An approach to performance assessment and fault diagnosis for rotating machinery equipment

Xiaochuang Tao,Zili Wang,Chen Lu,Chuan Lu

doi:10.1186/1687-6180-2013-5

Xiaochuang Tao, Zili Wang + Show 2 more

Open Access

https://doi.org/10.1186/1687-6180-2013-5

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Abstract

Abstract Predict and prevent maintenance is routinely carried out. However, how to address the problem of performance assessment maximizing the use of available monitoring data, and how to build a framework that integrates performance assessment, fault detection, and diagnosis are still a significant challenge. For this purpose, this article introduces an approach to performance assessment and fault diagnosis for rotating machinery, including wavelet packet decomposition for extracting energy feature samples from vibration signals acquired during normal and faulty conditions; clustering analysis for demonstrating the separability of the samples; and Fisher discriminant analysis for providing an optimal lower-dimensional representation, in terms of maximizing the separability among different populations, by projecting the samples into a new space. In the new low-dimensional space, the Mahalanobis distance (MD) between the new measurement data and normal population can be calculated for performance assessment. Moreover, this model for performance assessment only requires data to be available in normal conditions and any one of all possible fault conditions, without the necessity for the full life cycle of condition monitoring data. In addition, if monitoring data under different fault conditions are available, the fault mode can be identified accurately by comparing the MDs between the new measurement data and each fault population. Finally, the proposed method was verified to be successful on performance assessment and fault diagnosis via a hydraulic pump test and a ball bearing test.

Highlights

Driven by the demand to reduce maintenance costs, shorten repair time, and maintain high availability of equipment, maintenance strategies have progressed from breakdown maintenance to preventive maintenance, to condition-based maintenance (CBM), and lately toward a prospect of intelligent predictive maintenance, [1,2,3]
This article uses the combination of Fisher discriminant analysis (FDA) and Mahalanobis distance (MD) applied to rotating machinery fault diagnosis, which is further extended to performance assessment and fault detection
The analysis demonstrated that the performance assessment could be quantized and visualized by MD and confidence value (CV), and coupled with a presupposed threshold where abnormal states can be detected

Summary

Introduction

Driven by the demand to reduce maintenance costs, shorten repair time, and maintain high availability of equipment, maintenance strategies have progressed from breakdown maintenance (fail and fix) to preventive maintenance, to condition-based maintenance (CBM), and lately toward a prospect of intelligent predictive maintenance (predict and prevent), [1,2,3]. While the reactively breakdown maintenance and blindly preventive maintenance do sometimes reduce equipment failures, they are more labor intensive, do not eliminate catastrophic failures and cause unnecessary maintenance. If an abnormal state is detected by performance assessment, the MDs between the new measurement data and the normal and different fault populations are calculated, to identify which population the new data belong to, and the fault mode can be recognized. The proposed method for performance assessment only needs monitoring data under normal conditions and any one of all possible fault conditions. The proposed method was verified to be effective and pragmatic for performance assessment and fault diagnosis via a hydraulic pump test and a ball bearing test

Wavelet packet decomposition-based feature extraction

Findings

Conclusion