A hybrid machine learning algorithm approach to predictive maintenance tasks: A comparison with machine learning algorithms

Digital transformation has enabled the industrial sector to digitize all of its production processes, generating a vast quantity of data from sensors and actuators deployed along production lines. This data can provide valuable insights into the behavior of a specific machine, enabling optimization or the prediction of potential malfunctions. Supervised machine learning algorithms are capable of predicting the remaining useful life (RUL) of a machine. However, there is room for improvement in this prediction. In this paper, a hybrid approach that combines supervised learning (multi-layer perceptron MLP) and reinforcement learning (Q-learning) algorithms is proposed with the aim of improving the accuracy and precision of predicting the RUL. To test the efficacy of the proposed hybrid algorithm, a dataset comprising data from C-MAPPS aircraft engines is utilized. To assess the effectiveness of the hybrid algorithm, it was compared with a supervised learning algorithm. The results indicate that the proposed hybrid approach increases accuracy by 15% compared to models that use a single supervised learning algorithm, such as support vector regression (SVR), multi-layer perceptron (MLP), convolutional neural networks (CNN), and long short-term memory (LSTM), and an increase in accuracy of 4% over other hybrid algorithms, such as convolutional neural networks and long short-term memory. Additionally, the hybrid approach enables early predictions of approximately 17 cycles and late predictions of only 2%, while other learning algorithms, such as linear regression, do not make early predictions but 100% of their predictions are late. Implementing a hybrid approach would prevent unexpected machine downtime, enhancing reliability and reducing maintenance times and costs.