Engineering

Machinery Prognostics and Prognosis Oriented Maintenance by Jihong Yan

By Jihong Yan

This publication supplies a whole presentatin of the fundamental necessities of equipment prognostics and diagnosis orientated upkeep administration, and takes a glance on the state of the art self-discipline of clever failure diagnosis applied sciences for condition-based upkeep. most recent learn effects and alertness tools are brought for sign processing, reliability moelling, deterioration overview, residual lifestyles prediction and maintenance-optimization in addition to functions of those equipment.

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38 Machinery Prognostics and Prognosis Oriented Maintenance Management functions with different levels. This ability renders the analysis of the inspected signal easier for machine operators. Extensive research has been conducted on applying wavelet transform in machine fault diagnosis [4–7], and the recent advances of wavelet transform analysis have derived a new set of techniques of time–frequency analysis that have been shown to be powerful in the field of transient signal processing, such as wavelet coefficients analysis [8], wavelet ridge method [9], undecimated discrete wavelet transform (DWT) [10], and so on.

With the advances in digital signal processing methods, there has been an increasingly strong interest in the application of signal analysis techniques for the detection of fault features of machinery elements [2]. Owing to the advantage of direct association with the rotating characteristics, spectral analysis has been the most used signal processing tool in vibration analysis of rotating machinery [3]. The primary advantage of time domain methods is that they use directly the measured vibration signal in signal processing.

10. N. J. (1999) Data clustering: a review. ACM Computing Surveys, 31 (3), 264–323. 11. , Regenberg, B. et al. (2006) Robust multi-scale clustering of large DNA microarray datasets with the consensus algorithm. Bioinformatics, 22 (1), 58–67. 12. K. (2010) Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31 (8), 651–666. 13. , Xiao, L. et al. (2013) A sample-based hierarchical adaptive K-means clustering method for large-scale video retrieval. Knowledge-Based Systems, 49, 123–133.

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