Reliability Analysis and Prediction of Time to Failure Distribution of an Automobile Crankshaft
This paper emphasizes on analysing and predicting the reliability of an automobile crankshaft by analysing the time to failure (TTF) through the parametric distribution function. In this paper, the TTF was modelled to predict the likelihood of failure for crankshaft during its operational condition...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
| Published: |
BazTech
2015
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/12928/ http://umpir.ump.edu.my/id/eprint/12928/ http://umpir.ump.edu.my/id/eprint/12928/ http://umpir.ump.edu.my/id/eprint/12928/1/Eksploatacja%20i%20Niezawodnosc%20%E2%80%93%20Maintenance%20and%20Reliability-2.pdf http://umpir.ump.edu.my/id/eprint/12928/7/fkm-2015-nikabdullah-reliability%20analysis%20and%20prediction%20of%20time%20to%20failure%20distribution.pdf |
| Summary: | This paper emphasizes on analysing and predicting the reliability of an automobile crankshaft by analysing the time to failure (TTF) through the parametric distribution function. In this paper, the TTF was modelled to predict the likelihood of failure for crankshaft during its operational condition over a given time interval through the development of the stochastic algorithm. The developed stochastic algorithm has the capability to measure the parametric distribution function and validate the predict the reliability rate, mean time to failure and hazard rate. T, the algorithm has the capability to statistically validate the algorithm to obtain the optimal parametric model to represent the failure of the component against the actual time to failure data from the local automobile industry. Hence, the validated results showed that the three parameter Weibull distribution provided an accurate
and efficient foundation in modelling the reliability rate when compared with the actual sampling data. The suggested parametric distribution function can be used to improve the design and the life cycle due to its capability in accelerating and decelerating the mechanism of failure based on time without adjusting the level of stress. Therefore, an understanding of the parametric distribution
posed by the reliability and hazard rate onto the component can be used to improve the design and increase the life cycle based on the dependability of the component over a given period of time. The proposed reliability assessment through the developed stochastic algorithm provides an accurate, efficient, fast and cost effective reliability analysis in contrast to costly and lengthy experimental techniques. |
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