Analysis method combining improved AE algorithm and signal reconstruction in mechanical faults

Introduction Fault diagnosis analysis of mechanical equipment is greatly significant for maintaining the production efficiency of enterprises. Traditional diagnostic methods have shortcomings in accuracy and robustness. Methods Therefore, the study integrates variational autoencoders with long short-term memory network models, enhances them using dropout methods, and proposes a hybrid diagnostic analysis model that combines improved autoencoder algorithms and signal reconstruction. Results The experiment outcomes indicated that under the slow degradation mode of the bearing, the precision, recall, F1 score, and overall accuracy of the improved autoencoder model were 0.931, 0.933, 0.920, and 0.939, respectively, which were better than the pre-modified model. The fault diagnosis results showed that in the rapid degradation mode of the bearing, the research model discovered potential faults at 8,830 s, earlier than other models. The ablation experiment results showed that the precision, recall, F1 score, and overall accuracy of the enhanced study model using the dropout method were 0.83, 0.80, 0.82, and 0.99, respectively. Compared with the baseline model, the four indicators improved by 5.1%, 6.7%, 6.5%, and 5.3%, respectively. The memory usage test findings denoted that the average memory usage of the research model was less than 46%, which was better than the control model. Discussion The research promotes innovation and optimization of mechanical fault diagnosis technology, improves the accuracy and timeliness of fault diagnosis analysis models, and is of great significance for ensuring production safety, reducing maintenance costs, and improving enterprise economic benefits.