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.
Predictive maintenance (PdM) in edge-enabled Industrial Internet of Things (IIoT) environments requires reliable rare-fault detection, low-latency inference, robustness to sensor degradation, and explanations that can be inspected by engineers. This paper presents FusionNet, 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. The revised evaluation extends the original MetroPT3 compressor study to AI4I 2020 and NASA C-MAPSS FD001–FD004 using documented binary fault or failure-risk formulations. An additional AI4I-PMDI diagnostic run was audited separately and excluded from predictive claims because the source diagnostic field remained in the model inputs after being used to derive the target. To isolate the contribution of the fusion design, the experiments compare single branches, pairwise branch combinations, full FusionNet, late-fusion probability averaging, MLP-replacement variants, compact variants, and modern lightweight sequence baselines, including Time-Series Mixer (TS-Mixer), a lightweight Transformer encoder, and a state-space-model-inspired (SSM-inspired) gated convolution model. Robustness is examined under \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+3$$\end{document} dB Gaussian noise and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$50\%$$\end{document} channel dropout, while deployment-oriented evidence is assessed using parameter count, saved model size, latency, throughput, quantisation status, and execution-environment records. The main pipeline avoids the Synthetic Minority Over-sampling Technique (SMOTE) on flattened time-series windows; class weighting and focal loss are used as primary imbalance strategies, while flattened-window SMOTE is retained only as a labelled ablation. Integrated Gradients (IG) provides time–feature attribution maps, and all decision thresholds are selected from validation data using the geometric mean (G-Mean) prior to test evaluation. The experiments are regenerated from raw datasets on a high-performance computing (HPC) environment, with system specifications, thresholds, outputs, and reproducibility manifests recorded for verification. The results position FusionNet as a compact and interpretable candidate for sequence fusion in binary PdM evaluation. The reported latency and footprint measurements support deployment-oriented comparison under the tested HPC environment but do not constitute validation on a physical edge device.
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 induction motor systems.
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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