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.

The spatial information of the signal is neglected by the conventional frequency/time decompositions such as the fast Fourier transformation, principal component analysis, and independent component analysis. Framing of the data being as a three-way array indexed by channel, frequency, and time allows the application of parallel factor analysis, which is known as a unique multi-way decomposition. The parallel factor analysis was used to decompose the wavelet transformed ongoing diagnostic channel–frequency–time signal and each atom is trilinearly decomposed into spatial, spectral, and temporal signature. The time–frequency–space characteristics of the single-source fault signal was extracted from the multi-source dynamic feature recognition of mechanical nonlinear multi-failure mode and the corresponding relationship between the nonlinear variable, multi-fault mode, and multi-source fault features in time, frequency, and space was obtained. In this article, a new method for the multi-fault condition monitoring of slurry pump based on parallel factor analysis and continuous wavelet transform was developed to meet the requirements of automatic monitoring and fault diagnosis of industrial process production lines. The multi-scale parallel factorization theory was studied and a three-dimensional time–frequency–space model reconstruction algorithm for multi-source feature factors that improves the accuracy of mechanical fault detection and intelligent levels was proposed.

The monitoring of mechanical equipment systems contains an increasing number of complex content, expanding from traditional time, and frequency information to three-dimensional data of the time, space, and frequency information, and even higher-dimensional data containing subjects, experimental conditions. For high-dimensional data analysis, traditional decomposition methods such as Hilbert transform, fast Fourier transformation, and Gabor transformation not only lose the integrity of the data, but also increase the amount of calculation and introduce a lot of redundant information. The phenomenon of feature coupling, aliasing, and redundancy between the mechanical multi-source data signals will cause the inaccuracy of the evaluation, diagnosis, and prediction of industrial production operation status. The analysis of the three-way tensor composed of channel, frequency, and time is called parallel factor analysis (PARAFAC). The properties between the parallel factor analysis results and the input signals are studied through simulation experiments. Parallel factor analysis is used to decompose the third-order tensor composed of channel-time-frequency after continuous wavelet transformation of vibration signal into channel, time, and frequency characteristics. Multi-scale parallel factor analysis successfully extracted non-linear multi-dimensional dynamic fault characteristics by generating the spatial, spectral, time-domain signal loading value and three-dimensional fault characteristic expression. In order to verify the effectiveness of the space, frequency, and time domain signal loading values of the fault characteristic factors generated by the centrifugal pump system after parallel factor analysis, the characteristic factors obtained after parallel factor analysis are used as the SPRT test sequence for identification and verification. The results indicate that the method proposed in this article improves the measurement accuracy and intelligence of mechanical fault detection.

Polyethylene pipes reinforced by winding steel wires (PSP) have been widely used in many fields such as chemical engineering, pulp conveying, water supply, etc. The combined loads of inner pressure and bending sometimes leads to the failure of PSP, and for engineering projects it is still not proposed that the failure criterion of PSP subject to combined loads. In this paper, full-size finite element models (FEM) of PSP under inner pressure and bending were established to investigate the engineering failure criterion. In the FEM the steel wires and HDPE matrix were modeled separately. The freedom degrees of steel wires and HDPE were coupled together as the interface between these two constituents were considered intact. The investigation contains two parts: firstly, a FEM was established in reference to the details of an existing experiment, including the pipe specimen and relative boundary condition. The validation of the FEM was carried out and the simulation result agreed well with test result indeed. Subsequently, the model was optimized to undertake four-point bending under inner pressure, to analyze the failure behavior of PSP under this kind of condition, with three factors such as varied ratio of diameters to thickness, inner pressure and volume ratio of steel wires. In the end, the curvature of failed PSP was considered as the failure criterion, and the relationship of curvature and the three factors were discussed. This paper is useful for the safety of PSP subject to inner pressure and bending load.