Abstract
Numerous studies on fault diagnosis have been conducted in recent years because the timely and correct detection of machine fault effectively minimizes the damage resulting in the unexpected breakdown of machineries. The mathematical morphological analysis has been performed to denoise raw signal. However, the improper choice of the length of the structure element (SE) will substantially influence the effectiveness of fault feature extraction. Moreover, the classification of fault type is a significant step in intelligent fault diagnosis, and many techniques have already been developed, such as support vector machine (SVM). This study proposes an intelligent fault diagnosis strategy that combines the extraction of morphological feature and support vector regression (SVR) classifier. The vibration signal is first processed using various scales of morphological analysis, where the length of SE is determined adaptively. Thereafter, nine statistical features are extracted from the processed signal. Lastly, an SVR classifier is used to identify the health condition of the machinery. The effectiveness of the proposed scheme is validated using the data set from a bearing test rig. Results show the high accuracy of the proposed method despite the influence of noise.
Highlights
Given the rapid development of industrial technology, numerous multifunctional machineries have been employed to replace humans, in dangerous environments
This study presents a novel intelligent machinery fault diagnosis method
This method includes the extraction of morphological features and support vector regression (SVR) classifier
Summary
Given the rapid development of industrial technology, numerous multifunctional machineries have been employed to replace humans, in dangerous environments. The demand to inspect the health condition of these crucial components is increasing, and an efficient and intelligent machine fault diagnosis method should be developed to improve the reliability, safety, and effectiveness of operating systems [3, 4]. Extensive studies have been conducted in recent years to improve the effectiveness of fault diagnosis methods. Morphological analysis has been extensively performed to evaluate the satisfactory performance of signal processing in noise reduction This process is an originally developed nonlinear method that uses structure elements (SEs) to measure and extract the corresponding shape of a given image [11]. The results demonstrated that the proposed method can extract the influential characteristics of axle box vibration signals and effectively diagnose real-time wheel flat faults.
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