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For complex mechatronic products cable in a long time after the use of performance degradation will lead to operational problems and product quality and safety, in order to make early warning prediction of cable decline state and prevent unexpected accidents of complex mechatronic products, a cable performance degradation state prediction method based on time-series convolutional fusion long and short-term memory network for complex mechatronic products was proposed. First, by analyzing the causes of cable decline, the data set was determined by selecting parameter indicators based on the analysis results, and the data reflecting the performance state of the cable was extracted under the temporal convolutional network for the temporal features. Finally, these feature data were trained together with non-time-series data with sufficiently high correlation and influence coefficients determined by Pearson correlation coefficient analysis to predict through a long and short-term memory network to obtain prediction results. The case results show and compare that the prediction results fit well with the actual situation, and for the data set containing time series data, the prediction results of the model with the addition of the time-series network part are higher than those of the other individual networks. [ABSTRACT FROM AUTHOR]

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