Abstract
In order to improve the fault detection accuracy for rolling bearings, an automated fault diagnosis system is presented based on lifting wavelet packet transform (LWPT), sample entropy (SampEn), and classifier ensemble. Bearing vibration signals are firstly decomposed into different frequency subbands through a three-level LWPT, resulting in a total of 8 frequency-band signals throughout the third layers of the LWPT decomposition tree. The SampEns of all the 8 components are then calculated as feature vectors. Such a feature extraction paradigm is expected to depict complexity, irregularity, and nonstationarity of bearing vibrations. Moreover, a novel classifier ensemble is proposed to alleviate the effect of initial parameters on the performance of member classifiers and to improve classification effectiveness. Experiments were conducted on electric motor bearings considering various set of fault categories and fault severity levels. Experimental results demonstrate the proposed diagnosis system can effectively improve bearing fault recognition accuracy and stability in comparison with diagnosis methods based on a single classifier.
Highlights
Rolling element bearings are among the most critical components in various machines, and their faults are the main causes of breakdowns in rotating machinery
The current paper presents an intelligent diagnosis method for rolling bearings by integrating the lifting wavelet packet transform (LWPT), sample entropy (SampEn), and
The distinct merits of the diagnosis method lie in the feature extraction methods combining the LWPT with the SampEn as well as the recognition methods by binary tree system based classifier ensemble
Summary
Rolling element bearings are among the most critical components in various machines, and their faults are the main causes of breakdowns in rotating machinery. It was reported that rolling bearing faults accommodate 45–55% of asynchronous motor failures. A variety of fault diagnosis methods have been developed and exploited effectively to detect bearing faults at an early stage for the purpose of keeping machinery performing at its best and avoid unplanned downtime and economical loss. Vibrations emitted from industry machinery like asynchronous motors usually contain signatures of multiple resources and are affected by operation parameters including speed and load. Bearing fault diagnosis is not a trivial task in terms of signal processing and fault identification. As an antecedent step of machine prognostics and health management (PHM), it needs to find the faulty bearings and locate faulty components, as different fault location follows different fault development mode. The objective of the present work is to identify bearing health condition and locate faulty bearing components with emphases on feature extraction and faulty component recognition
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