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
Aσ
=
σ
=
,6
,
0.5
,
10
a B u
N
B [image:2.612.81.519.79.273.2]σ
=
σ
=
,σuis stress limit, it can be calculated that:b=11.75,B=289.90.Figure 1. Stress-cyclic number characteristic curve.
Introduces the coefficient of k(Ac), 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 headAcstress amplitudeσa(Ac,az) andσm(Ac,az), axle head acceleration amplitude aazand mean valueamz
.
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
u, affected mean value, we can fix the stress by Goodman correction model: u z c m z c a z ca 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 stressσi, corresponding to
fatigue lifeNiwith 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 1σ
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
[image:3.612.184.426.398.549.2]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).
Reference
[1] Zhang Lu, Ji Wei, Zhou Wei, el at. Analysis of Time-dependent Reliability and Its Sensitivity of Vehicle Components [J]. Journal of Highway and Transportation Research and Development, 2015, 32(10): 146-152.
[2] Zhang Lu, Ji Wei, Li Wenliang, el at. Time-dependent Reliability Design of Vehicle Driving Axle [J]. Journal of Highway and Transportation Research and Development, 2016, 33(4): 149-152.
[3] Zhou Wei, Li Wenliang, Guo Zhiping, et al. Study on Enhancement Coefficient of Washboard Road of Automobile Proving Ground [J]. Journal of Highway and Transportation Research and Development, 2008, 25(11): 140-144.
[4] Li Wenliang, Gao Li. Intensifying coefficient of the moderate standard washboard road of the automobile proving ground [J]. Journal of Vibration, Measurement & Diagnosis, 2015(6): 1110-1115.
[5] Li Wenliang, Zhou Wei, Zhang Lu. A Method for Constructing Reliability Target Load Spectrum of Taxi Customer [J]. Journal of Highway and Transportation Research and Development, 2016, 33(2): 130-134.
[6] Li Wenliang, Zhou Wei, Zhang Lu, el at. A Method for Constructing Customer Target Load Spectrum Considering Distribution of Road Roughness and Velocity [J], Journal of Highway and Transportation Research and Development, 2016, 33(12):154-158.
[7] Wang Shaomin. Research on Failure Regularity and Maintenance Strategy of Key Components of EMU Bogie [D]. Beijing Jiaotong University, 2016.
[8] Ding Feng, He Jiazheng. Cutting Tool Wear Monitoring for Reliability Analysis Using Proportional Hazards Model [J]. International Journal of Advanced Manufacturing Technology, 2011, 57(5-8): 565-574.
[9] Chen Baojia, Chen Xuefeng, Li Bing, et al. Reliability Estimation for Cutting Tools based on Logistic Regression Model Using Vibration Signals[J]. Mechanical Systems and Signal Processing, 2011, 25(7): 2526-2537.
[10] Fong B., Li C.K. Methods for Assessing Product Reliability: Looking for Enhancements by Adopting Condition-based Monitoring [J]. Consumer Electronics Magazine, 2012, 1(1):43-48.
[11] He Zhengjia, Cai gaigai, Shen Zhongjie, et al. Study of operation reliability based on diagnosis information for mechanical equipment [J]. Engineering RDciences, 2013, 15(1):9-14.
[13] Sun Yuanzhang, Cheng Lin, Liu Haitao, et al. Power system operational reliability evaluation based on real time operating state. Proceedings of 7th International Power Engineering Conference (IPEC2005) [C]. New Jersey: IEEE Computer Society, 2005: 722- 727.
[14] Cheng Lin, He Jian, Sun Yuanzhang. Impact of Transmission Line’s Real-Time Reliability Model Parameter upon Power System Operational Reliability Evaluation [J]. Power System Technology, 2006, 30(13): 8-13.
