Abstract:

Gear transmission system is one of most important key equipment to guarantee safe and stable operation in locomotive. With time variation, unpredictability and dynamic linkage, the problem of fault feature acquisition still be misdiagnosis by traditional fault diagnosis method. Sparse blind source separation is a kind of soft computing method based on orthogonal basis mapping to effectively separate multiple nonlinear signals under signals transmission channel unknown. However, gear fault data in actual working status are weak and uncertainty, thus causing source signal characteristics cannot accurate diagnosis fault after sparse separation. Therefore, a method of adaptive time-varying blind separation based on variable metric empirical mode decomposition (VMEMD) is presented, which is using sparseness and iterative screening to separate fault source and obtain optimal intrinsic mode function by adjusting time span. Redundancy factors are deleted and fault recognition rate is improved. The analysis through simulation experiments shows that fault feature can be obtained quickly and correctly on low signal to noise ratio which provides key technology for state detection and fault diagnosis of railway transportation.

Key words: fault diagnosis, time-varying blind source separation, gear transmission system