Industrial Part Faults Prediction for Nonlinearity and Implied Temporal Sequences

The ability to preemptively identify potential failures in industrial parts is crucial for minimizing downtime, reducing maintenance costs and ensuring system reliability and safety. However, challenges such as data nonlinearity, temporal dependencies, and imbalanced datasets complicate accurate fault prediction. In this study, we propose a novel combined approach that integrates the Logistic Model Tree Forest (LMT) with Stacked Long Short-Term Memory (LSTM) networks, addressing these challenges effectively. This hybrid method leverages the decision-making capability of the LMT and the temporal sequence learning ability of Stacked LSTM to improve fault prediction accuracy. Additionally, to tackle the issues posed by imbalanced datasets and noise, we employ the ENN-SMOTE (Edited Nearest Neighbors-Synthetic Minority Over-sampling Technique), a technique for data preprocessing, which enhances data balance and quality. Experimental results show that our approach significantly outperforms traditional methods, achieving a fault prediction accuracy of up to 98.2%. This improvement not only demonstrates the effectiveness of the combined model but also highlights its potential for real-world industrial applications, where high accuracy and reliability are paramount.