Managing and Predicting Intermittent Failures Within Long Life Electronics

Prognostics and health management (PHM) represents a paradigm shift from legacy condition based maintenance (CBM) frameworks by expanding the potentials to accurately and robustly detect and diagnose incipient system faults. The ultimate goal of PHM is reliably predicting system failure times to allow for efficient maintenance scheduling either autonomously or by human decision makers (DM). In many industrial settings today the output from PHM systems constitutes a decision support system (DSS) used to aid DM, as entirely autonomous systems have not seen widespread industrial integration. However, there is relatively little support for engineers designing PHM systems in terms of human factors and how to provide the information in a way that actively supports human decision-making and this gap may result in limited use of PHM system by maintainers. The reliability of the information presented is a critical factor in the user acceptance and trust in a system. As a first step in developing such guidance, this paper reviews the implementation of other DSS and presents a design framework whereby PHM reliability levels are mapped against a suggested level of human input to the decision making process regarding required maintenance. The aim is to provide engineers with a guide to the level to which they should consider human factors and the presentation of information in the design of their PHM system. Fundamental to the suggested paradigm is that the uncertainties within a PHM system can be quantified, and as uncertainty increases, the requirement for DM to access additional information not explicitly tied to the PHM output increases. This information can form both explicit and tacit knowledge of a system or indeed industrial contexts surrounding decision implications, such as acceptable maintenance intervention windows in busy production schedules. As the complexity of a system or component being monitored is likely to affect the uncertainty within the PHM system associated with it, we are considering the overall cumulative uncertainty of a model output as the metric through which the required level of human input can be inferred. Coupled to this is the fact that increased model uncertainty is a causal factor in distrust and potential non-use of the model in industrial applications. It is the authors’ belief therefore that designing for increased human-model interaction concurrent with increasing model uncertainty may lead to a better engagement with PHM decision support capabilities, thereby offering the full advantages that PHM has to offer. The framework presented in this paper is an initial step towards facilitating the design of more usable and useful PHM systems.

This paper based on four key abilities of the Prognostics and Health Management (PHM) System including the abilities of fault detection, fault isolation, fault prognosis and prognosis of the remaining life, analyzes and constructs a framework of the PHM quantitative requirements, and defines the quantitative assessment emphasis of the PHM abilities. Targeting these quantitative requirements and proceeding from the viewpoint of life cycle assessment, this paper presents three methods of the PHM System validation, which are the validation method based on analysis and assessment, the validation method based on simulation (including entire simulation validation and half-entity simulation validation) and the validation method based on experimentation. This paper defines the emphasis on the PHM System analysis and assessment, ascertains the implementary elements and the simulated environmental framework of the PHM System simulated validation and elaborates two central elements of the PHM System experimental validation - the experiment scheme and the experiment technique. This paper provides thoughts and methods to the quantitative validation assessment of the rapidly developing and applying PHM System.

Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications

Aircraft are highly valuable assets and large budgets are spent in preventive and predictive maintenance programs. The application of PHM (Prognostics and Health Management) technologies can be a powerful decision support tool to help maintenance planners. The RUL (Remaining Useful Life) estimations obtained from a PHM system can be used in order to plan in advance for the repair of components before a failure occurs. However, when system architecture is not taken into account, the use of PHM information may lead the operator to rush to replace a component that would not affect immediately the operation of the system under consideration. This paper presents a methodology for decision support in maintenance planning with application in aeronautical systems. The methodology combines system architecture information and RUL estimations for all components comprised in the system under study, allowing the estimation of a RUL value for the whole system. This system level RUL (S-RUL) can be used as support information for identifying the best moment to repair a component. Also, when several components present high degradation levels, the proposed methodology can be used to define a set of components that, when repaired, will bring the whole system to a safe degradation level with lowest cost. A case study is used to illustrate the application of the methodology in a simplified aircraft electrical system.

