Prognostics And Health Management Technology Research Articles

Research Progress and Development Trend of Prognostics and Health Management Key Technologies for Equipment Diesel Engine

The diesel engine, as the main power source of equipment, faces practical problems in the maintenance process, such as difficulty in fault location and a lack of preventive maintenance techniques. Currently, breakdown maintenance and cyclical preventive maintenance are the main means of maintenance support after a diesel engine failure, but these methods require professional maintenance personnel to carry out manual fault diagnosis, which is time-consuming. Prognostics and health management (PHM), as a new technology in the field of equipment maintenance support, has significant advantages in improving equipment reliability and safety, enhancing equipment maintenance support capability, and reducing maintenance support costs. In view of this, when introducing PHM into diesel engine maintenance support, the research progress and development trend of the key technologies of PHM for diesel engines are carried out with the objective of achieving precise maintenance and scientific management of diesel engines, and the key technologies demand traction. Firstly, the development history of PHM technology is reviewed, and its basic concept and main functions are introduced. Secondly, the system architecture of PHM for diesel engines is constructed, and its key technologies are summarized. Then, the research progress in the field of PHM for diesel engines is reviewed from four aspects: data acquisition, data processing, fault diagnosis, and health status assessment. Finally, the challenges faced by diesel engine PHM in engineering applications are analyzed, effective solutions to address these challenges are explored, and the future development trend is foreseen.

A Review on the Advanced Maintenance Approach for Achieving the Zero-Defect Manufacturing System

Recently, a revolutionary change is taking place in manufacturing and production systems thanks to the development of various advanced technologies such as IIoT (Industrial Internet of Things), CPPS (Cyber-Physical Production System), digital twins, big data analytics, AI (Artificial Intelligence), and so on. One of the change is that manufacturing and production systems are now trying to transform into the ZDM (Zero-Defect Manufacturing) system. For a manufacturing company, quality takes precedence over any other competitive factors, so the implementation of a ZDM system is very important. For the implementation of ZDM, many fundamental technologies are required. Among them, the advanced maintenance approach for the facilities/equipment of the manufacturing and production system is much more important because it could support the zero-defect and high-efficiency operation of manufacturing and production systems. The advanced maintenance approach, which is often called by various terms such as predictive maintenance, condition-based maintenance plus (CBM+), and PHM (Prognostics and Health Management), requires various interdisciplinary knowledge and systematic integration. In this study, we will review previous works mainly focusing on advanced maintenance subject among ZDM research works, and briefly discuss the challenging issues for applying PHM technologies to the ZDM.

회전기계를 위한 건전성 예측 및 관리 시스템 개발과 로터리 테이블에 적용

Recently, interest in Prognostics and Health management (PHM) has been increasing as an advanced technology of maintenance. PHM technology is a technology that allows equipment to check its condition and predict failures in advance. To realize PHM technology, it is important to implement artificial intelligence technology that diagnoses failures based on data. Vibration data is often used to diagnose the state of the rotating machine. Additionally, there have been many efforts to convert vibration data into 2D images to apply a convolutional neural network (CNN), which is emerging as a powerful algorithm in the image processing field, to vibration data. In this study, a series of PHM processes for acquiring data from a rotary machine and using it to check the condition of the machine were applied to the rotary table. Additionally, a study was conducted to introduce and compare two methodologies for converting vibration data into 2D images. Finally, a GUI program to implement the PHM process was developed.

Data-Driven Health Assessment in a Flight Control System under Uncertain Conditions

PHM technology plays an increasingly significant role in modern aviation condition-based maintenance. As an important part of prognostics and health management (PHM), a health assessment can effectively estimate the health status of a system and provide support for maintenance decision making. However, in actual conditions, various uncertain factors will amplify assessment errors and cause large fluctuations in assessment results. In this paper, uncertain factors are incorporated into flight control system health assessment modeling. First, four uncertain factors of health assessment characteristic parameters are quantified and described by the extended λ-PDF method to acquire their probability distribution function. Secondly, a Monte Carlo simulation (MCS) is used to simulate a flight control system health assessment process with uncertain factors. Thirdly, the probability distribution of the output health index is solved by the maximum entropy principle. Finally, the proposed model was verified with actual flight data. The comparison between assessment results with and without uncertain factors shows that a health assessment conducted under uncertain conditions can reduce the impact of the uncertainty of outliers on the assessment results and make the assessment results more stable; therefore, the false alarm rate can be reduced.

