Stable Implicit Conditioning With Residual Statistics for Multivariate Time-Series Anomaly Detection in Industrial IoT Monitoring

Nonstationarity-Aware IoT Time-Series Modeling for Industrial Fault Detection and Diagnosis

An advanced neural network architecture that dynamically adapts to distribution shifts through a continuous domain adaptation mechanism and an adaptive attention module is proposed that significantly outperforms existing state-of-the-art diagnostic models in terms of accuracy, robustness, and generalization capabilities under severe operational transitions.

State-conditioned association learning for unsupervised anomaly detection in industrial measurements

Normal temporal dependencies in multichannel industrial measurements vary across operating conditions and may contain recurring lag structures caused by delayed process dynamics. A state-invariant current association and a mainly local temporal reference may therefore confound normal regime variation with anomaly-related structural change. This paper proposes State-Conditioned Association Learning (SCAL), an unsupervised anomaly-detection framework in which the current temporal association adapts to measurement-derived state and time-varying channel contributions, whereas the normal reference is anchored by recurring lag patterns learned exclusively from normal data. Temporal Variable Contribution Modeling calibrates Value-side content, State-Conditioned Series Association regulates temporal matching, and Empirical Temporal Prior Association combines Gaussian locality with a fixed empirical normal-lag profile. Their discrepancy weights the reconstruction error for anomaly scoring. SCAL is evaluated on SWaT, WADI, HAI 21.03, and ADAPT under a common normal-only training and evaluation protocol. It achieves the highest F1-score among the compared methods on the evaluated datasets, with an average F1-score of 95.60% across the three benchmarks and 97.69% on ADAPT, where the missed-alarm rate of Anomaly Transformer decreases from 13.71% to 1.00%. Ablation, sensitivity, computational profiling, and fault-run analyses further characterize the proposed mechanism across heterogeneous industrial measurement settings.

Online Unsupervised Anomaly Detection with Model Explainability and Human-in-the-Loop Feedback

Smart building IoT networks generate complex, continuous sensor streams that challenge traditional anomaly detection due to concept drift and limited interpretability. This project proposes and validates an online, unsupervised, and human-refinable anomaly detection architecture. The system uses a parallel inference engine to simultaneously run two models: a high-accuracy deep transformer and a lightweight spectral CNN, both deployed in a real-time Streamlit prototype with Kafka ingestion and edge hardware benchmarking. To ensure interpretability, the architecture integrates a realtime explainability module using Shapley Additive Explanations (SHAP) and a Human-in-the-Loop (HITL) feedback mechanism to validate detections. Experimental results from a controlled streaming simulation demonstrate that the Online Transformer adapts to distribution shifts via dynamic thresholding, achieving 93% precision. At the same time, the SR-CNN offers superior efficiency (< 10 ms latency) with 96% precision. The integration of SHAP achieved a 98.1% convergence rate within the 2-second transmission window, indicating that 68% of detected faults were “Contextual Anomalies”-subtle history conflicts that are invisible to static thresholds. Integrating online representation learning with HITL feedback creates a strong anomaly detection system for dynamic edge environments.

Hierarchical Residual Attribution with Supervised Channel Shortlisting for Sensor-Local and Process Fault Diagnosis in Multivariate Sensor Time Series

Binary anomaly alarms are often insufficient for sensor systems because maintenance actions depend on whether abnormal behavior is localized to a sensor channel or reflects a process-level event. This paper proposes Hierarchical Residual Attribution with supervised channel shortlisting (HRA-SL), a diagnostic framework for multivariate sensor windows under a controlled injected-perturbation protocol. HRA fits calibration-based cross-channel consistency residuals for source-level diagnosis; HRA + spectral is the main source-diagnosis configuration; and HRA-SL adds supervised channel-candidate shortlisting for affected-channel localization. The evaluation uses synthetic, UCI HAR, and UCI Air Quality base signals with split-before-injection protocols over five seeds and five severity levels. HRA + spectral improves source-level macro-F1 over statistical, spectral, statistical + spectral, tuned TCN-AE, tuned USAD-like AE, and GRU-AE reconstruction-feature baselines, reaching 0.780±0.028, 0.655±0.017, and 0.554±0.029, respectively. Ablations show that HRA-Core carries most source-level signal, while residual-attribution features support channel ranking and diagnostic interpretation. The fixed HGB HRA-SL localizer reaches top-1 affected-channel localization of 0.916, 0.714, and 0.715. Under matched target-channel supervision, HRA-SL is practically tied with AE-only HGB on synthetic data (0.916 versus 0.918) and UCI HAR (0.714 versus 0.716), and is higher on Air Quality (0.715 versus 0.652). It also improves over the strongest unsupervised tuned reconstruction ranking baseline by 0.127, 0.357, and 0.048 top-1 under different supervision assumptions. The study establishes a controlled-injection benchmark for reproducible sensor source diagnosis and affected-channel shortlisting on public base signals, providing pre-deployment evidence that can be extended to field-fault studies with maintenance records.

IoT Time-series Anomaly Detection Using a Hybrid Transformer-GRU Fusion Model

This work proposes Residual GRU-Attention Anomaly Detector (RGAAD), an unsupervised framework for IoT time-series anomaly detection that achieves highly competitive performance and consistently outperforms strong baseline methods.

CAAD: A Cross-Modal Autoregressive Diffusion Approach for Anomaly Detection in Complex Industrial Processes