The fields of risk analysis and prognostics and health management (PHM) have developed in a largely independent fashion. However, both fields share a common core goal. They aspire to manage future adverse consequences associated with prospective dysfunctions of the systems under consideration due to internal or external forces. This paper describes how two prominent risk analysis theories and methodologies - Hierarchical Holographic Modeling (HHM) and Risk Filtering, Ranking, and Management (RFRM) - can be adapted to support the design of PHM systems in the context of smart manufacturing processes. Specifically, the proposed methodologies will be used to identify targets - components, subsystems, or systems - that would most benefit from a PHM system in regards to achieving the following objectives: minimizing cost, minimizing production/maintenance time, maximizing system remaining usable life (RUL), maximizing product quality, and maximizing product output. HHM is a comprehensive modeling theory and methodology that is grounded on the premise that no system can be modeled effectively from a single perspective. It can also be used as an inductive method for scenario structuring to identify emergent forced changes (EFCs) in a system. EFCs connote trends in external or internal sources of risk to a system that may adversely affect specific states of the system. An important aspect of proactive risk management includes bolstering the resilience of the system for specific EFCs by appropriately controlling the states. Risk scenarios for specific EFCs can be the basis for the design of prognostic and diagnostic systems that provide real-time predictions and recognition of scenario changes. The HHM methodology includes visual modeling techniques that can enhance stakeholders' understanding of shared states, resources, objectives and constraints among the interdependent and interconnected subsystems of smart manufacturing systems. In risk analysis, HHM is often paired with Risk Filtering, Ranking, and Management (RFRM). The RFRM process provides the users, (e.g., technology developers, original equipment manufacturers (OEMs), technology integrators, manufacturers), with the most critical risks to the objectives, which can be used to identify the most critical components and subsystems that would most benefit from a PHM system. A case study is presented in which HHM and RFRM are adapted for PHM in the context of an active manufacturing facility located in the United States. The methodologies help to identify the critical risks to the manufacturing process, and the major components and subsystems that would most benefit from a developed PHM system.

There are an extensive body of knowledge and some commercial products available for calculating prognostics, remaining useful life, and damage index parameters. The application of these technologies within the nuclear power community is still in its infancy. Online monitoring and condition-based maintenance is seeing increasing acceptance and deployment, and these activities provide the technological bases for expanding to add predictive/prognostics capabilities. In looking to deploy prognostics there are three key aspects of systems that are presented and discussed: (1) component/system/structure selection, (2) prognostic algorithms, and (3) prognostics architectures. Criteria are presented for component selection: feasibility, failure probability, consequences of failure, and benefits of the prognostics and health management (PHM) system. The basis and methods commonly used for prognostics algorithms are reviewed and summarized. Criteria for evaluating PHM architectures are presented: open, modular architecture; platform independence; graphical user interface for system development and/or results viewing; web enabled tools; scalability; and standards compatibility. Thirteen software products were identified and discussed in the context of being potentially useful for deployment in a PHM program applied to systems in a nuclear power plant (NPP). These products were evaluated by using information available from company websites, product brochures, fact sheets, scholarly publications, and direct communication with vendors. The thirteen products were classified into four groups of software: (1) research tools, (2) PHM system development tools, (3) deployable architectures, and (4) peripheral tools. Eight software tools fell into the deployable architectures category. Of those eight, only two employ all six modules of a full PHM system. Five systems did not offer prognostic estimates, and one system employed the full health monitoring suite but lacked operations and maintenance support. Each product is briefly described in Appendix A. Selection of the most appropriate software package for a particular application will depend on the chosen component, system, or structure. Ongoing research will determine the most appropriate choices for a successful demonstration of PHM systems in aging NPPs.

Reliability is an important issue to consider for mechanical systems. The state of art is regular checkup and maintenance to ensure normal operations. This is not good enough for safety-critical systems like gearboxes in vehicles and helicopters because the risk of system failure still exists, let alone the manpower and monetary cost required. Prognostics and health management (PHM) was first raised by the U.S. armed force, which should ideally be able to predict faults and schedule maintenance only when necessary by monitoring the system condition. In this paper, inspired by the idea of Built-In Self Test (BIST) in electronic systems, we propose a novel framework to fulfill the task of prognostics and health management with a set of smart sensors, consisting of embedded sensing elements, wireless communication modules and micro-controllers. Both the significance and challenges of the framework are discussed.

