Real-Time Fault Diagnosis in Control Systems Using Big Data

Abstract

The ability to detect faults in real-time is critical for maintaining the reliability and safety of control systems in industries ranging from manufacturing to aerospace. Traditional fault diagnosis methods often rely on pre-established models or heuristics, which may be inadequate for complex, dynamic systems. With the advent of big data and advanced analytics, real-time fault detection has moved beyond simple thresholds and diagnostics to predictive models that adapt to changing system behaviors. This article explores the role of big data in real-time fault diagnosis for control systems, examining how large-scale data acquisition, machine learning algorithms, and real-time analytics can identify faults faster, improve system uptime, and enhance decision-making. Case studies from various industries are discussed to highlight the practical benefits of integrating big data into fault diagnosis strategies.