A Method Combining Order Tracking and Fuzzy C-Means for Diesel Engine Fault Detection and Isolation

• Published in: Shock and vibration Vol. 2015; no. 2015; pp. 1 - 7
• Main Authors: Jia, Jide, Zhou, Bin, Mei, Jianmin, Xiao, Yunkui, Zhang, Lingling, Zeng, Ruili, Zhao, Huimin
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2015; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Accuracy; Classification; Computation; Diesel engines; Diesel motor; Fault location (Engineering); Faults; Fourier transforms; Fuzzy; Fuzzy algorithms; Fuzzy logic; Fuzzy set theory; Fuzzy systems; Mathematical analysis; Mechanical properties; Methods; Pattern recognition; Personal computers; Signal processing; Testing; Tracking; Vectors (mathematics); Wavelet transforms; Working conditions
• ISSN: 1070-9622; 1875-9203
• DOI: 10.1155/2015/547238

Diesel engine works under variable speed conditions; fault symptoms are clearer in the angular/order domains than in the common time/frequency ones. In this paper, firstly, the acceleration signal of diesel engine is resampled by order tracking, in which the rotating speed is computed in every working cycle, and the order tracking spectrum is created in each interval’s speed; then different order band accumulated energy is computed as feature vector. After standardizing these features, the fuzzy c-means (FCM) is introduced to use them as input vector; the optimized classified matrix and clustering centers can be obtained using FCM iteration method; then the fault can be detected by calculating the approach degree between the unknown samples and the known ones. To validate the method, some experiments have been performed; the results show that the signal can be reconstructed, and the features of order band accumulated energy can reflect the information of different wear conditions in crank-shaft bearing; then the fault can be detected accurately. The method of nonentire work cycle is also introduced as a comparison with our method; the result shows our method has more accuracy classification.