2017 2nd International Conference on Computer, Mechatronics and Electronic Engineering (CMEE 2017) ISBN: 978-1-60595-532-2

## Vehicle Operation Reliability Evaluation Model Under

User's Actual Service Condition

Wen-liang LI, Chen CAO

*, Wei ZHOU and Lu ZHANG

Research Institute of Highway Ministry of Transport, Beijing 100088, China

*Corresponding author

**Keywords:** User's actual service condition, Axle head acceleration, Pseudo damage, Vehicle

operation reliability evaluation model.

**Abstract.** In order to evaluate the vehicle reliability in the process of user's use, a fatigue pseudo
damage reliability evaluation model Based on axle head acceleration is proposed. Based on the
acceleration, an improved MINER cumulative fatigue damage model is studied. Based on the model,
the influence of user condition, running speed, load capacity and pavement condition on vehicle
reliability is discussed. The reliability evaluation model of vehicle operation under the condition of
user use is established. The model can be used for on-line, short-term and long-term evaluation and
prediction of vehicle reliability.

## Introduction

Improving the reliability of vehicle use is an effective means to solve the frequent accidents, save transportation cost and improve transportation efficiency. Many researchers at home and abroad of vehicle reliability optimization design [1, 2], proving ground reliability enhancement test [3, 6] t, user use reliability failure rule [7] and so on has carried on the certain research, some results were obtained. The reliability optimization design and enhancement test belong to the problem of reliability before user's use. The study of failure rule belongs to the problem of reliability after user's use. However, there are few studies on reliability of the vehicle in the process of user's use

*b*
*a*
*B*
*N*=

σ

−(1)

As shown in Fig.1, At point A

(

σ

*a A*

_{,},

*NA*

)

and point B(

σ

*a B*, ,

*NB*

)

,3

,

0.9

,

10

*a A* *u*

N

*A*

σ

=

σ

=

,6

,

0.5

,

10

*a B* *u*

N

*B*[image:2.612.81.519.79.273.2]

σ

=

σ

=

,σ*is stress limit, it can be calculated that:*

_{u}*b*=11.75,B=289.90.

Figure 1. Stress-cyclic number characteristic curve.

Introduces the coefficient of *k*(*A _{c}*), specific values are determined by the location, geometry size
and material properties depend of the components. There is the following relationship between the
four variables: the axle head

*A*stress amplitudeσ

_{c}*(*

_{a}*A*,

_{c}*a*) andσ

_{z}*(*

_{m}*A*,

_{c}*a*), axle head acceleration amplitude

_{z}*a*and mean value

_{az}*a*

_{mz}.

*az*
*c*
*z*

*c*

*a*(*A*,*a* )=*k*(*A*)*a*
σ
(2)
*mz*
*c*
*z*
*c*

*m*(*A*,*a* )=*k*(*A* )*a*
σ

(3)

For the stress is less than

σ

*, affected mean value, we can fix the stress by Goodman correction model:*

_{u}*u*

*z*

*c*

*m*

*z*

*c*

*a*

*z*

*c*

*a* _{A}_{a}

*a*
*A*
*a*
*A*

σ

σ

σ

σ

) , ( 1 ) , ( ) , ( ' − = (4)According to Eq. 1-4, we can get fatigue life based on acceleration:

*b*
*u*
*mz*
*c*
*az*
*c*
*c*
*b*
*z*
*c*

*c* _{k}_{A}_{a}

*a*
*A*
*k*
*A*
*B*
*a*
*A*
*A*
*B*
*N*
−
−
−
=
=

σ

σ

) ( 1 ) ( ) ( ) , ( ) ( (5)## Fatigue Damage Accumulation Model

The Miner fatigue cumulative damage criteria do not take into account stress that less than fatigue limit value. Take into consideration Haibach extension feature, the part of stress-cycle times curve

where under fatigue limit in logarithmic relationship has been extended with slope of

1 2

1

-−

<
≥
=
−
−
−
−
−
1
1
2
1
1
1
σ
σ
σ
σ
σ
*a*
*b*
*a*
*b*
*a*
*BN*
*BN*
(6)

