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

Abstract

The integration of Internet of Things devices in modern industrial environments has fundamentally transformed paradigms of machine health monitoring and predictive maintenance. However, industrial environments are inherently dynamic, leading to continuous fluctuations in operational conditions such as varying loads, speeds, and environmental temperatures. These fluctuations induce significant nonstationarity in the collected time-series data, rendering conventional assumption of independent and identically distributed data invalid and severely degrading the performance of standard deep learning models for fault diagnosis. This paper presents a comprehensive theoretical and methodological framework for nonstationarity-aware time-series modeling specifically tailored for industrial fault detection and diagnosis. We propose an advanced neural network architecture that dynamically adapts to distribution shifts through a continuous domain adaptation mechanism and an adaptive attention module. By incorporating localized statistical normalization and non-stationary feature alignment, the proposed approach effectively mitigates the adverse effects of concept drift without requiring explicit operational condition labels. Extensive theoretical analysis is provided to formalize the bounds of distribution divergence in degrading machinery. Furthermore, rigorous empirical evaluations on multiple complex industrial datasets demonstrate that the proposed framework significantly outperforms existing state-of-the-art diagnostic models in terms of accuracy, robustness, and generalization capabilities under severe operational transitions. The findings emphasize the critical necessity of embedding nonstationarity awareness directly into the optimization objectives of predictive models, paving the way for more resilient and autonomous industrial health management systems.