Prognostics and health management: A review from the perspectives of design, development and decision

Prognostics and health management (PHM) is an enabling technology used to maintain the reliable, efficient, economic and safe operation of engineering equipment, systems and structures. In the last 5 years, many PHM review works have been published for discussing and summarizing the state-of-the-art of different approaches and methods, usage modes and applications of PHM. This paper intends to enrich the PHM state-of-the-art by reviewing the PHM works from three different points of view: DEsign, DEvelopment and DEcision (DE3). We dig into the problem essence of the DE3 of PHM by reviewing the research work, extracting the research conclusions from 235 related publications, and exposing the current methodologies and solution frameworks for addressing the DE3 issues. We identify the gaps and challenges of existing PHM concerning the DE3, and point out issues and opportunities of future PHM researches. We expect this review work to provide clear directions for assisting the researchers and practitioners in advancing the PHM methodologies and maturing them into practical PHM technologies.

A nonlinear signal processing framework for rapid identification and diagnosis of structural freeplay

Structural freeplay due to loosened mechanical linkages is a discrete nonlinear event which occurs pseudo-routinely in modern aircraft, causing severe airframe vibration. This impacts fatigue life, and has serious implications for fleet management and Structural Health Monitoring (SHM). While the concepts which drive SHM for aircraft are traditionally based on reactive procedures, we are currently observing a major shift towards actionable and pro-active condition-based maintenance, known as Prognostics and Health Management (PHM), to significantly reduce fleet sustainment costs. Given this current paradigm shift, there is a demand for the development of novel strategies to address decades old SHM problems, where the adaptation of existing methods or the development of new and innovative techniques both play critical roles. In this paper a signal processing framework is presented, based upon well-established nonlinear system identification methods, to rapidly diagnose structural freeplay in aircraft systems with a focus on the requirements of PHM technology. The framework exploits the nonlinear dynamical characteristics of the structural freeplay anomaly in a transonic aeroelastic system by specifically targeting rich bilinear signatures that are encoded in time-domain sensory outputs, via the Higher-Order Spectra (HOS) and the Empirical Mode Decomposition (EMD). The characteristic freeplay signatures which were initially extracted from computational transonic aeroelastic models are shown to be analogous in a transonic flight-test case-study (an all-movable horizontal tail with actuator freeplay), presenting a rare and important opportunity to verify the practical freeplay identification research. Once verified, a comprehensive understanding of the fundamental bilinear signatures allows the HOS and EMD to be adapted and refined towards a structured freeplay diagnosis framework. Using the extensive flight-test dataset as a case study, it is shown that the freeplay location and magnitude information can be extracted with a high level of robustness, verified by making consistent predictions over a period of three years and several maintenance cycles, with a large variation in Mach number and angle-of-attack (predominantly high angle maneuvers). The paper is intended to communicate the fundamental principles and significance of the data-driven framework, highlighting revisiting and adapting existing well-established nonlinear identification tools, it is possible to address the requirements of contemporary SHM, although practical implementation requires ongoing research. Limitations of the data-driven approach are discussed, predominantly related to data acquisition requirements.

Joint Special Issue on PHM for Aerospace Systems

Prognostics and health management (PHM) is becoming one of the most popular topics for research and development in the aviation industry. The reasons for this are varied, but one of the main ones is that PHM affords the operator with a way to reduce lifecycle operating costs without necessarily adding expensive accessories that might need to be certified. Many of the papers in this special issue discuss PHM techniques that are based on post flight, or off-board, data processing that adds benefits without additional regulatory constraints. On the other hand, regulators themselves are keenly looking at changing regulations to allow more PHM equipment on board and to allow suppliers and operators to gain maintenance credits from their use. All in all, it is a exciting time to be a engineering working on these topics in the aviation sector. While progress is being made in developing better sensors, models, and analytical methods, the field of aviation itself is changing rapidly. It is seeking to become more sustainable by increasing propulsion efficiencies, it has many players looking to develop and commercialize electrical systems, and it is fast undertaking a digital transformation of the entire ecosystem. In response, system developers and researchers in the field are working on a number of key technologies and methodologies to solve some of the issues that these changes have wrought. This special issue on PHM for Aerospace Systems provides a forum to discuss recent advances pertaining to papers that address topics in this field with an emphasis on Prognostics and Health Management. These topics include advances in anomaly detection and diagnostics, uncertainty management for prognostics, resilient design for fault-tolerant operations, systematic evaluation of PHM for maintenance processes, assessment of retrofit solutions for particular aircraft systems, PHM informed decision-making for safe operations of autonomous aerial systems, impact of noise on prognostic performance, and lessons learned from trends in aircraft fatigue failure accidents for Structural Health Monitoring. We hope that this special joint issue of the International Journal of PHM and SAE International Journal of Aerospace will increase the awareness about innovations in the development of PHM technologies in aerospace and believe that this inspires researchers to focus more attention on this important topic. We would like to express our sincere thanks to all the reviewers of this issue who have generously contributed their valuable time and effort to ensure that it lives up to the Journal’s high quality.

