A new feature extraction approach using improved symbolic aggregate approximation for machinery intelligent diagnosis

Abstract

Feature extraction from vibration signals is considerably significant for condition monitoring and fault diagnosis. The Symbolic Aggregate approXimation (SAX) technique essentially transforming a real-valued time series into a symbol sequence, has been proven as a potential tool of feature extraction for machinery intelligent diagnosis. However, challenge still exists that the SAX cannot fulfill feature extraction tasks well since it is carried out only on the basis of mean value in time domain. To overcome this limitation, an improved SAX (ISAX) is proposed in this paper. This new method substitutes the feature of mean value in time domain with multiple features extracted from time, frequency and time-frequency domains in order to obtain comprehensive fault information. With the ISAX transformation, a vibration signal can be transformed into various symbol sequences according to the multi-domain features. Next the Shannon entropy technique is conducted on a symbol sequence to capture sequential patterns in local signals and then the Shannon entropy value is used as the eigenvalue of the symbol sequence. Various eigenvalues are obtained to describe a vibration signal from different perspectives, which leads to a better feature extraction. These eigenvalues are then fed into the Kernel Principal Component Analysis (KPCA) to reduce dimensions and extract principal features for classification tasks. Compared with SAX, the most significant advantage of ISAX is extracting comprehensive signal characteristics from multi-domain. Moreover, the ISAX captures fault information better considering the local fault patterns. The effectiveness and superiority of ISAX were validated by experimental studies using the fault signals of rolling bearings and reciprocating compressor valves with remarkably high classification rates.