Fault Diagnosis Based on Weighted Extreme Learning Machine With Wavelet Packet Decomposition and KPCA

• Published in: IEEE sensors journal Vol. 18; no. 20; pp. 8472 - 8483
• Main Authors: Hu, Qin, Qin, Aisong, Zhang, Qinghua, He, Jun, Sun, Guoxi
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.10.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Back propagation networks; Classification; Decomposition; Downtime; Fault detection; Fault diagnosis; Feature extraction; kernel principal component analysis; Learning; Machinery; multiple dimensionless parameters; Neural networks; Principal components analysis; Rotating machinery; Support vector machines; Time-domain analysis; Time-frequency analysis; Vibrations; Wavelet analysis; wavelet packet decomposition; Wavelet packets; Weight; Weighted extreme learning machine
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2018.2866708

Fault diagnosis has received considerable attention because its implementation can effectively prevent costly and even catastrophic downtime. However, quickly identifying faults and accurately obtaining diagnosis results from a feature set of rotating machinery are still a problem. To this end, this paper proposes an effective method based on a weighted extreme learning machine (WELM) with wavelet packet decomposition (WPD) and kernel principal component analysis (KPCA). The feature set affecting classification accuracy can be obtained using WPD and KPCA. By taking feature reliability into consideration, a new type of improvement to the extreme learning machine (ELM), i.e., WELM, is proposed by associating the hidden layer and input layer with a weight matrix. The WELM model can help in guaranteeing a quick and an accurate identification of fault status. To verify the superiority of the fault identification speed and accuracy of the proposed method, results from other methods, namely, using the sensitive features based on WPD and KPCA with ELM, a back-propagation neural network, and a support vector machine, were compared. The experimental results indicate that the proposed method can effectively improve the accuracy and quickly diagnose the fault. The average accuracy of fault classification could reach 95.45%, and the computation time of WELM was only 0.0156 s.