A hybrid approach of symbolic aggregate approximation and bitmap: application to fault diagnosis of reciprocating compressor valve

Abstract

Feature extraction plays an important role in machinery fault diagnosis and prognosis. The features extracted from time, frequency and time-frequency domains are widely investigated to describe the properties of overall signal from different perspectives (e.g. RMS, energy, etc.), seldom considering the sequential pattern of time-series signal in which the fault information may be embedded. This paper contributes a novel approach based on Symbolic Aggregate approXimation (SAX) framework and bitmap technology to extract fault information by analyzing sequential pattern in time-series signal for fault diagnosis. In the proposed method, SAX and bitmap are subtly combined. SAX technique reduces the dimensionality of raw data by transforming the original real valued time series into a discrete one. Fault features are extracted with bitmap representation by a simple histogram form summarizing the occurrence of the chosen symbols words, in which signal timing change character is investigated. Compared with the commonly used methods, the proposed approach has high computation efficiency and feature extraction accuracy. Experimental studies on reciprocating compressor valve demonstrate that the presented approach outperforms the methods of SAX-entropy and EMD-energy-entropy using support vector machine for classification.