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.

Keywords: Root cause analysis, Diagnosis, Fault signature matrix, Fault detection and identification, Knowledge-based, Ship propulsion, Internal combustion engine.