Under variable working conditions, a problem arises, which is that it is difficult to obtain enough labeled data; to address this problem, an adaptive transfer autoencoder (ATAE) is established to diagnose faults in rotating machinery. First, a data adaptation module, which calculates the maximum mean discrepancy for the network hidden-layer data in reproducing kernel Hilbert space, is introduced to the autoencoder network, thus making the classification model operate under variable working conditions. Variation particle-swarm optimization is then invoked to optimize the data adaptation parameters. Finally, the k-nearest neighbors algorithm, as the classification layer of the network, identifies the state of health of the rotating machinery. The capabilities of the intelligent fault-diagnosis network are verified using vibration signals from a bearing test rig and a gearbox test rig. The experimental results suggest that, compared with state-of-the-art diagnosis methods, the proposed ATAE network can significantly boost diagnostic performance in the absence of target vibration signal labels.

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Lamb waves were utilized to quantify micro-crack damage in aluminum plates, and the scattering and mode conversion of Lamb waves passing through cracks were analyzed. The dynamic time warping (DWT) method was used to match and compare each Lamb wave time series that represented different damage degrees. The matching difference between the damaged plate and undamaged plate was taken as a marker to measure the damage degree of the workpiece. At the same time, due to the pathological alignment of traditional DTW methods, the shape context (SC) profile recognition method was introduced to optimize the algorithm for calculating the distance between sampling points in the DTW method and solve the pathological alignment problem. Finally, the SC-DTW method based on Lamb waves was verified by the finite element simulation model and bending test of aluminum plates. The results showed that the method was feasible for quantifying the damage degree of aluminum plates and had a great advantage in the analysis and processing of time series in low-sampling frequency and high-noise scenarios.

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