No abstract

The incipient defect is difficult to be identified by ultrasonic signal analysis. The nonlinear ultrasonic method based on the nonlinear Lamb wave principle is proposed by establishing a nonlinear Lamb wave ultrasonic inspection platform. The optimal Lamb wave parameters are obtained for the incipient fatigue material defects. The aluminum alloy board with 3 mm thickness under the different fatigue tensile cycles is tested. The nonlinear ultrasonic signals are analyzed to obtain second harmonic signals. The intelligent diagnosis method for incipient material degrade is proposed based on the Sequential Probability Ratio Test (SPRT). The sequential probabilistic ratio test (SPRT) algorithm is carried out to classify and identify the second harmonics of four different fatigue damages. The results show that the method about with nonlinear Lamb wave analysis with SPRT is effective and reliable for the incipient material microdefect degradation.

The fault diagnosis model for nonstationary mechanical system is proposed in the condition monitoring. The algorithm with an improved particle filter and Back Propagation for intelligent fault identification is developed, which is used to reduce the noise of the experimental vibration signals to delete the negative effect of the noise on the feature extraction of the original vibration signal. The proposed integrated method is applied for the trouble shoot of the impellers inside the centrifugal pump. The principal component analysis (PCA) method optimizes the clean vibration signal to choose the optimal eigenvalue features.The constructed BP neural network is trained to get the condition models for fault identification. The proposed novel model is compared with the BP neural network based on traditional PF and particle swarm optimization particle filter (PSO-PF) algorithm. The BP neural network diagnosis method based on the improved PF algorithm is much better for the integrity assessment of the centrifugal pump impeller. This method is much significant for big data mining in the fault diagnosis method of the complex mechanical system.