A divide and conquer approach to anomaly detection, localization and diagnosis

Prognosis of anterior cruciate ligament reconstruction: a data-driven approach

“…The system input-output operation space is partitioned into small regions using selforganizing maps and then a statistical model of the system expected behaviour within each region is constructed based on time-frequency distribution [12]. The significant deviations from the trained normal behaviour are recognized as anomalies.…”

A prognostics and health management roadmap for information and electronics-rich systems

“…The model-based approaches takes into account the physical processes and interactions between components in the system [20]. The data-driven approaches use statistical pattern recognition and machine-learning to detect changes in parameter data, thereby enabling diagnostic and prognostic measures to be calculated [21].…”

“…Statistical estimation techniques based on residuals and parity relations (the difference between the model predictions and system observations) are then used to detect, isolate and predict degradation [20,22]. Estimation techniques such as Kalman filters, particle filters, and parity relations are commonly used to calculate the residuals.…”

A Prognostics and Health Management for Information and Electronics-Rich Systems

“…17) A PoF-based tool has been developed for real time prediction of RUL of PCBs exposed to thermal cycling environments. 18) The tool integrates information from sensors, PoF models, and data fusion algorithms to en- Development of the models requires detailed knowledge of the underlying physical processes that lead to system failure, 21) and in complex systems, it is often difficult to create dynamic models representing the multiple physical processes occurring in the system. 22) Although JAPAN ARTICLE has shown that this is pos- …”