A Recurrent Neural Network-Based Approach for Predictive Analysis of Equipment Durability

Machine Learning algorithms are used in industries to analyze the machinery equipment. It enhances the product quality by detecting the defects and reducing the cost of inventory optimization. Recurrent Neural Network is the best fit for durability prediction over time. This paper provides a thorough study in the prediction of machinery wear using techniques from machine learning, specifically LSTM networks and Random Forest Regression. The study is utilized with the help of a dataset made up of the sensor readings collected from the machinery and kept in format from CSV including proper headers for feature labels and targets associated with levels of wear and tear. The methodology involves the main steps, such as data acquisition, preprocessing, addressing missing values and normalization, sensor data, and exploratory data analysis (EDA), which assists with visualizing relationships in features. Two predictive models implemented are LSTM: wherein temporal dependencies within the sequential data are captured, and Random Forest Regression, ensemble method managing nonlinear relationships. Both have been trained on a training set and later tested on a test set by using performance metrics such as Mean Squared Error (MSE), R-squared, and also for classification, precision percentage for the decisions made, 'Replace' vs. 'Repair'. Both the models show effective prediction of wear and tear levels, based on these results, valuable insights are developed for maintenance decisionmaking in the industrial applications. This possibility is demonstrated within the study and a means of integrating machine learning techniques into predictive maintenance strategies for enhanced operational efficiency and, subsequently, lessened equipment downtime.