Prognostics And Health Management System Research Articles

Standards for Prognostics and Health Management (PHM) Techniques within Manufacturing Operations


 
 
 Prognostics and health management (PHM) technologies reduce time and costs for maintenance of products or processes through efficient and cost-effective diagnostic and prognostic activities. These activities aim to provide actionable information to enable intelligent decision-making for improved performance, safety, reliability, and maintainability. Thoughtful PHM techniques can have a dramatic impact on manufacturing operations, and standards for PHM system development, data collection and analysis techniques, data management, system training, and software interoperability need to exist for manufacturing. The National Institute of Standards and Technology (NIST) conducted a survey of PHM-related standards applicable to manufacturing systems to determine the needs addressed by such standards, the extent of these standards, and any commonalities as well as potential gaps among the documents. Standards from various national and international organizations are summarized, including those from the International Electrotechnical Commission, the International Organization for Standardization, and SAE International. Finally, areas for future PHM-related standards development are identified.
 
 

Designing for Human-Centred Decision Support Systems in PHM

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.

Improving Aircraft Engines Prognostics and Health Management via Anticipated Model-Based Validation of Health Indicators

The aircraft engines manufacturing industry is subjected to many dependability constraints from certification authorities and economic background. In particular, the costs induced by unscheduled maintenance and delays and cancellations impose to ensure a minimum level of availability. For this purpose, Prognostics and Health Management (PHM) is used as a means to perform online periodic assessment of the engines’ health status. The whole PHM methodology is based on the processing of some variables reflecting the system’s health status named Health Indicators. The collecting of HI is an on-board embedded task which has to be specified before the entry into service for matters of retrofit costs. However, the current development methodology of PHM systems is considered as a marginal task in the industry and it is observed that most of the time, the set of HI is defined too late and only in a qualitative way. In this paper, the authors propose a novel development methodology for PHM systems centered on an anticipated model-based validation of HI. This validation is based on the use of uncertainties propagation to simulate the distributions of HI including the randomness of parameters. The paper defines also some performance metrics and criteria for the validation of the HI set. Eventually, the methodology is applied to the development of a PHM solution for an aircraft engine actuation loop. It reveals a lack of performance of the original set of HI and allows defining new ones in order to meet the specifications before the entry into service.

Distilling the Verification Process for Prognostics Algorithms


 
 
 The goal of prognostics and health management (PHM) systems is to ensure system safety, and reduce downtime and maintenance costs. It is important that a PHM system is verified and validated before it can be successfully deployed. Prognostics algorithms are integral parts of PHM systems. This paper investigates a systematic process of verification of such prognostics algorithms. To this end, first, this paper distinguishes between technology maturation and product development. Then, the paper describes the verification process for a prognostics algorithm as it moves up to higher maturity levels. This process is shown to be an iterative process where verification activities are interleaved with validation activities at each maturation level. In this work, we adopt the concept of technology readiness levels (TRLs) to represent the different maturity levels of a prognostics algorithm. It is shown that at each TRL, the verification of a prognostics algorithm de- pends on verifying the different components of the algorithm according to the requirements laid out by the PHM system that adopts this prognostics algorithm. Finally, using simplified examples, the systematic process for verifying a prognos- tics algorithm is demonstrated as the prognostics algorithm moves up TRLs.
 
 

Test selection and optimization for PHM based on failure evolution mechanism model

The test selection and optimization (TSO) can improve the abilities of fault diagnosis, prognosis and health-state evaluation for prognostics and health management (PHM) systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability (DFT) to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propagation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model (FEMM) for PHM systems is built up. This model describes the fault evolutiontest dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of inherent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and minimize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm (ASAGA) is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.

