Print Email Facebook Twitter Planetary gears feature extraction and fault diagnosis method based on VMD and CNN Title Planetary gears feature extraction and fault diagnosis method based on VMD and CNN Author Liu, C. (China University of Mining and Technology) Cheng, Gang (China University of Mining and Technology) Chen, Xihui (Hohai University) Pang, Y. (TU Delft Transport Engineering and Logistics) Date 2018 Abstract 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. Subject CNNDegradationFeature extractionPartitionPlanetary gearSVDVMD To reference this document use: http://resolver.tudelft.nl/uuid:ed3df416-e469-4ebc-b2e3-f4ac2c3e4788 DOI https://doi.org/10.3390/s18051523 ISSN 1424-8220 Source Sensors, 18 (5) Part of collection Institutional Repository Document type journal article Rights © 2018 C. Liu, Gang Cheng, Xihui Chen, Y. Pang Files PDF sensors_18_01523.pdf 4.03 MB Close viewer /islandora/object/uuid:ed3df416-e469-4ebc-b2e3-f4ac2c3e4788/datastream/OBJ/view