A Novel Unsupervised Anomaly Detection Framework for Early Fault Detection in Complex Industrial Settings

Abstract

The increasing complexity and automation inherent in the contemporary Industry 4.0 paradigm necessitate robust and proactive fault detection methodologies to ensure both operational efficiency and safety. Existing unsupervised anomaly detection techniques, however, often encounter challenges when confronted with the high dimensionality, inherent noise, and complex interdependencies characteristic of industrial data. This paper proposes a novel unsupervised anomaly detection framework explicitly designed for early fault detection within such complex industrial environments. The proposed data-driven methodology systematically identifies the most effective unsupervised model for anomaly prediction from a candidate set of learning algorithms. This approach is particularly advantageous as it obviates the need for labeled historical fault data, a resource often limited in real-world operational settings. The 2015 PHM Data Challenge dataset, specifically selected for its inclusion of systems exhibiting incomplete fault logs, is used to validate the efficacy of the proposed framework. Findings underscore the significant potential of data-driven methodologies to enhance fault detection capabilities, thereby enabling timely intervention and contributing to the improvement of both the reliability and safety of industrial systems.

Keywords

• Fault detection
• anomaly detection
• industrial analytics
• machine learning
• unsupervised learning
• data-driven models