Integrating Model and Signal Based Methods for Efficient Fault Diagnosis

Fault diagnosis is a well-defined process that involves identification and isolation of abnormalities, or defects within a system. The system can work at its best in terms of speed and reliability by providing prompt identification and correction of the errors. It involves systematically identifying the abnormalities in the expected behavior and analyzing their root causes so that effective management and rectification can be made. These diagnostic processes are described in this article, and they range from traditional hardware redundancy to the current signal-based and model-based diagnostics. Diagnostics is one of the critical activities of the system maintenance process that involves identification, localization, and analysis of the defects, which enables the provision of the structure with reliability and efficiency. The paper categorizes fault diagnosis methodologies into hardware redundancy, signal processing and model-based approach and provides clear description of what they entail and how they are used. Notably, it stresses the use of model-based method, which is a software-based process model that eliminates the need for hardware duplication. This technique examines the critical parts, such as residual generation and observer-based fault detection. In addition, the research focuses on the probabilistic and deterministic techniques and signal-based approaches with the aim of detecting faults. These methods include those that address the strategies, which analyze the indicators in the domains of time, and time thresholds.