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

Much research has been conducted in prognostics and health management (PHM), an emerging field in mechanical engineering that is gaining interest from both academia and industry. Most of these efforts have been in the area of machinery PHM, resulting in the development of many algorithms for this particular application. The majority of these algorithms concentrate on applications involving common rotary machinery components, such as bearings and gears. Knowledge of this prior work is a necessity for any future research efforts to be conducted; however, there has not been a comprehensive overview that details previous and on-going efforts in PHM. In addition, a systematic method for developing and deploying a PHM system has yet to be established. Such a method would enable rapid customization and integration of PHM systems for diverse applications. To address these gaps, this paper provides a comprehensive review of the PHM field, followed by an introduction of a systematic PHM design methodology, 5S methodology, for converting data to prognostics information. This methodology includes procedures for identifying critical components, as well as tools for selecting the most appropriate algorithms for specific applications. Visualization tools are presented for displaying prognostics information in an appropriate fashion for quick and accurate decision making. Industrial case studies are included in this paper to show how this methodology can help in the design of an effective PHM system.

A Review of Current Prognostics and Health Management System Related Standards

Prognostics and health management(PHM) is a promising technology to reduce the overall life cycle cost and improve flight readiness in the civil and military aviation field. As the prognostic algorithms are applied into monitoring condition of aircraft structures, avionics, wiring, control actuators, power supplies, propulsion systems and etc, more attention has been focused on establishing a common standard to validate whether the PHM algorithms or systems can reach the design requirements and compare the efficiency of the diagnostics and prognostics algorithms. Currently, there’re many standards in the diagnostics and test regions, such as condition monitoring and diagnosis standard in ISO, the engine health management standards in SAE and so on. All these PHM related standards may help developing the new standard. First, this paper identifies the specification of PHM standards from different applications. And the state-of-the-art in PHM related standards is also given. Second, the current PHM-related standards in various standardizations and agencies are overviewed and analyzed according to the CBM, PHM, IVHM and HUMS classification; Lastly, some suggestions are given for PHM standardization development, and the focus of the future work and directions are also proposed.

Methodology and Framework of a Cloud-Based Prognostics and Health Management System for Manufacturing Industry

Prognostics and Health Management (PHM) as a research discipline has been extensively studied in recent years, with many sophisticated techniques and intelligent algorithms developed for machinery data analysis, health assessment and decision making. PHM solutions for typical components such as roller bearing and machine tool have grown and proved to be reliable enough for industrial application. However utilization of PHM solutions in industry is still much limited due to high research, development and implementation costs. Such limitations are more severe in smaller scale factories and workshops where noample resource is available for implementing PHM systems. Efforts have been made to tackle such issue, for instance, Watchdog Agent® Toolbox developed by the IMS Center enables faster deployment of PHM solutions through algorithm modularization and standardization. With advantages that can be deliveredwith the emerging cloud computing paradigm, a cloud-based prognostics and health management system for manufacturing industry has been developed based on Watchdog Agent® tools and the ideology of PHM as a Service. In addition to traditional data acquisition and management functions in a machine conditionmonitoring system, the cloud based PHM platform is able to further provide on-demand, customizable and low-cost data analysis service. Machinery data accumulated within the cloud system further enables more advanced services such as machine-to-machine comparison, data mining and knowledge discovery.

Multiple optimized online support vector regression for adaptive time series prediction

Data-driven prognostics based on sensor or historical test data have become appropriate prediction means in prognostics and health management (PHM) application. However, most traditional data-driven prognostics methods are off-line which would be seriously limited in many PHM systems needed on-line predicting or real-time processing. Furthermore, even in some on-line prediction algorithms such as Online Support Vector Regression (Online SVR) and Incremental learning algorithm, there are conflicts and trade-offs between prediction efficiency and accuracy. Therefore, in different PHM applications, prognostics algorithms should be on-line, flexible and adaptive to balance the prediction efficiency and accuracy. An on-line adaptive data-driven prognostics strategy is proposed with five various optimized on-line prediction algorithms based on Online SVR. These five algorithms are improved with kernel combination and sample reduction to realize higher precision and efficiency. These algorithms can achieve more accurate results by data pre-processing and model optimization, moreover, faster operating speed and lower computational complexity can be obtained by optimization of training process with on-line data reduction. With these different improved Online SVR methods, varies of prediction with different precision and efficiency demands could be fulfilled by an adaptive strategy. To evaluate the proposed prognostics strategy, we have executed simulation experiments with Tennessee Eastman (TE) process. In addition, the prediction strategies are also applied and evaluated by traffic mobile communication data from China Mobile Communications Corporation Heilongjiang Co., Ltd. Experiments and test results prove its effectiveness and confirm that the algorithms can be effectively applied to the on-line status prediction with increased performance in both precision and efficiency.

