Research of Mechanical Vibration Signal Classification Based on LMD

The traditional signal processing methods are difficult to accurately extract fault information, because mechanical fault vibration signals have non-stationary, which will cause system instability. Local mean decomposition is adaptive signal processing method. However, in the local mean decomposition of the signal, the trend of the endpoint can not be predicted which cause contaminating the entire signal sequence, the original moving average of the signal used over-smoothing treatment, resulting in fault characteristics can not accurately extract. The article introduces waveform matching to solve the original features of signals at the endpoints, using linear interpolation to get local mean and envelope function, then obtain production function PF vector through making use of the local mean decomposition. The energy entropy of PF vector take as identification input vectors. These vectors are respectively inputted BP neural networks, support vector machines, least squares support vector machines to identify faults. Experimental result show that the accuracy of least squares support vector machine with higher classification accuracy has been improved.