A three-dimensional geometric features-based SCA algorithm for compound faults diagnosis

To achieve compound faults diagnosis with single channel signal, a three-dimensional geometric features-based sparse component analysis (TGF-SCA) method is proposed. Intrinsic characteristic-scale decomposition (ICD) is used to decompose the single channel of mixed signal into three channels. Then, the three-dimensional potential function (TPF) is constructed based on the three-dimensional geometric features to estimate matrix. In addition, an energy factor (EF) is introduced to improve the computational efficiency in the process. Ultimately, the minimal l(1) norm algorithm is used to obtain the separated signal based on the estimated matrix. Experimental analysis results for roller bearing show that the fault feature frequencies of bearings acquired using the proposed approach are evidently close to the theoretical values. For example, when the rotating speed is 900 rpm, the feature frequency 60.27 Hz is very similar to the theoretical calculation of ball pass frequency of the outer race (BPFO) 60.5 Hz and the feature frequency 74.01 Hz is close to the theoretical calculation of the ball pass frequency of the roller (BPFR) 74.4 Hz. Compared with the ICA method, the SCA method based on Fuzzy C-means algorithm (FCM) and the SCA method based on K-means algorithm, the experimental verification results indicate that the TGF-SCA method can separate the source signal, extract the fault features and realize compound faults diagnosis for roller bearing. (C) 2018 Elsevier Ltd. All rights reserved.