A novel approach of testability modeling and analysis for PHM systems based on failure evolution mechanism

Prognostics and health management (PHM) significantly improves system availability and reliability, and reduces the cost of system operations. Design for testability (DFT) developed concurrently with system design is an important way to improve PHM capability. Testability modeling and analysis are the foundation of DFT. This paper proposes a novel approach of testability modeling and analysis based on failure evolution mechanisms. At the component level, the fault progression-related information of each unit under test (UUT) in a system is obtained by means of failure modes, evolution mechanisms, effects and criticality analysis (FMEMECA), and then the failure-symptom dependency can be generated. At the system level, the dynamic attributes of UUTs are assigned by using the bond graph methodology, and then the symptom-test dependency can be obtained by means of the functional flow method. Based on the failure-symptom and symptom-test dependencies, testability analysis for PHM systems can be realized. A shunt motor is used to verify the application of the approach proposed in this paper. Experimental results show that this approach is able to be applied to testability modeling and analysis for PHM systems very well, and the analysis results can provide a guide for engineers to design for testability in order to improve PHM performance.

装备故障预测与健康管理体系结构及其关键技术研究

故障预测和健康管理（PHM）技术是复杂的装备系统采用的便于测试和维修诊断的一种革新方案，是实现其经济可承受性的关键技术，本文结合PHM技术在降低装备保障维修费用方面的明显优势，在深入探讨了PHM技术的概念和内涵的基础上，建立了PHM系统的开放式体系结构框架，并对框架中包含的七个模块进行了分析；概述了PHM技术的研究和应用现状，重点研究了实现装备故障预测与健康管理的关键技术和主要实现方法，包括传感器与信号处理技术、故障诊断与状态监测技术、健康性能评估技术、状态预测技术和智能决策和健康管理技术等，最后总结了PHM技术的特点和发展趋势。Fault prognostics and health management (PHM) technology is an innovative solution for the complex equipment system which is convenient to test and repair in the diagnosis and it is the key technology to achieve economic affordability. Combining the advantage of PHM technology in reducing equipment maintenance costs, on the basis of discussing the concept and connotation of PHM technology, this paper establishes and analyzes open architecture framework which contains seven modules for PHM system. Also, PHM technology research and application are summarized with the focus on the implementation method and key technology for equipment PHM, including the sensor and signal processing technology, fault diagnosis, condition monitoring technology, health & performance evaluation technology, condition forecasting technology, intelligent decision technology and health management technology, etc. Finally, the characteristics and development trend of PHM technology are summarized.

Framework of Designing an Adaptive and Multi-Regime Prognostics and Health Management for Wind Turbine Reliability and Efficiency Improvement

