A new model for maintenance prediction using altruistic dragonfly algorithm and support vector machine

Abstract Predictive maintenance (PdM) is a proactive approach aimed at anticipating the future point of failure for a machine or a component, with the goal of reducing both the frequency and the expenses associated with unplanned downtime. Recent advances in machine learning (ML) techniques have enabled PdM to be more efficient with diverse and successful applications in various manufacturing industries. The support vector machine (SVM), a fundamental ML algorithm, is renowned for its effectiveness in addressing classification and regression tasks. Nevertheless, the successful application of SVM hinges on the careful tuning of its parameters, a process that significantly influences its predictive performance. This research seeks to optimize the selection of the regularization parameter <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>C</m:mi> </m:math> C and the kernel parameter <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>σ</m:mi> </m:math> \sigma using metaheuristic methods. It suggests combining the altruistic dragonfly algorithm (ADA) with SVM to enhance the prediction of maintenance failures. The primary motive for integrating altruism into this research is the unprecedented utilization of altruistic principles within this specific area. In addition, ADA-SVM provides a balance between exploration and exploitation. This balance is achieved through the altruistic behavior of dragonflies, where they help each other find better solutions. Therefore, this model is not trapped in the local optimum. The effectiveness of the model ADA-SVM is assessed on aircraft engine sensor data in comparison with other metaheuristic optimization algorithms, namely, genetic algorithms (GA), particle swarm optimization (PSO), grey wolf optimization (GWO) and dragonfly algorithm (DA). The performance of the SVM has been improved significantly by using parameter optimization. Besides, while GA-SVM, PSO-SVM, and DA-SVM models were able to predict engine failures with 95% accuracy, and the GWO-SVM, which demonstrated a good performance in terms of accuracy compared to other metaheuristics algorithms, achieves an accuracy of 97%, the ADA-SVM reached the best accuracy value which is 98%. The findings, thus, reveal that the proposed model outperforms the other models in optimizing SVM parameters, and, therefore, improves the performance of the engines failures prediction.