veatuvastusest

Veatuvastusest, the Estonian term for fault detection, refers to the systematic process of identifying faults or anomalies in a system, component, or data stream. The goal is to detect issues early, prevent failures, maintain safety, and optimize performance. It encompasses monitoring, diagnosis, and verification activities that establish whether a fault exists, its type, and often its location.

Definition and scope: It covers detecting the existence of faults, determining their type and severity, and locating the source in many contexts.

Methods: Techniques include threshold-based monitoring, statistical process control, model-based diagnostics, signal processing, sensor fusion, redundancy with majority voting, and machine learning for anomaly detection. Real-time health monitoring and built-in self-test are common implementations in hardware and software systems.

Applications: Industrial automation and maintenance, electrical grids, aerospace and transportation, consumer electronics, medical devices, and software reliability.

Process: Data collection from sensors, continuous monitoring, fault isolation, alerting, and triggering maintenance or safety actions. Diagnostic reasoning may use residual analysis, pattern recognition, or causal inference.

Challenges: Balancing sensitivity and specificity to minimize false alarms; data quality; noisy environments; latency; evolving systems requiring adaptive models.

Relation to other concepts: It is a core component of fault tolerance and predictive maintenance, complementing fault diagnosis, reliability engineering, and quality assurance.