Nonlinear Wiener process analysis for industrial equipment life prediction

Predicting the lifespan of industrial equipment can promptly identify potential problems and provide timely maintenance and updates. However, current methods for predicting equipment lifespan still suffer from poor prediction performance and long prediction time because of the complexity of equipment information. To address the aforementioned issues, this study designs a nonlinear micro–nano-process analysis model based on intelligent algorithms and proposes an industrial equipment life forecasting approach grounded on the constructed model. The study first tests the predictive performance of the analysis model, and the outcomes indicate that the model has a forecasting accuracy of over 97% on the BSDS500 dataset, Set14 dataset, Nam dataset, and PolyU dataset. Further analysis of the performance of the industrial equipment life forecasting approach grounded on this model shows that the forecasting accuracy of this method for different types of industrial equipment is greater than 98%, and the prediction time is less than 2 s. It can also reduce the operation and maintenance costs of industrial equipment by more than 70%. From the above-mentioned results, the proposed industrial equipment life forecasting method can precisely predict the life of equipment while saving most of the equipment operation and maintenance costs for enterprises and promoting the development of enterprise intelligence and automation.