Examine the changing condition on fleet of vehicles



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EXAMINE THE CHANGING CONDITION ON FLEET OF VEHICLES

Mikhail Milchev, Nikolai Kolev, Emil Savev, Daniel Liubenov

Summary: The article discussed the main points of amendment of the technical condition of commercial vehicles of different class. An analysis is to assess their reliability and optimizing the organization of work on their maintenance.

Introduction

The car industries aims and objectives are to create highly reliable and lasting vehicles. This will ensure normal and safe exploitation of all vehicles. It is particularly important in the transport logistics companies. Every unexpected breakdown of the vehicle could lead to unplanned expenses and financial loses. Regular inspections of each vehicle will reduce the chances of breakdowns. There is always possibility of the vehicle breaking down but regular inspections will reduce it [1].

The research is done to examines the randomness of vehicle failures. The aim of this research is to evaluate the reliability of the fleet of commercial vans from two different brands. This evaluation will determine the choice of service-station to maintain the vehicles.

The research:

Before we start we are going to define some of the main concepts regarding the reliability of the commercial vans [2], [4].

The average production rate could be shown with this mathematical equation

(1)

Where f(t) is the density of the distribution of random variable t.

When statistical data is used, the average production rate until failure for the tested group of vehicles from one brand is determined by the relation of the overall working time until failure of each vehicle emerges and the overall number of vehicles.

(2)

Where ti is the time without any failures; n shows the total number of examined vehicles.

The production rate between two failures between time (t1 and t2) is

(3)

The possibility for working time without failure in the period between t1 and t2 is



(4)

The transport company consists of limited amount of vehicles of two brands. Those vehicles can be in any of these conditions: excellent, need for repair etc. This is why we enter the random variable xi (t); i=1,2,3,...,x: and xj (t); j=1,2,3,...,x . These formulas show the current amount of vehicles in i and j condition .

The sum of the vehicles in all conditions is equivalent to the number of vehicles in the fleet.

(5)

The criteria for choosing the examined vehicles are by covered distance and weight capacity [3]. Only vehicles which covered more than 100k km are selected from the fleet. Commercial vans with capacity up to 800 kg are used for transportation of small packages. From the first brand only five vehicles match these criteria. From the second brand four vehicles match the same criteria. The total number of vehicles from the first brand (brand ‘A’) is 40 and second brand (brand B) is 70. The inspected vehicles are maintained in specialised brand service centres until reaching 100k km covered distance. After 100k km is covered the warranty is off and vehicles are no longer maintained in the same centre. The vehicles are used in different regions in Bulgaria.

The whole covered distance is divided into 13 intervals for brand A and 12 for brand B. The tables show the number of failures between each interval.

Distribution of failures fleet A Table 1






Car 1

Car 2

Car 3

Car 4

Car 5

Up to 10k.км

1

2

3

1

0

Up to 20k.км

2

2

3

2

0

Up to 30k.км

2

4

5

3

1

Up to 40k.км

5

5

8

4

1

Up to 50k.км

7

5

8

5

6

Up to 60k.км

11

7

8

18

6

Up to 70k.км

13

8

8

20

11

Up to 80k.км

13

10

8

24

13

Up to 90k.км

23

10

11

27

26

Up to 100k.км

30

11

13

40

37

Up to 110k.км

66

11

14

51

52

Up to 120k.км

106

17

14

58

52

Up to 130k.км

106

18

27

63

52

Distribution of failures fleet B Table 2






Car 1

Car 2

Car 3

Car 4

Up to 10k.км

0

0

0

0

Up to 20k.км

0

0

0

0

Up to 30k.км

0

0

5

0

Up to 40k.км

2

0

9

0

Up to 50k.км

7

0

11

0

Up to 60k.км

9

4

14

0

Up to 70k.км

11

19

23

8

Up to 80k.км

19

37

31

24

Up to 90k.км

22

43

37

39

Up to 100k.км

32

63

55

54

Up to 110k.км

39

86

62

63

Up to 120k.км

47

133

99

69

The data is shown in 2 different graphs.

Fig. 1 Number of failures according covered distance in brand A.



Fig.2 Number of failures according covered distance in brand B.

Looking at the analysis for brand a we can see steady rise in the number of failures as the covered distance gets higher. The number of failures rises as the car is normally exploited and this is due to amortization. The pattern for number of failures stays the same until the car reaches 100k km covered distance. After the vehicle reaches 100 k km the warranty is off and the vehicle is no longer obliged to visit the specialised brand service centre. After this we can see steep rise in the number of failures as high as 79.89% [2]. This is probably due to the poor technical maintenance, bad quality reserve parts and less qualified mechanics.

The example of 100k km shows that 48 amount of repairs were made on suspension and from 100k km and up 59 amount of repairs is made.

The warranty for vehicles in brand B is also 100k km and the number of failures also rises a lot after the warranty is off but it is a little bit less than brand a at 46.57%. This rise of failures is caused by the same reasons stated above for brand A.

On both graphs there is uneven distribution of failures in the vehicles from the two brands. For example in vehicle 1 from brand A holds 40 % of the total number of failures in this brand. In brand b we can see a similar occurrence.

This could be caused by the different circumstances the vehicles are exploited and also the level of qualification of their drivers etc.

Conclusion

The condition of the vehicles examined from both brands becomes worse after the warranty is off and due to the poor maintenance, parts, less qualified mechanics and also the amortization of the vehicle.

The tendency of failures in the graphs could be affected by using better reserve parts, hiring better more qualified mechanics at the service centre, opening a special service centre for the company vehicle and raising the overall quality of maintenance for the vehicles.

Such studies can be used to predict failure of vehicles.



Reference

1. Димов Димитър, Петър Димитров, Христо Белоев, Калоян Стоянов, Относно необходимостта от определяне комплексното влияние на основни нормообразуващи фактори върху технико-експлоатационни показатели на земеделските агрегати. Сп.ССТехника-бр.6, 2006.

2. Митков Атанас, Димитър Минков. Статистически методи за изследване и оптимизиране на селскостопанска техника. Земиздат.

3. Симеонов Д.,В. Пенчева. Взаимодействие на видовете транспорт. Русенски Университет. Русе 2002

4. Стоянов Калоян, Христо Белоев. МЕТОДИКА ЗА ИЗСЛЕДВАНЕ ВЛИЯНИЕТО НА СИСТЕМАТА ЗА ПРИНУДИТЕРНО ПЪЛНЕНЕ НА ТРАКТОРНИЯ ДВИГАТЕЛ ВЪРХУ НЯКОИ ТЕХНИКО-ЕКСПЛОАТАЦИОННИ ПОКАЗАТЕЛИ НА ЗЕМЕДЕЛСКИЯ АГРЕГАТ. НК-РУ,10.2008

The study was supported by contract № BG051PO001-3.3.04/28, "Support for


the Scientific Staff Development in the Field of Engineering Research and
Innovation”. The project is funded with support from the Operational
Programme "Human Resources Development" 2007-2013, financed by the
European Social Fund of the European Union.

Address


University of Ruse, 8 Studentska Str., 7017 Ruse, Bulgaria

Department of Transport

Tel. +359082/888 605

e-mail: mmilchev@uni-ruse.bg







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