A valuable asset for the improvement of aviation maintenance is the correct assessment of the aircraft systems health condition, for a more accurate planning and execution of maintenance routines. As such, the creation of a Prognostic and Health Management (PHM) system, supported by Condition Based Maintenance (CBM) can bring important benefits to the aeronautical field. The ultimate goal of a PHM system is the correct prediction of the Remaining Useful Lifetime (RUL) of a certain aircraft system, using the sensors data extracted during flights. Nevertheless, a relevant stage in the PHM pipeline concerns the estimation of the system condition, expressed by the Health Indicator (HI). The HI value reflects the health condition of a specific aircraft system, which can possibly be affected by degradation, failures or external conditions occurred during flight time. Henceforth, due to the relevancy of the HI assessment for the accuracy of the PHM model, this paper aims to propose a new formulation for the HI computation, derived from raw anonymized data retrieved from different sensors within the aircraft system. The proposed formulation combines information from the different variables (like sensors) that have an impact on the overall system condition, by assigning a positive or negative weight to each variable depending on the influence on the system behaviour. The weights are determined based on the typical and standard data patterns. Thus, the estimated HI aims to reflect the number of hours of flight expected to be flown, based only on raw data extracted from the system. Furthermore, considering that the available sensors data is anonymized, a study of the relevancy of the different sensors features for the degradation assessment is performed, using specific metrics. Considering a scenario where the HI ground truth is unknown, the failure data of each aircraft system is used to evaluate and discuss the formulation suitability. The HI formulation is applied in real datasets, on the environmental systems of two wide body aircraft types.

With the continuous development of prognostics and health management (PHM) technology, it has been widely applied in satellite, and gradually developed into two directions: on-board PHM and on-ground PHM. For the high health management of the navigation satellite, we proposed and implemented a new on-ground PHM system. We further constructed a health assessment system from four aspects: the fault diagnosis, performance assessment, trend prognostic and life prediction. Subsequently, the health maintenance scheme is recommended based on the health assessment results. The system has been applied in the in-orbit management of navigation satellites, ensuring the healthy and stable service of all satellites, and 13 satellites have been in service beyond their lifetime. The successful application can provide reference for the promotion of navigation constellation health management.

The Prognostic and Health Management (PHM) system of an aircraft has complex structures and diverse functions. It is highly coupled with other systems, such as the avionics system and flight management system. The Model-based Systems Engineering (MBSE) method is effective to support the design and verification of the aircraft PHM system. As a powerful semantic web construction method, ontology has been widely used to express design information, such as the concepts and the relationships between them. However, traditional graphical MBSE models have a natural weakness in transforming into ontology. In this paper, a semantic MBSE method is proposed to support the transform of the ontology model. Firstly, according to the design characteristics of the aircraft PHM system, a meta-model library of the aircraft PHM system is developed to support the design and evaluation. An MBSE modeling method based on requirement analysis, function analysis, logical architecture design, and physic architecture design is applied in the PHM design process. Secondly, the semantic system modeling language KARMA based on “graph, object, property, point, relationship, role, and extension” (GOPPRRE) is used to transfer the graphical MBSE model to the semantic MBSE model, which can be easily transformed to an ontology model. Finally, an ontology based on semantic modeling is developed to describe the MBSE entities and to support MBSE design. In this paper, a case study of an aircraft fuel PHM system is carried out to validate the proposed method. Based on the developed meta-model library, a complete MBSE design process for the aircraft PHM system is realized. And then an ontology model supporting PHM system design is generated from the semantic MBSE model. The MBSE ontology provides a shareable capability to help designers communicate effectively. Quantitative analysis based on ontology is also provided to verify the complexity and scale of the MBSE design process. Moreover, logical reasoning ability can also be provided to support the early requirement traceability for MBSE design. In general, the case study results show the feasibility and effectiveness of the proposed method for the aircraft PHM system design.

The maintenance industry is undergoing a major transformation as it embraces the shift towards Industry 5.0. The focus of Industry 5.0 is on the integration of human intelligence with advanced technologies. It emphasizes interaction and collaboration between humans and machines and aims to combine the strengths of both. The efficiency of prognostics and health management (PHM) for maintenance in industrial contexts can be enhanced by improving this human-machine interaction and collaboration. This paper investigates the human-centric aspects, with a focus on PHM systems for facilitating the enablement of Industry 5.0 in maintenance. Acknowledging human as an active participant, this study explores their integral role in designing and developing PHM systems. The data collection for this study has been based on available literature, active and passive observations, and unstructured interviews and discussions with experienced industry professionals. As a result of the analysis of collected data, this study identifies and highlights potential areas for research and exploration. The research in these areas can advance the understanding and application of human-centric PHM strategies within Industry 5.0 in maintenance contexts. This is expected to improve the resilience and sustainability aspects of the industrial ecosystem and facilitate the shift towards Industry 5.0.

