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A Hybrid Deep Learning-Based Machine Learning Model for Predicting Industrial Machinery Failures in Preventive Maintenance
Mohammad reza Mehregan1 , Arman Rezasoltani2 , Amir Mohammad Khani2 , Ali Hosseinzade kashan3
1- Prof., Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran.
2- Ph.D. Candidate, Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran.
3- Associate Prof., Department of Socio-economic Systems, , Tarbiat Modares University, Tehran, Iran. , a.kashan@modares.ac.ir
Abstract:   (863 Views)
In the modern industrial view, it is strategically imperative to predict failure of industrial machinery with a view to reducing the occurrence of unexpected failures and enhancing operational efficiency. This study seeks to introduce a new hybrid machine learning model for predictive maintenance, combining the use of deep learning and advanced ensemble machine learning models. The model presented follows a stacking ensemble structure, where XGBoost, CatBoost, Gradient Boosting, and a deep neural network are base learners. Thereafter, the LightGBM, acting as a meta-model, is used to collect its predictions. Further, in this study, the Optuna hyperparameter optimization framework is employed to optimize the hyperparameters automatically, and the NearMiss algorithm solves the class imbalance problem by enhancing the representation of the minority class and removing the bias in favor of the majority class. As can be seen in the experimental results, the combined model outperforms the single models, achieving an outstanding accuracy of 96.17%. This is followed by a precision of 97.86%, a recall of 94.4%, and an F1 score of 96.1%. It is worth noting that though the XGBoost models' independent results were high (with an F1 score of 89/41) and better than the 16 individual models studied in this paper and regarded as a comparison to the hybrid model, the hybrid model significantly defeated the independent models by nearly 7 percentage points, hence the strong suit of the smart ensemble framework in model combination. The model has been tried using industrial data with 10000 records of a milling machine system, which is representative of most industrial machinery. The model aids in making decisions in preventive maintenance processes in a more informed and timely way by detecting failures accurately before they happen, avoiding unwanted situations of unplanned downtime and operation costs. One can arrive at the conclusion based on these results that the mentioned hybrid model can offer a solid and workable way of predicting failures in the industrial context and can also be integrated into the actual maintenance processes without any issues.
Keywords: Deep Learning, Hybrid Model, Machine Learning, Predictive Maintenance, Stacking Ensemble.
     
Type of Study: Research | Subject: Operations Managment
Received: 2025/04/26 | Accepted: 2025/10/26
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