Published: 30 September 2012

Machine performance assessment based on integrated signal redundancy and bootstrap technique

Guangrui Wen1
Xiaoni Dong2
Yuhe Liao3
Tingting Wu4
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Abstract

Prediction of machine performance based on current states and historical data has been a challenging issue in a predictive maintenance of machine performance assessment. Traditional methods mainly focused on developing prediction algorithms, rather than paying attention to the understanding of the data. This paper presents an innovative method to quantitatively evaluate the predictability of machinery performance assessment based on information redundancy and a statistical simulation technique. The predictability of a series of simulated signals including periodicity signal, simulated periodicity signal, chaos signal and random white noise signal were simulated for testing the correctness of the proposed method. In addition, practical vibration data were analyzed and a high-precision prediction was achieved by computing the redundancies of these sample sequences. Results indicate that evaluation tool can present a clear indication of machine performance predictability and therefore can guide the development and selection of prediction algorithms.

About this article

Received
12 May 2012
Accepted
04 September 2012
Published
30 September 2012
Keywords
prognostics
preventive maintenance
predictability
signal redundancy
bootstrap