Self-supervised prediction contrast time frequency for industrial fault detection

Industrial informatics produce vast data, but fault detection is hindered by dynamic processes and non-linear interactions. Deep learning and machine learning help, but sensor errors, transmission failures, and scarce labels remain issues. Self-supervised learning (SSL) uses unlabeled data to train models and generate labels. Prediction contrast learning, a key SSL method, preserves temporal patterns, making it ideal for time-series data while avoiding constant parameter tuning required by traditional methods. To tackle fault detection task, this thesis proposes a novel self-supervised learning architecture using prediction contrast learning to enhance fault detection (FD) in industrial settings. Our approach integrates time and frequency domain information to capture crucial temporal dependencies and improve model robustness. The key contributions of this work include: the novel combination of time-frequency information in SSL, the first application of prediction contrast learning for FD, a complex network analysis for real-time performance and scalability, and an extensive evaluation including ablation studies. To address the challenges of data scarcity and enhance fault detection accuracy in complex environments, our proposed model incorporates data transformations that improve robustness while preserving essential temporal relationships for time series analysis. We evaluated our approach using the Tennessee Eastman Process (TEP) benchmark datasets, including both Ricker and Rieth versions, which are widely recognized standards for testing FD systems in industrial processes. We also evaluate our model in Secure Water Treatment (SWaT) and Water Distribution (WADI) dataset, which contain anomalies such as intentional attack scenarios injected into the water management system. Experimental results on the TEP datasets demonstrate that our approach significantly outperforms existing benchmark models, achieving a True Positive Rate (TPR) of 92.04% and reducing the Average Detection Delay(ADD) to 22.18 steps, Correct Diagnosis Rate (CDR) of 97.62% effectively addressing data scarcity challenges and enhancing fault detection accuracy in complex industrial environments. Similar outperforming results have also been observed in other benchmark datasets. To ensure practical applicability, we conduct complex network topology analysis to evaluate our model's real-time performance and scalability in both cloud and edge computing environments, considering industrial networks as complex systems with non-trivial topological features. This analysis provides a comprehensive comparison of latency differences between edge and cloud computing architectures, addressing a critical gap in the fault detection literature. The proposed SSL architecture, combined with our comprehensive network analysis, offers a robust solution for FD in industrial chemical facilities. By integrating advanced deep learning techniques with practical considerations for implementation in various computing environments, our work paves the way for more efficient and reliable fault detection systems in industry.