Nonparametric Wavelet-based Multivariate Control Chart for Rotating Machinery Condition Monitoring  

Oleh:

Jung, Uk

Jenis: Article from Proceeding
Dalam koleksi: 12th ANQ Congress in Singapore, 5-8 Agustus 2014, page 1-15.
Topik: Multivariate Control Chart; Fault Detection; Wavelet Transformation; Bootstraping Technique; Vibration Signal
Fulltext: QP2-5.2-P0178.pdf (873.56KB)

Isi artikel

As one of the most critical categories of industrial equipment, rotating machinery requires a condition monitoring technique for early warning towards the occurrence of fault before it develops to failure and breakdown. Apparently, reliable monitoring and evaluation techniques are responsible for providing prompt decision-making to the operator regarding the operation of rotating machinery based on thorough evaluation of available data in timely fashion. In this context, signal processing of vibration signals from rotating machinery has been an active research area for recent years. Especially, discrete wavelet transform (DWT) is considered as a powerful tool for feature extraction in detecting fault on rotating machinery. However, the number of retained DWT features can be still too large to be used for standard multivariate statistical process control (SPC) techniques although DWT significantly reduces the dimensionality of the data. Even though many feature-based SPC methods have been introduced to tackle this deficiency, most of methods require a parametric distributional assumption that restricts their feasibility to specific problem of process control and thus limits their applications. This study introduced new feature extraction technique to alleviate the high dimensionality problem of implementing multivariate SPC when the quality characteristic is a vibration signal from bearing system. A set of multi-scale wavelet scalogram features was generated to reduce the dimensionality of data, and is combined with the bootstrapping technique as nonparametric density estimation to set up an upper control limit of control chart. Our example and numerical simulation of a bearing system demonstrated that the proposed method has satisfactory fault-discriminating ability without any distributional assumption

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