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  2. Fault detection and isolation - Wikipedia

    en.wikipedia.org/wiki/Fault_detection_and_isolation

    Fault detection, isolation, and recovery (FDIR) is a subfield of control engineering which concerns itself with monitoring a system, identifying when a fault has occurred, and pinpointing the type of fault and its location. Two approaches can be distinguished: A direct pattern recognition of sensor readings that indicate a fault and an analysis ...

  3. Predictive maintenance - Wikipedia

    en.wikipedia.org/wiki/Predictive_maintenance

    Machine Learning approaches are adopted for the forecasting of its future states. [3] Some of the main components that are necessary for implementing predictive maintenance are data collection and preprocessing, early fault detection, fault detection, time to failure prediction, and maintenance scheduling and resource optimization. [4]

  4. Anomaly detection - Wikipedia

    en.wikipedia.org/wiki/Anomaly_detection

    Anomaly detection is applicable in a very large number and variety of domains, and is an important subarea of unsupervised machine learning. As such it has applications in cyber-security, intrusion detection, fraud detection, fault detection, system health monitoring, event detection in sensor networks, detecting ecosystem disturbances, defect ...

  5. Condition monitoring - Wikipedia

    en.wikipedia.org/wiki/Condition_monitoring

    The Criticality Index is often used to determine the degree on condition monitoring on a given machine taking into account the machines purpose, redundancy (i.e. if the machine fails, is there a standby machine which can take over), cost of repair, downtime impacts, health, safety and environment issues and a number of other key factors. The ...

  6. Structural health monitoring - Wikipedia

    en.wikipedia.org/wiki/Structural_health_monitoring

    Group classification and regression analysis are categories of supervised learning algorithms. Unsupervised learning refers to algorithms that are applied to data not containing examples from the damaged structure. Outlier or novelty detection is the primary class of algorithms applied in unsupervised learning applications.

  7. Deformation monitoring - Wikipedia

    en.wikipedia.org/wiki/Deformation_monitoring

    Deformation monitoring is primarily associated with the field of applied surveying but may also be relevant to civil engineering, mechanical engineering, construction, and geology. The measurement devices utilized for deformation monitoring depend on the application, the chosen method, and the preferred measurement interval.

  8. Failure mode, effects, and criticality analysis - Wikipedia

    en.wikipedia.org/wiki/Failure_Mode,_Effects,_and...

    Fault detection coverage that system built-in test will realize; Whether the analysis will be functional or piece-part; Criteria to be considered (mission abort, safety, maintenance, etc.) System for uniquely identifying parts or functions; Severity category definitions

  9. Fault tree analysis - Wikipedia

    en.wikipedia.org/wiki/Fault_tree_analysis

    A fault tree diagram. Fault tree analysis (FTA) is a type of failure analysis in which an undesired state of a system is examined. This analysis method is mainly used in safety engineering and reliability engineering to understand how systems can fail, to identify the best ways to reduce risk and to determine (or get a feeling for) event rates of a safety accident or a particular system level ...

  1. Related searches fault detection techniques in machine learning applications in civil engineering

    fault detection and isolationfault detection and recovery