An incorporation of metaheuristic algorithm and two-stage deep learnings for fault classified framework for diesel generator maintenance

Abstract

Diesel generators play a vital role in providing reliable power to ensure uninterrupted power supply. However, effective fault classification in these systems is challenging due to their complexity. This paper proposes a two-stage framework that combines deep learning with metaheuristic optimization for fault classification of diesel generators in Artificial Internet of Things (AIoT) systems. The first stage involves employing a Long short-term memory convolutional neural network (LSTM-CNN) model for accurate feature extraction and fault detection. The improved particle swarm optimization (IPSO) algorithm is employed to optimize the hyperparameters of the LSTM-CNN model, resulting in an enhanced IPSO-LSTM-CNN framework. A comprehensive performance evaluation is conducted by comparing the developed algorithm with other models, including recurrent neural networks (RNN), CNN, gated recurrent units (GRU), LSTM, and CNN-LSTM. The IPSO-LSTM-CNN obtains the most significant gains of many evaluation benchmarks when compared to other state-of-the-art algorithms. In terms of fault classification accuracy and robustness, the created model performs better than the alternative methods, confirming its usefulness in enhancing the operational efficiency and dependability of diesel generators in AIoT frameworks. This research provides a completed IPSO-LSTM-CNN framework in AI application for failure diagnosis of industrial machinery in maintenance service.