Improving Fault Diagnosis Efficiency by Integrating FMEA and FTA in a Compact Fault Signature Matrix

Complex systems have an integrated architecture that leads to non-trivial interdependencies between components.Any fault in such a system can impact other components, reducing system performance.While most existing methods can detect process abnormalities and component faults, they often fail to identify root causes.In response, this study presents a fault diagnosis framework based on domain-specific knowledge.The framework enhances root cause identification by leveraging expert insights, maintenance logs, and/or other documented knowledge.The proposed method integrates this knowledge through a structured approach.First, a Failure Mode and Effects Analysis (FMEA) is conducted to determine the most critical failure modes and associated fault symptoms for each component.Second, a Fault Tree Analysis (FTA) is used to reveal dependencies between components.The resulting information is used to construct an improved Fault Signature Matrix (FSM) that captures individual failures and their system-level dependencies.In this way, people with and without knowledge can use this tool to investigate failure causes after detecting malfunctions.The proposed methodology is applied to a ship propulsion technology, providing information on the parameters required to diagnose the state of the system.