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In order to realize the prediction of the remaining useful life (RUL) of the low-voltage conventional circuit breaker mechanical system in the whole life cycle, a prediction method based on opening-related vibration signal and stage attention hybrid neural network is proposed. The opening vibration signal contains richer mechanical state information. Therefore, the variational mode decomposition (VMD) based on energy loss is used to reconstruct the signal to increase the time-domain identification of the shock characteristics of the vibration signal, and the double threshold method (DTM) based on short-time energy entropy ratio (STEER) is used to realize the segmentation of the opening-related vibration signal. A stage attention mechanism (SA) is proposed, combining the convolutional neural network (CNN) and the gated recurrent unit (GRU) to construct the SA-CNN-GRU hybrid model. The first-stage distributed attention (DA) acts on the CNN to refine the mining of the information sensitive to degradation in the time and feature dimensions, and the second-stage time-step attention (TA) acts on the GRU to learn the time-series gradual information of the degradation process in a targeted manner and eliminate the influence of the degradation uncertainty of the complex system. Using the vibration data of three circuit breakers to test, the results show that the proposed method can solve the RUL prediction problem of circuit breaker mechanical systems and have high prediction stability, and the introduction of SA can effectively improve the prediction performance of the model. [ABSTRACT FROM AUTHOR]

In order to realize on-line mechanical life prediction of conventional low-voltage circuit breaker and improve its operation reliability, a life prediction method based on vibration signal detection is proposed. Firstly, the effective vibration segment that characterizes the mechanical performance of contact system are extracted by combining with the designed signal processing method; then, a multi-channel convolutional auto-encoder long short term memory network (MCCAE-LSTM) life prediction model for individual circuit breaker is constructed, in which the MCCAE is used to obtain the time sequential micro features rich in life degradation information, and the LSTM realizes the quantitative output of the remaining mechanical life; finally, through the actual mechanical life experiment and prediction performance analysis, the effective vibration segment can reflect the mechanical characteristics of the contact system, the measurement method is easy to be carried out on-line, and the average goodness of fit of the prediction model is improved by about 10% compared with the traditional method, which verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In order to eliminate the influence of parameter predefined caused by manual feature extraction, achieve fast feature extraction, and improve the recognition rate of fault diagnosis, a 2-D convolution neural network (CNN) method for fault diagnosis of conventional circuit breaker contact system is proposed. First, by introducing the data preprocessing method of continuous wavelet transform (CWT), the nonlinear and nonstationary original vibration signal is transformed into a time–frequency image to extract the transformed image features. Second, the convolutional layer module in the AlexNet model is combined with the network in network (NIN) module, and the global average pooling (GAP) layer is adopted to replace the fully connected (FC) layer, which realizes the improvement of the traditional AlexNet model. Then, an improved Adam optimization algorithm, namely, AMSGrad, is adopted to solve the problem that the Adam optimization algorithm may not converge or produce local optimization during model training. Finally, the preprocessed time–frequency image is taken as the input of the improved AlexNet model and through the supervised adjustment of network parameters, the fault diagnosis of the contact system for the conventional circuit breaker is realized accurately. [ABSTRACT FROM AUTHOR]

In terms of estimating the ac contactor’s electrical life, a new method based on initial characteristic parameters is put forward in this article. First, the characteristic parameters of the contact voltage in the closing stage and the arcing energy in the breaking stage are selected to extract their statistical indexes due to different stages in the contactor’s action, and the correlation between the indexes and electrical life is calculated by means of the Spearman rank correlation coefficient. Next, the electrical life evaluation indexes corresponding to the stages of closing and breaking are computed according to the weighted Euclidean distance calculation; the electrical life comprehensive evaluation index is presented by secondary fusion of the abovementioned indexes. Finally, a life prediction model based on unitary regression is established to evaluate electrical life with the predicted input of the comprehensive evaluation index. The results have displayed that the proposed method succeeds in quantitatively evaluating the electrical life of an ac contactor. [ABSTRACT FROM AUTHOR]