Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears.

Rolling element bearings are among the most common components in industrial rotating machinery. Condition monitoring technologies for bearing fault detection and diagnosis are critical to avoid machines from productivity reduction and emergency breakdown. Acoustic emission (AE) technology which detects the expanding of cracks in the fatigued material is able to monitor bearings before visual defects formed in contacting surfaces. This advantage may help the AE method identify the fault before the commonly used vibration method does. In this paper, the effectiveness of AE method on bearing diagnosis is compared with vibration method. The two methods were tested on the same bearing fault under three rotary speeds, and two kinds of signals were both processed by envelope analysis method. The results show that the quality of acoustic emission signal decreases more significantly than vibration signal when rotary speed slows down, and the attenuation of high frequency component of AE signal should be specially addressed before applying to industrial equipment.

According to the rolling bearing local fault vibration mechanism, a monopulse feature extraction and fault diagnosis method of rolling bearing under low‐speed and heavy‐load conditions based on phase scan and CNN is proposed. The synchronous collected speed signal is used to calculate bearing phase function and divide fault monopulse periods. The monopulse waveforms of multiple fault periods are scanned and ensemble averaged to suppress noise interference and detail feature loss at the same time of feature extraction. By iteratively calibrating phase function, the feature matrix containing bearing fault information can be obtained. Finally, CNN is used to recognize and classify different bearing states. The experimental and analysis results show that bearing fault diagnosis can be achieved. The total recognition rates of constant and variable speed samples are 99.67% and 99.89%, respectively. The trained network has fast convergence speed and good generalization ability for different fault sizes and working conditions. Further experiments show that the method can also accurately identify different bearing degradation states. The total recognition rates of constant and variable speed samples are 96.67% and 95.56%, respectively. The limited errors are concentrated between the degradation states with the same type weak fault. The experimental results using Case Western Reserve University bearing data show that feature extraction and network training are better, and the recognition rates of 5 bearing states are all 100%. Therefore, the proposed method is an effective rolling bearing feature extraction and fault diagnosis technology.