Simulation of Mechanical Component Fault Prediction Model Based on Artificial Neural Network Algorithm

This article proposes a mechanical component fault prediction model based on ANN (Artificial Neural Network), aiming to predict potential faults by learning historical operating data. By utilizing deep learning technology, this model can extract features from complex and nonlinear operational data and identify fault patterns. The study adopted a multi-layer perceptron structure, trained through backpropagation algorithm, and optimized the loss function to improve the accuracy of prediction. The research results indicate that in the testing of prediction errors, it can be found that the error value of the model in this paper remains around 5.45. Among the test results of the three models, the error value of the model in this paper is the lowest, indicating that the model in this paper has an advantage in specific types of fault prediction. In addition, the real-time predictive ability of the model provides a new solution for preventive maintenance, helping to reduce unexpected downtime and maintenance costs. Future work will focus on the real-time deployment of models and the study of generalization capabilities across multiple industrial scenarios.