Reliability analysis and prediction for time to failure distribution of an automobile crankshaft

Singh, Salvinder Singh Karam; Abdullah, Shahrum; Mohamed, Nik Abdullah Nik

doi:10.17531/ein.2015.3.11

Cited by 12 publications

(7 citation statements)

References 30 publications

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“…Reliability is an important tool in lifetime prediction for components especially in the electronic industry [16]. Evaluating the reliability indices such as the MTTF (mean time to failure) and percentile of the cycle life distribution, is an essential task in cycle life prediction for Lithium-ion cells.…”

Section: Life Prediction Ignoring Temperature Effectmentioning

confidence: 99%

Cycle life prediction of lithium-ion cells under complex temperature profiles

Cheng

Pan

et al. 2016

EiN

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These studies mentioned above, however, are mainly conducted based on the cycle life tests under one or more deterministic temperature levels, in which the high-precision temperature chamber and rigid data acquisition methods are required. Unlike the laboratory testing condition, cells in the field-use condition are usually run under complex temperature profiles. Previous researches indicate that the capacity fading process of Lithium-ion cells is significantly influenced by temperature [5,7,10]. In [10], capacity fading of Lithium-ion cells can be divided into true capacity fading and temporary capacity loss. True capacity fading leads to permanent capacity loss as a result of lithium ion and active material consuming, where high temperatures will accelerate the fading rate. On the other hand, temporary capacity loss is due to the temperature drop in a certain cycle, which is somewhat recoverable if the temperature goes back. A more accurate capacity fading model can be obtained by considering these temperature effects, especially for the cells operating under field-use conditions.However, because of the difficulty in modeling the complicated temperature effects, the existing papers regarding capacity fading modeling or cycle life prediction under complex temperature profiles are very rare. In most of them, the classic regression-based approach [6] is adopted, which assumes that field conditions are deterministic or to simply use the mean value of temperatures while ignore their variability. This approach may result in significant prediction errors. For predicting the cycle life of Lithium-ion cells without the temperature chamber, this paper proposes a cycle life prediction method considering complex temperature profiles, including a cycle life test plan and an improved capacity fading model.In our test, cells experience ambient temperature that continuously varies at all times. It will lead to the variance of cell capacity. The variance contains abundant information about cycle life and the relationship between cycle life and temperature. If we can effectively mine the information from the immense performance data using data modeling methods, the cycle life of Lithium-ion cells can be predicted accurately.In this paper, firstly, the cycle life test plan for Lithium-ion cells under complex temperature profiles is introduced. Then, the classic regression-based life prediction method which ignores temperature effects is reviewed. Based on the classic method, we establish a more accurate capacity fading model, by taking into account the effects of temperature on both the actual capacity fading and the temporal capacity loss. Using the data acquired from the cycle life test, the parameters of the model are estimated. At last, the cycle life of this type of Lithium-ion cells is predicted. Experimental Testing proceduresIn the cycle life test, 6 LiFePO 4 18650 cells, which are indexed as Cell #1, Cell #2,· · ·, Cell #6 respectively, are used to charge and discharge repetitively. The parameters setup of these cells i...

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Section: Life Prediction Ignoring Temperature Effectmentioning

confidence: 99%

Cycle life prediction of lithium-ion cells under complex temperature profiles

Cheng

Pan

et al. 2016

EiN

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“…It is demonstrated that the reliability of a vehicle is very different because of its driving [10]. e reliability of an automobile crankshaft is analyzed and predicted through analyzing the time to failure [11]. A method of estimating automotive component reliability uses field warranty repair data that comprises early failure data [12].…”

Section: Introductionmentioning

confidence: 99%

Ergonomic Reliability Assessment for Passenger Car Interface Design Based on EWM-MADM and Human Cognitive Reliability Experiments

Liu

Chen

et al. 2020

Mathematical Problems in Engineering

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The ergonomic reliability assessment of interface design scheme for the passenger car is very practical to enhance driving safety performance. In addition, it can significantly reduce development costs and the development cycle of the new car. From the perspective of guiding the improvement of the central console interactive interface design of the passenger car, the most effective method to build the ergonomic reliability assessment method of the interactive interface is to evaluate and predict the human reliability objectively and subjectively and to design, feedback, and guide the design process of the ergonomic interface for the passenger car. Firstly, the questionnaire survey and the classification of ergonomic reliability analysis factors are analyzed to be put forward based on predecessors; the judgment layer factors and index layer factors of human-machine interaction interface in automobile central console are put forward. Secondly, entropy weight method (EWM) and multiple attribute decision-making (MADM) were used for objective evaluation and subjective evaluation, respectively. Thirdly, the interaction interfaces in central consoles of three different passenger cars are taken as examples; objective simulated experimental test based on entropy weight method and subjective scoring evaluation based on MADM were conducted, respectively. Besides, the objective evaluation and subjective evaluation are coupled by fuzzy comprehensive evaluation. Finally, to verify the effectiveness and rationality of the ergonomic reliability assessment method, human cognitive reliability experiments are made based on the data acquisition from the eye-tracking experiments.

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“…The extremely loaded component of every vehicle is obviously the power unit and its crank-piston assembly components, the gearbox and engine equipment. The source of main loads acting on the assembly are gas and inertia forces arising during combustion of the fuel-air mixture, resulting from masses set in reciprocating or rotary motion (Singh et al, 2015). The nature of these forces is periodic, and their frequency depends directly on the rotational speed of the motor shaft.…”

Section: Introductionmentioning

confidence: 99%