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This article proposes a novel bearing fault detection framework for the real-time condition monitoring of induction motors based on difference visibility graph (DVG) theory. In this regard, the vibration signals of healthy as well as different rolling bearing defects were acquired from both fan-end and drive-end accelerometers. These data were recorded for three different bearing defects and under four loading conditions. The acquired vibration time series were converted to a topological network using DVG. From the transformed vibration data in the graph domain, degree distribution (DD) was selected as feature to discriminate different fault networks. Using analysis of variance test and false discovery rate correction, most discriminative DD features were selected. These features were subsequently fed as inputs to a deep learning model, i.e., a bidirectional long short-term memory network classifier for fault classification. In this study, 112 classification problems have been addressed, and for all of them, the proposed approach delivered very high fault detection accuracy. Finally, the classification performance of the proposed framework is compared with other well-known deep-learning classifiers all of which delivered satisfactory results. [ABSTRACT FROM AUTHOR]

Rolling bearing defects in induction motors are usually diagnosed using vibration signal analysis. For accurate detection of rolling bearing defects, appropriate feature extraction from vibration signals is necessary, failure of which may lead to incorrect interpretation. Considering the above fact, this article presents an autocorrelation aided feature extraction method for diagnosis of rolling bearing defects. To this end, the vibration signals of healthy as well as different faulty bearings were recorded using accelerometers and autocorrelation of the respective vibration signals were done to examine their self-similarity in time scale. Following this, several statistical, hjorth as well as non-linear features were extracted from the respective vibration correlograms and were subjected to feature reduction using recursive feature elimination technique. The dimensionally reduced top ranked feature vectors were subsequently fed to a random forest classifier for classification of vibration signals. A large number of experiments were carried out for (i) three different fault diameters at (ii) four different shaft speeds and also at (iii) two different sampling frequencies. Besides, for each condition, six binary class and one multiclass classification problem is also addressed in this paper, resulting in a total 112 different classification tasks. It was observed that the proposed method has yielded very high accuracy in identifying different bearing defects which can be practically implemented for automated bearing fault detection of induction motors. [ABSTRACT FROM AUTHOR]