AIoT Fault Diagnosis Maintenance Frameworks Based Metaheuristic and Two‐Stage Deep Learning Algorithms for Diesel Generators

Background The reliable operation of diesel generators, which are critical devices in many industrial applications in maintaining continuous power supply, is essential. Objective This research presents an advanced Artificial Intelligence of Things (AIoT) fault diagnosis framework that combines metaheuristic optimization and two‐stage deep learning methods to improve the fault classification accuracy of diesel generators. Methods The improved particle swarm optimization (IPSO) is used to fine‐tune the hyperparameters of bidirectional long short‐term memories (BiLSTM) combined with convolutional neural networks (CNN). The developed hybrid model aims to exploit the power of both the sequential learning of BiLSTMs and the feature extraction capability of CNNs to achieve superior fault diagnosis performance. Seven established baselines provide the point of reference for the IPSO‐BiLSTM‐CNN framework: recurrent neural network (RNN), CNN, gated recurrent unit (GRU), LSTM, bidirectional RNN (BiRNN), BiLSTM and CNN‐BiLSTM. Results Experimental results demonstrate that IPSO‐BiLSTM‐CNN methods outperform existing models in terms of accuracy and stability, achieving relative reductions of 71.56% in loss, 73.79% validating loss, 64.63% mean absolute error (MAE), 69.75% validating MAE, 23.17% cosine proximity (CP), 21.99% validating CP, 63.08% mean squared error (MSE), 71.55% validating MSE, together with relative improvements of 4.79% precision (PRE), 3.79% validating PRE, 5.8% recall (REC), and 5.29% validating REC. Conclusions This highlights its potential in practical AIoT applications for diesel generator monitoring and maintenance.