etc. To achieve an ANN-based DEGM; a 7-input neural network model was developed using a two-layer feed-forward network comprising of 18 neurons, as shown in Fig. 3. ...
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