Abstract
There is a growing body of literature which recognizes the importance of mechanical equipment reliability during processing, and reliability assessment is important in guaranteeing the precision, function, and use life span of mechanical equipment. For products with a long lifetime and high reliability, it is difficult to assess lifetime and reliability using traditional statistical inference based on a large sample of data from the lifetime test. Therefore, this study contributed to this growing area of research, through a reliability evaluation method based on degradation path distribution related to signal characteristics. In this research, an effective method for reliability assessment was constructed, in which the signal features of the machining process were used to replace traditional time data and fit equipment degradation model. The pseudo failure characteristic (PFC) was obtained according to the failure threshold and the reliability curve was plotted by a PFC distribution model. Experimental investigation on tool reliability assessment was used to verify the effectiveness of this method, in which the trend that tool wear changes with the features was fitted by a Gaussian distribution function and Logarithmic distribution function, to obtain a better tool degradation model. The results illustrated the model could evaluate reliability of mechanical equipment effectively.
Highlights
Reliability assessment for mechanical equipment is important in condition-based maintenance to lower cost and improve equipment reliability, it emerges repeatedly and has become an important research area for mechanical equipment reliability analysis and life prediction [1,2,3]
This paper presented the reliability assessment method of mechanical equipment based on the performance degradation path, which was developed for reliability through exploring the relationship between signal characteristics and performance degradation
The signal characteristic correlated with the degradation of performance was obtained, the estimation method based on the feature-performance model was more practical
Summary
Reliability assessment for mechanical equipment is important in condition-based maintenance to lower cost and improve equipment reliability, it emerges repeatedly and has become an important research area for mechanical equipment reliability analysis and life prediction [1,2,3]. Equipment condition is closely related to a machine’s efficiency and productivity, research on reliability assessment of mechanical equipment is important, based on performance degradation analysis [7]. A novel method for reliability assessment of mechanical equipment based on process signals and degenerate data was developed. Performance degradation data and process signals for mechanical equipment were used fit the degradation path for reliability assessment. This kind of method depended on sizable historical data and sensor signals from related equipment, and an experiment data set of tool wear was used to verify the effectiveness of the method.
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