Intelligent fault diagnosis of rotating machinery using improved multiscale dispersion entropy and mRMR feature selection

Intelligent fault diagnosis of rotating machinery is essentially a pattern recognition problem. Meanwhile, effective feature extraction from the raw vibration signal is an important procedure for timely detection of mechanical health status and the assessment of fault recognition results. Therefore, to efficiently extract fault feature information and improve fault diagnosis accuracy, a novel fault diagnosis technique based on improved multiscale dispersion entropy (IMDE) and max-relevance min-redundancy (mRMR) is proposed in this paper. Firstly, the IMDE method is developed to capture multi-scale fault features from the collected original vibration signal, which can overcome the deficiencies of traditional multiscale entropy and improve the stability of the recently presented multiscale dispersion entropy (MDE). Then, the mRMR algorithm is utilized to select automatically the sensitive features from the candidate multi-scale features without any prior knowledge. Finally, the sensitive feature vector set after normalization treatment is inputted into the extreme learning machine (ELM) classifier to train the intelligent diagnosis model and provide fault diagnosis results. The validity of our proposed method is assessed through two experimental examples. The experimental results show that our proposed method works efficiently for identification of different fault conditions of mechanical components including rolling bearing and gearbox. Moreover, our proposed method gives better diagnosis results as compared to some existing approaches (e.g. MSE and MPE) when being utilized for fault condition classification. This research provides a new perspective for fault information extraction and fault classification of rotating machinery.