At the same time, the stress of material was divided into two parts,

[

0.5σ σ

_{-1},

_{-1}

)

,[

σ

_{-1},

σ

*u*

]

corresponding to Haibach model and Miner model. Supposing that the stressσ* _{i}*, corresponding to

fatigue life*N _{i}*with level 1 to

k

is greater than or equal to fatigue limitσ

-1, levelk

+

1

tol

greater than1

0.5

σ

− and less thanσ

-1. The fatigue damage cumulative formula is as follows:∑

∑

∑

∑

+ = − − = − + = = + = + =*l*

*k*

*i*

*b*

*i*

*i*

*k*

*i*

*b*

*i*

*i*

*l*

*k*

*i*

*i*

*i*

*k*

*i*

*i*

*i*

*B*

*n*

*B*

*n*

*N*

*n*

*N*

*n*

*D*1 ) 1 2 ( 1 1 1

σ

σ

(7)Combining Eq. 6 and 7, the fatigue accumulative pseudo damage calculation model based on axle head acceleration has been given.

## The Effect of User's Actual Condition on Fatigue Pseudo Damage

**The Effect of Speed on Pseudo Fatigue Damage **

Supposing that the empty vehicle running on the road of level A, we can use fatigue pseudo damage model that based on acceleration to calculate the axle head damage with 10-100km/h speed, as shown in Fig.2:

Figure 2. The axle head pseudo damage of 10-100KM/H speed on road of A level irregularity.

It can be seen from Fig.2, the axle head pseudo damage increases linearly with speed increasement when the vehicle running at a speed of 10-100KM/H on road of A level irregularity.

## Road Irregularity

Figure 3. The axle head pseudo damage at 50km/h on road irregularity level A -D.

**Sprung Mass **

Take a speed of 50km/h for an example that respectively calculate the axle head pseudo damage when a unladen vehicle and a full load running on the road of level A irregularity. As shown in Fig.4:

Figure 4. The axle head pseudo damage of different load capacity vehicle operating on level A road.

It can be seen from Fig.4, the effect of load capacity on axle head pseudo damage is relatively small.

## The Reliability Evaluation Model of User's Actual Use

[image:4.612.169.440.305.472.2]Figure 5. The relationship between axle head pseudo damage and road irregularity and speed.

Assuming that v is speed, k is the power spectral density geometric mean of road irregularity, d is axle head pseudo damage. Considering the sensitivity of vehicle body and road irregularity of power spectral density to pseudo damage, we can take a base-10 logarithm of the road irregularity power spectral density and pseudo damage to build the fitting formula of d (k, v) :

( )

( )

( )

( )

6 2 3 2

7 3 6 3

log 19.654 4log +0.123 +1.256 10 log 1.664 10

1.746 10 log 7.56 10

*d* *k* *v* *k* *v*

*k* *v*

− −

− −

= − + × − ×

− × + ×

(8) With speed and power spectral density geometric mean of road irregularity, the fitting curved surface of the base-10 logarithm pseudo damage is shown in Fig.6.

Figure 6. The fitting effect of the relationship with axle head pseudo damage and speed and road irregularity.

In Fig.6, the dot represents initial value and the surface represents fitting curved surface. As known from the calculation, this fitting model satisfies the precision requirement, because of the average relative error of the fitting value and the initial value is 6.50%, the maximum relative error is 13.11%, and the correlation between the fitting data and the initial data is 99.67%.

## Conclusion

In this paper, the influence rule of the user's actual conditions on the axle head pseudo damage of acceleration is discussed. The road irregularity is the most sensitive, the speed is the second, the load capacity is the least.

According the user’s actual conditions on road (the power spectral density geometric mean of road irregularity) and speed, this paper builds the axle head pseudo damage prediction model under user’s actual conditions. It can realize the vehicle reliability of online, short-term, long-term estimation.

**Acknowledgement **

We thank the support of the special fund project in basic scientific research operation from the central public welfare science research institute (No. 2014 - 9042).

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