TL;DR
The Predictive Maintenance Fault Network (PdM-FaultNet) is the combination of the Enhanced Wombat Optimization Algorithm (EWOA) for the feature selection and Dual Quantum-inspired Denoising Autoencoder Transformer (DQDAT) for the predictive modeling.
The proposed Explainable AI-Based Predictive Maintenance Framework (XAI-PMF) addresses this challenge by integrating IIoT sensing, intelligent feature engineering, hybrid machine learning, and explainability techniques such as SHAP, LIME, and rule extraction.
FusionNet is presented, a compact three-branch sequence-fusion architecture that combines one-dimensional convolution (Conv1D), bidirectional long short-term memory (BiLSTM), and multilayer perceptron (MLP) pathways for multivariate industrial fault classification.
Experimental results demonstrate that the proposed framework achieves high fault prediction accuracy, enhances system reliability, reduces maintenance costs, and supports data-driven decision-making in industrial environments.
A structured methodology for ML-based PdM frameworks, covering data-driven, physics-based, and hybrid approaches, including supervised, unsupervised, and deep learning models is proposed, offering valuable insights for developing efficient and scalable PdM solutions.
The proposed IoT-MDS-EDFIM-IGANN framework is efficient, accurate, and cost-effective solution for induction motor fault diagnosis and combines advanced preprocessing, class balancing, feature extraction, and optimization to achieve reliable predictive maintenance and promote operational reliability of industrial induc...
A number of physically meaningful indicators, including thermal instability, vibration anomalies, smoke-related features, and aggregated risk scores, have a significant impact on maintenance predictions and hence provide better operational interpretability and maintenance transparency.
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