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This research introduces a hybrid neural architecture integrating spatiotemporal permutation entropy and quantile regression to improve long-horizon fault prediction in industrial systems, directly addressing the need for uncertainty-aware prognostics. By combining Spiking Neural Networks with Transformers, the framework aims to capture weak degradation signatures that traditional point-estimate models often miss across distributed sensor networks. The study validates this approach on a nine-system dataset, offering a practical advancement over standard baselines like LSTM and LightGBM for complex system maintenance without proposing new foundational learning theory.
Boosted Enhanced Quantile Regression Neural Networks with Spatiotemporal Permutation Entropy for Complex System Prognostics
arXiv:2507.14194v3 Announce Type: replace-cross Abstract: This paper presents an integrative prognostic framework that combines Spatiotemporal Permutation Entropy (STPE), Boosted Enhanced Quantile Regression Neural Networks (B-EQRNNs), Gated Temporal Attention, a Spiking Neural Network (SNN) refinement stage, and a Temporal Fusion Transformer (TFT) classifier. The motivation is long-horizon fault prediction in distributed industrial electronic systems, where single-sensor or point-estimate models can miss weak spatially propagating degradation signatures and provide limited uncertainty information. The proposed pipeline first converts 70-channel sensor streams into multiscale STPE descriptors, then learns conditional quantile representations and attention-weighted temporal context before final Normal/Abnormal classification. Evaluation is reported on a nine-system industrial electronic-sensor dataset with 48-, 90-, and 168-hour prediction horizons. The comparison includes a tree-based LightGBM baseline and modern sequence baselines available under the same preprocessing protocol, including LSTM, Autoformer, and TCN models. The full pipeline reaches 81.17% accuracy at the 168-hour horizon and is evaluated with component ablations, computational-cost analysis, and an explicit reproducibility protocol. The contribution is therefore framed as a validated hybrid architecture for uncertainty-aware spatiotemporal prognostics rather than as a new standalone learning theory.
Abstract:This paper presents an integrative prognostic framework that combines Spatiotemporal Permutation Entropy (STPE), Boosted Enhanced Quantile Regression Neural Networks (B-EQRNNs), Gated Temporal Attention, a Spiking Neural Network (SNN) refinement stage, and a Temporal Fusion Transformer (TFT) classifier. The motivation is long-horizon fault prediction in distributed industrial electronic systems, where single-sensor or point-estimate models can miss weak spatially propagating degradation signatures and provide limited uncertainty information. The proposed pipeline first converts 70-channel sensor streams into multiscale STPE descriptors, then learns conditional quantile representations and attention-weighted temporal context before final Normal/Abnormal classification. Evaluation is reported on a nine-system industrial electronic-sensor dataset with 48-, 90-, and 168-hour prediction horizons. The comparison includes a tree-based LightGBM baseline and modern sequence baselines available under the same preprocessing protocol, including LSTM, Autoformer, and TCN models. The full pipeline reaches 81.17% accuracy at the 168-hour horizon and is evaluated with component ablations, computational-cost analysis, and an explicit reproducibility protocol. The contribution is therefore framed as a validated hybrid architecture for uncertainty-aware spatiotemporal prognostics rather than as a new standalone learning theory.
Preliminary version of a predictive maintenance framework using spiking neural networks and entropy-based analysis. To be expanded in future publications with hardware implementations and real-time drift detection modules. arXiv admin note: substantial text overlap with arXiv:2501.05087
Signal Processing (eess.SP); Machine Learning (cs.LG); Systems and Control (eess.SY)
arXiv:2507.14194 [eess.SP]
(or arXiv:2507.14194v3 [eess.SP] for this version)
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