Wind turbine systems are increasing in technical complexity, and tasked with operating and degrading in highly dynamic and unpredictable conditions. Sustaining the reliability of such systems is a complex and difficult task. In spite of extensive efforts, current prognostics and health management (PHM) methodologies face many challenges, due to the complexity of the degradation process and the dynamic operating conditions of a wind turbine. This research proposed a novel adaptive and multi-regime prognostics and health management (PHM) approach with the aim to tackle the challenges of traditional methods. With this approach, a scientific and systematic solution is provided for health assessment, diagnosis and prognosis of critical components of wind turbines under varying environmental, operational and aging processes. The system is also capable of adaptively selecting the tools suitable for a component under a certain health status and a specific operating condition. The adopted relevant health assessment, diagnosis and prognosis tools and techniques for wind turbines are warranted by the intensive research of PHM models by the IMS center for common rotary machinery components. Some sub-procedures, such as information reconstruction, regime clustering approach and the prognostics of rotating elements, were validated by the best score performance in PHM Data Challenge 2008 (student group) and 2009 (professional group). The success of the proposed wind turbine PHM system would greatly benefit current wind turbine industry.

Research on Prognostics and Health Management System for Self-Propelled Gun

Considering the encapsulation, tremendous working stress, complicated fault mechanism and process, an object-oriented architecture Prognostics and Health Management (PHM) system of Self-Propelled Gun is put forward to solve the difficulties in modern equipments’ maintenance decision and safeguard. The relative key techniques, such as fault characteristics extraction, system identification, data fusion among sensors, adaptive fault thresholds setting, intelligent fault inference mechanism and fault prediction mechanism are discussed in detail. Above functional modules compose the PHM system. Their mutual relationships and data flow are shown as architecture graph, which is the basis of PHM system establishment. Simulation verification experiments indicate that the proposed architecture can satisfy the requirements of standardization, hierarchy and evolvement, as well as reducing the maintenance cost and difficulties.

Prognostics Assessment Using Fleet-wide Ontology

Large complex systems, such as power plants, ships and aircraft, are composed of multiple systems, subsystems and components. When they are considered as embedded in operating systems such as a fleet, mission readiness and maintenance management issues are raised. PHM (Prognostics and Health Management) plays a key role in controlling the performance level of such systems, at least on the basis of adapted PHM strategies and system developments. Moreover considering a fleet implies to provide managers and engineers a relevant synthesis of information and to keep this information updated in terms of the global health of the fleet as well as the current status of their maintenance efforts. In order to achieve PHM at a fleet level, it is thus necessary to manage relevant knowledge arising from both modeling and monitoring of the fleet. In that way, this paper presents a knowledge structuring scheme based on ontologies for fleet PHM management applied to marine domain, with emphasis on prognostics modeling.

A Technical Framework and Roadmap of Embedded Diagnostics and Prognostics for Complex Mechanical Systems in Prognostics and Health Management Systems

Prognostics and Health Management (PHM) technologies have emerged as a key enabler to provide early indications of system faults and perform predictive maintenance actions. Implementation of a PHM system depends on accurately acquiring in real time the present and estimated future health states of a system. For electronic systems, built-in-test (BIT) makes it not difficult to achieve these goals. However, reliable prognostics capability is still a bottle-neck problem for mechanical systems due to a lack of proper on-line sensors. Recent advancements in sensors and micro- electronics technologies have brought about a novel way out for complex mechanical systems, which is called embedded diagnostics and prognostics (ED/EP). ED/EP can provide real-time present condition information and future health states by integrating micro-sensors into mechanical structures when designing and manufacturing, so ED/EP has a revolutionary progress compared to traditional mechanical fault diagnostic and prognostic ways. But how to study ED/EP for complex mechanical systems has not been focused so far. This paper explores the challenges and needs of efforts to implement ED/EP technologies. In particular, this paper presents a technical framework and roadmap of ED/EP for complex mechanical systems. The framework is based on the methodology of system integration and parallel design, which includes six key elements (embedded sensors, embedded sensing design, embedded sensors placement, embedded signals transmission, ED/EP algorithms, and embedded self-power). Relationships among these key elements are outlined, and they should be considered simultaneously when designing a complex mechanical system. Technical challenges of each key element are emphasized, and the corresponding existed or potential solutions are summarized in detail. Then a suggested roadmap of ED/EP for complex mechanical systems is brought forward according to potential advancements in related areas, which can be divided into three different stages: embedded diagnostics, embedded prognostics, and system integration. In the end, the presented framework is exemplified with a gearbox.

