Traditional bearing fault diagnosis mainly based on periodic feature extraction from bearing fault pulses. However, the diagnostic accuracy was limited due to the absence of rotation speed information. Firstly, through envelope analysis and order tracking to extract roller passing frequency (RPF) and fault order, this paper verifies the performance of vibration and acoustic emission signals (AE) on diagnosis of bearing fault in low-speed. Secondly, the advantages of fault source localization of AE are also verified and analyzed. In order to accurately extract the pulse generated by the fault source, a method for AE event extraction using short time autocorrelation function is proposed, which is verified to be efficient and reliable under different working conditions of low-speed bearings.

Aiming at the problem that a single neural network model has difficulty in accurately predicting trends of the remaining useful life of a rolling bearing, a method of predicting the remaining useful life of rolling bearings using a gated recurrent unit-deep autoregressive model (GRU-DeepAR) with an adaptive failure threshold was proposed. First, time domain and frequency domain features were extracted from the rolling bearing vibration signal. Second, its operation process was divided into a smooth operation stage and degradation stage according to the trend of the accumulated root mean square of maximum. Then, the failure threshold for different bearings were determined adaptively by the maximum of the smooth operation data. The degradation dataset of a rolling bearing was subsequently obtained. In the meantime, a GRU-DeepAR model was built to obtain predictions of the failure time and failure probability. Appropriate model parameters were determined after a large number of tests to assure the effectiveness and prediction accuracy. Finally, the trend of time series and failure times were predicted by inputting the degradation dataset into the GRU-DeepAR model. Experiments showed that the proposed method can effectively improve the accuracy of the remaining useful life prediction of a rolling bearing with good stability.

Displacement measurement is an essential method for structural safety assessment and health monitoring, and the static and dynamic characteristics of the structure can be obtained through displacement. In order to overcome the limitations of sensors in vibration measurement of large structures, as well as the poor adaptability of visual measurement algorithms such as machine learning and digital image processing, this paper takes the bridge structure as the research object and introduces deep learning into the field of visual vibration measurement. Moreover, based on the deep convolutional neural network, a new high-precision displacement measurement algorithm of multi-scale feature extraction and fusion is proposed to solve the inaccurate measurement of existing neural networks. Experiments are carried out on bridge models in the laboratory environment and bridges in the real world to verify the adaptability and reliability of the proposed method. At the same time, the time-frequency characteristic curves of different deep learning models, template matching algorithms, and acceleration sensors are compared. The result analysis shows that the vibration displacement trajectory of the algorithm in this paper has the best coincidence with the standard displacement signal. Three experiments have fully verified that the algorithm in this paper has good application potential and implementation space in the field of structural state monitoring.

In light of the problems of a single vibration feature containing limited information on the degradation of rolling bearings, the redundant information in high-dimensional feature sets inaccurately reflecting the reliability of rolling bearings in service, and assessments of the degradation performance being disturbed by outliers and false fluctuations in the signal, this study proposes a method of assessing rolling bearings’ performance in terms of degradation using adaptive sensitive feature selection and multi-strategy optimized support vector data description (SVDD). First, a high-dimensional feature set of vibration signals from rolling bearings was extracted. Second, a method combining the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and K-medoids was used to comprehensively evaluate the features with multiple evaluation indicators and to adaptively select better degradation features to construct the sensitive feature set. Next, multi-strategy optimization of the SVDD model was carried out by introducing the autocorrelation kernel regression (AAKR) and a multi-kernel function to improve the ability of the evaluation model to overcome outliers and false fluctuations. Through validation, it could be seen that the method in this study uses samples of rolling bearings in the healthy early stage to establish the evaluation model, which can adaptively determine the starting point of the bearing’s degradation. The stability and accuracy of the model were effectively improved.