Airborne prognostics and system health management (PHM) system is a very important part of aircraft autonomous logistics support system. To achieve autonomous logistics support mode, improve aircraft operational readiness, reduce life cycle cost, PHM technology is one of the key technologies. Capabilities that airborne PHM system detect fault, isolate fault and forecast fault directly determine the effectiveness of the work of aircraft maintenance. It also influences aircraft economy, availability and security. Airborne PHM system adopts a hierarchical architecture. Using built-in test equipment (BITE) and sensors, PHM system collects data that reflect characterization of failures and faults trends. With the advanced algorithms and intelligent model to monitor, diagnose, predict and manage the status of the aircraft. Furthermore, accurate fault detection, isolation and projections are achieved. At Present, fault detection, isolation and predictive measurement and verification methods in MIL-STD2165, GJB2547, GJB2072 and GJB1909 have not fully applicable to the PHM system. In this paper, based on detail analysis of the relevant standards for airborne PHM system and airborne PHM system function and work flowchart, according to design features of airborne PHM system architecture and in accordance with the different levels of system design, index requirements of sensor-BITE, regional manager and aircraft platform manager are proposed respectively. The index system of airborne PHM system is established. At the same time, a verification method of airborne PHM system is derived. That is, “hierarchical verification, comprehensive evaluation” method. Utilizing validation results of underlayer indexes including the design capacity of board level and equipment level built-in test (BIT), fault diagnostic model of regional platform manager and so on, we validate top-level requirements of airborne PHM system by comprehensive evaluation.

According to the electrical power supply system analysis, a system of prognostic and health management (PHM) for the electrical power supply system is presented. The PHM system can accomplish the condition assessment of the key characteristics and device in the electrical power supply system without additional test equipment. The condition of rotating rectifier, contact resistance and so on can be evaluated by the data-driven approach. The architecture of the PHM system is easy to modify and update on the base of traditional aircraft electrical power supply system.

In view of the lack of design requirements and performance evaluation parameters in the prognostics and health management (PHM) system during the equipment development and procurement process, based on the study of the composition of the typical PHM system, the different functions of the PHM system at the member, area and platform levels are analyzed. Based on the functions and workflows of the system, the qualitative and quantitative capability parameter sets of the PHM system design are proposed. The quantitative evaluation methods are given for the key parameters of the diagnostic capability. Finally, the quantitative design requirements of fault diagnosis are given for the environmental control system PHM system. The problem of lack of PHM system performance evaluation parameter system in the design and verification phase is solved. This paper has certain reference value for the work of new equipment in design demonstration, verification and identification.

Prognostics and Health Management (PHM) is vital for complex engineering systems, yet its data-driven solutions are often hampered by the "small data problem"—a scarcity of labeled fault data in industrial settings. This limitation restricts the training and generalization of machine learning models and is compounded by varying operational conditions that reduce the relevance of historical data and pre-trained models. This research introduces a research framework to tackle these small data challenges in PHM. The primary objective is to develop a robust and adaptable PHM methodology by enhancing and synergistically integrating similarity-based Few-Shot Learning (FSL) with large-scale pre-trained time-series models. The research will focus on two main thrusts. First, it aims to improve the generalization capabilities of FSL frameworks by addressing limitations such as noise robustness, domain shift adaptability, and generalization to novel faults across diverse PHM domains. This involves developing noise-robust feature extraction, integrating domain adaptation techniques, and exploring expressive similarity metrics. Second, the study will investigate the effective adaptation of state-of-the-art pre-trained time-series models (e.g., TimesNet) for PHM tasks under data scarcity, focusing on efficient fine-tuning and synergistic integration with the enhanced FSL approaches. The author's prior success in a PHM data challenge using a similarity-based method for spacecraft systems provides preliminary validation. This research is expected to deliver an enhanced PHM framework for high-accuracy diagnostics with limited data, contributing generalized FSL models, systematic methods for leveraging pre-trained models in PHM, and advancing the practical deployment of intelligent PHM solutions.