Abstract
This paper presents a comprehensive multifault diagnosis methodology for incipient rolling element bearing failures. This is done by combining a wavelet packet transform- (WPT-) based kurtogram and a new vector median-based feature analysis technique. The proposed approach first extracts useful features that are characteristic of the bearing health condition from the time domain, frequency domain, and envelope power spectrum of incoming acoustic emission (AE) signals by using a WPT-based kurtogram. Then, an enhanced feature analysis approach based on the linear discriminant analysis (LDA) technique is used to select the most discriminant bearing fault features from the original feature set. These selected fault features are used by a Naïve Bayes (NB) classifier to classify the bearing fault conditions. The performance of the proposed methodology is tested and validated under various bearing fault conditions on an experimental test rig and compared with conventional state-of-the-art approaches. The proposed bearing fault diagnosis methodology yields average classification accuracies of 91.11%, 96.67%, 98.89%, 99.44%, and 98.61% at rotational speeds of 300, 350, 400, 450, and 500 rpm, respectively.
Highlights
For the past several decades, the development of reliable fault diagnosis systems to accurately detect and classify various bearing faults has been at the heart of research in the field of machine condition monitoring for preventive and predictive maintenance
We propose a feature analysis method based on the linear discriminant analysis (LDA) technique plus a vector median-based discriminant criterion (VMDC) to find the most discriminative subset of the extracted fault features for accurate fault diagnosis
This paper proposed a comprehensive multifault diagnosis methodology based on acoustic emission (AE) analysis to detect multiple localized bearing faults of a rolling element bearing
Summary
For the past several decades, the development of reliable fault diagnosis systems to accurately detect and classify various bearing faults has been at the heart of research in the field of machine condition monitoring for preventive and predictive maintenance. In attempts to enhance the performance of the kurtogram method for fault diagnosis, many researchers have integrated the kurtogram either with the short-time Fourier transform (STFT) [6] or with multirate filter banks (MRFB) [1] These approaches, have yielded little improvement in extracting transient characteristics and have rendered kurtogram analysis more sensitive to irrelevant impulsive components [7]. This paper presents a new LDA-based feature analysis method that uses a vector median-based discriminant criterion (VMDC) to characterize the intraclass compactness and interclass separability of the feature space in order to select the most discriminative subset of bearing fault features. This paper proposes a reliable multifault diagnosis scheme for rolling element bearings that combines an improved WPT-based kurtogram and the VMDC-based feature analysis methods.
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