Testability Design of the PHM System for Aero-Engines

PHM (Prognostics and Health Management) for Aero-engines is an effective technical approach to balance the economy and safety of the flight in the total life cycle. In this paper, we mainly analyze the popular issues in the process of designing PHM system for aero-engines including the testability design concept, the scheme of condition monitoring and the utilization extent of condition information. Then presents some useful solutions and advices for the testability design respectively; and analyzes the influence of testability on health management strategies and the main source of uncertainty; then propose a roadmap for making test program based on the PHM requirements and evaluating test program, for improve the utilizing degree of monitoring information, we lastly presented common data fusion methods and some typical examples is illustrated.

Fleet-wide health management architecture

Large complex systems, such as power plants, ships and aircraft, are composed of multiple systems, subsystems and components. When they are considered as embedded in system operating as a fleet, it raises mission readiness and maintenance management issues. PHM (Prognostics and Health Management) plays a key role for controlling the performance level of such systems, at least on the basis of adapted PHM strategies and system developments. However, considering a fleet implies to provide managers and engineers with a relevant synthesis of information and keep it updated regarding both the global health of the fleet and the current status of their maintenance efforts. For achieving PHM at a fleet level, it is thus necessary to manage relevant corresponding knowledge arising both from modeling and monitoring of the fleet. In that way, this paper presents a knowledge structuring scheme for fleet PHM management applied to marine domain.

Design of Information Terminal for PHM Based on Multi-MEMS Sensors

In order to accomplish the Prognostics and Health Management System(PHM) for guided missile, sensor network node of storage and conveyance’s environment’s monitoring based on multi-MEMS sensors is designed. Centering on the low power consumption singlechip, microsensor collecting the information of temperature, humidity, acceleration(calculate quiver), and magnetic field intensity(calculate posture), realizing the wireless communication between the node and the upper management system (ARM controller) with the radio frequency send-receive chip nRF2401. This node has the characteristic of small volume, easy to layout, low power consumption, continuous operation for long time and so on. It preferably realized the function of information terminal of PHM system.

Making use of prognostics health management information for aerospace spare components logistics network optimisation

Although research has evolved significantly over the last decade, there are still a large number of Grand Challenges confronting modelling, model deployment, and model-based decision making of large-scale complex Discrete Event Logistics Systems (DELS) to be tackled, as identified and reviewed during a Dagstuhl workshop in March 2010. This paper illustrates how several of these challenges are already being addressed, based on a series of case studies from the Aerospace Spare Components Logistics domain, where consolidated operational Prognostics and Health Management (PHM) information can be used for tactical planning and optimisation of spare components logistics networks. In this setting, the growing potential of PHM technology to facilitate the maintenance and support of commercial and military aircraft emphasises the need for tools to determine the impacts and benefits of a PHM system. To achieve this, the prognostics parameters and related logistics policies were identified, modelled, and subsequently incorporated into a simulation-based decision support framework.

Cost-benefit Model for PHM

Abstract One goal of implementing PHM (Prognostic and Health Management) is to lower the cost of maintenance, and it is highly recommended to implement the model that is adequate for providing the right tool for evaluating PHM system. The implementing PHM will lead some cost and achieve some benefits. There are many types of costs, such as PHM implementation cost, the cost of buying or developing, integrating, and sustaining a PHM technology. The benefit can be quantified in both economic and in non-economic terms. In addition, benefits should be evaluated from a total enterprise viewpoint. This paper presents a methodology to perform cost-benefit analysis from the view of lower costs, analysis the main factors of implementing PHM and designs the Cost-Benefit Model. Using this model, we calculate costs and benefits from PHM together in integrating way according to the different scenarios and choose the right method to get the goal of lowering costs.