In this paper, damage pre-cursors based residual life computation approach for various package elements to prognosticate electronic systems prior to appearance of any macro-indicators of damage has been presented. In order to implement the system-health monitoring system, precursor variables or leading indicators-of-failure have been identified for various package elements and failure mechanisms. Model-algorithms have been developed to correlate precursors with impending failure for computation of residual life. Package elements investigated include, first-level interconnects, dielectrics, chip interconnects, underfills and semiconductors. Examples of damage proxies include, phase growth rate of solder interconnects, intermetallics, normal stress at chip interface, and interfacial shear stress

Prognostics and Health Management of Electronics

Digital twin modeling

Prognostics and Health Management (PHM), including monitoring, diagnosis, prognosis, and health management, occupies an increasingly important position in reducing costly breakdowns and avoiding catastrophic accidents in modern industry. With the development of artificial intelligence (AI), especially deep learning (DL) approaches, the application of AI-enabled methods to monitor, diagnose and predict potential equipment malfunctions has gone through tremendous progress with verified success in both academia and industry. However, there is still a gap to cover monitoring, diagnosis, and prognosis based on AI-enabled methods, simultaneously, and the importance of an open source community, including open source datasets and codes, has not been fully emphasized. To fill this gap, this paper provides a systematic overview of the current development, common technologies, open source datasets, codes, and challenges of AI-enabled PHM methods from three aspects of monitoring, diagnosis, and prognosis.

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Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review

Traditional hydraulic servomechanisms for aircraft control surfaces are being gradually replaced by newer technologies, such as Electro-Mechanical Actuators (EMAs). Since field data about reliability of EMAs are not available due to their recent adoption, their failure modes are not fully understood yet; therefore, an effective prognostic tool could help detect incipient failures of the flight control system, in order to properly schedule maintenance interventions and replacement of the actuators. A twofold benefit would be achieved: Safety would be improved by avoiding the aircraft to fly with damaged components, and replacement of still functional components would be prevented, reducing maintenance costs. However, EMA prognostic presents a challenge due to the complexity and to the multi-disciplinary nature of the monitored systems. We propose a model-based fault detection and isolation (FDI) method, employing a Genetic Algorithm (GA) to identify failure precursors before the performance of the system starts being compromised. Four different failure modes are considered: dry friction, backlash, partial coil short circuit, and controller gain drift. The method presented in this work is able to deal with the challenge leveraging the system design knowledge in a more effective way than data-driven strategies, and requires less experimental data. To test the proposed tool, a simulated test rig was developed. Two numerical models of the EMA were implemented with different level of detail: A high fidelity model provided the data of the faulty actuator to be analyzed, while a simpler one, computationally lighter but accurate enough to simulate the considered fault modes, was executed iteratively by the GA. The results showed good robustness and precision, allowing the early identification of a system malfunctioning with few false positives or missed failures.

Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms

Computational framework for real-time diagnostics and prognostics of aircraft actuation systems

Real-time Fault Detection and Prognostics for Aircraft Actuation Systems

A Lumped Parameter High Fidelity EMA Model for Model-Based Prognostics

Prognostic algorithms able to identify precursors of incipient failures of primary flight command electromechanical actuators (EMAs) are beneficial for the anticipation of the incoming fault: an early and correct interpretation of the degradation pattern, in fact, can trig an early alert of the maintenance crew, who can properly schedule the servomechanism replacement. Given that very often these algorithms exploit a model-based approach (e.g. directly comparing the monitor with the real system or using it to identify the fault parameters by means of optimization processes), the design and development of appropriate monitoring models, able to combine simplicity, reduced computational effort and a satisfactory level of sensitivity and accuracy, becomes a fundamental and unavoidable step of the prognostic process. To this purpose, the authors propose a new simplified EMA Monitor Model able to accurately reproduce the dynamic response of the Reference Model in terms of position, speed and equivalent current, even with the presence of various incipient faults; its ability in reproducing the effects of several EMA faults is a good starting point for the implementation of a robust and accurate GA-based optimization, leading to a reliable and early fault isolation

Pier Carlo Berri

Papers

Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms

Computational framework for real-time diagnostics and prognostics of aircraft actuation systems

Real-time Fault Detection and Prognostics for Aircraft Actuation Systems

A Lumped Parameter High Fidelity EMA Model for Model-Based Prognostics

A Smart Electromechanical Actuator Monitor for New Model-Based Prognostic Algorithms