faultdiagnostic

Fault diagnostic, also called fault diagnosis, is the process of detecting, isolating, and identifying faults in a system to understand the cause of a fault and guide corrective action. It is a key component of reliability engineering, maintenance planning, and safety assurance across many domains.

The process typically includes fault detection (determining that a fault exists), fault isolation (localizing the fault to a component or subsystem), fault identification (determining the specific fault type), and prognosis or diagnosis of consequences to assess impact and timing.

Methods fall into model-based and data-driven categories. Model-based techniques use mathematical representations of the system to generate expected behavior and compute residuals that indicate anomalies. Data-driven methods rely on historical data and machine learning, statistics, or signal processing to recognize abnormal patterns and classify fault types.

Applications span industrial automation, automotive and aerospace maintenance, power systems, and process industries. Fault diagnostics enable condition-based maintenance, reduce unplanned downtime, extend asset life, and improve safety. Examples include engine fault diagnosis, bearing fault detection, and grid fault localization.

Challenges include noisy measurements, partial observability, rare fault events, and the need for real-time operation and interpretability. Integration with digital twins, data fusion, and standardized data protocols is advancing the field. Standards such as ISO 13374 provide guidance for condition monitoring and diagnostics data processing.