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In the fault diagnosis of rolling bearing, the vibration signals, which are collected from the field test, are often more complex because they unavoidably contain various noises and measurement errors, so ‘outliers’ may occur in the features extracted from the collected vibration signals. Aiming at the above problems, the agent discriminate model based optimization weighted (ADMOW) method is proposed. By using the entropy weight method (EWM), the entropy weights of the sample features are calculated first, and the features are then weighted to weaken the influence of ‘outliers’ on the modeling. Secondly, the particle swarm optimization (PSO) algorithm is used to optimize the parameters of the model, and a more accurate classification model is obtained. Eventually, ADMOW is applied to recognize defaults of rolling bearings. The test results indicate that by comparing several pattern recognition methods, the proposed method can effectively weaken the influence of ‘outliers’ and improve the recognition rate and the recognition accuracy.

This paper presents a new method which combines empirical mode decomposition (EMD) and power spectral density (PSD) together for bearing fault diagnosis in low speed-high load rotary machine. EMD is a novel self-adaptive method which is based on partial characters of the signal. Vibration signal measured from a defective rolling bearing is decomposed into a number of intrinsic mode functions (IMFs), with each IMF corresponding to a specific range of frequency components contained within the vibration signal. Then calculate the PSD of each IMF. The results of application in simulation signal and practical bearing fault signal both show its efficiency. Index Terms -mode decomposition, intrinsic mode function, power spectral density, fault diagnosis, roller bearing.

Mathematical morphology (MM) is a very effective signal processing tool which has been widely applied in 2-dimentional signal processing such as image processing. But there is little application in mechanic signals. Empirical mode decomposition (EMD) is a novel self-adaptive method which is based on partial characters of the signal. This paper firstly proves the efficiency of MM in 1-dimentional signal by illustrating an example of a simulation signal. Then in practical application, it combines MM and EMD together to diagnosis bearing fault. First de-noise the practical bearing fault signal by MM filter, and then decompose the de-noised signal into several IMFs by EMD. Finally calculate the power spectral density of each IMF. The result indicates the efficiency of this method.