Performance Metrics for Artificial Intelligence (AI) Algorithms Adopted in Prognostics and Health Management (PHM) of Mechanical Systems

Research into the use of artificial intelligence (AI) algorithms within the field of prognostics and health management (PHM), in particular for predicting the remaining useful life (RUL) of mechanical systems that are subject to condition monitoring, has gained widespread attention in recent years. It is important to establish confidence levels for RUL predictions, so as to aid operators as well as regulators in making informed decisions regarding maintenance and asset life-cycle planning. Over the past decade, many researchers have devised indicators or metrics for determining the performance of AI algorithms in RUL prediction. While most of the popularly used metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), etc. were adapted from other applications, some bespoke metrics are designed and intended specifically for use in PHM research. This study provides a synopsis of key performance indicators (KPIs) that are applied to AI-driven PHM technologies of mechanical systems. It presents details of the application scenarios, suitability of using a particular metric in different scenarios, the pros and cons of each metric, the trade-offs that may need to be made in choosing one metric over another, and some other factors that engineers should take into account when applying the metrics.

Advancing Measurement Science to Assess Monitoring, Diagnostics, and Prognostics for Manufacturing Robotics

Unexpected equipment downtime is a 'pain point' for manufacturers, especially in that this event usually translates to financial losses. To minimize this pain point, manufacturers are developing new health monitoring, diagnostic, prognostic, and maintenance (collectively known as prognostics and health management (PHM)) techniques to advance the state-of-the-art in their maintenance strategies. The manufacturing community has a wide-range of needs with respect to the advancement and integration of PHM technologies to enhance manufacturing robotic system capabilities. Numerous researchers, including personnel from the National Institute of Standards and Technology (NIST), have identified a broad landscape of barriers and challenges to advancing PHM technologies. One such challenge is the verification and validation of PHM technology through the development of performance metrics, test methods, reference datasets, and supporting tools. Besides documenting and presenting the research landscape, NIST personnel are actively researching PHM for robotics to promote the development of innovative sensing technology and prognostic decision algorithms and to produce a positional accuracy test method that emphasizes the identification of static and dynamic positional accuracy. The test method development will provide manufacturers with a methodology that will allow them to quickly assess the positional health of their robot systems along with supporting the verification and validation of PHM techniques for the robot system.

Present Status and Future Growth of Advanced Maintenance Technology and Strategy in US Manufacturing

The goals of this paper are to 1) examine the current practices of diagnostics, prognostics, and maintenance employed by United States (U.S.) manufacturers to achieve productivity and quality targets and 2) to understand the present level of maintenance technologies and strategies that are being incorporated into these practices. A study is performed to contrast the impact of various industry-specific factors on the effectiveness and profitability of the implementation of prognostics and health management technologies, and maintenance strategies using both surveys and case studies on a sample of U.S. manufacturing firms ranging from small to mid-sized enterprises (SMEs) to large-sized manufacturing enterprises in various industries. The results obtained provide important insights on the different impacts of specific factors on the successful adoption of these technologies between SMEs and large manufacturing enterprises. The varying degrees of success with respect to current maintenance programs highlight the opportunity for larger manufacturers to improve maintenance practices and consider the use of advanced prognostics and health management (PHM) technology. This paper also provides the existing gaps, barriers, future trends, and roadmaps for manufacturing PHM technology and maintenance strategy.

Present Situation, Challenge and Future of Health Management about Complex Equipment

Recently, the public has focused on the safety and reliability of complex equipment such as aerospace. Health management about complex equipment contains state detection, fault prognostics, fault diagnosis and so on, and is the guarantee for accomplishment of space missions and the long-term operation of in-orbit spacecraft. Under the background, the development of prognostics and health management (PHM) technology, aiming at improving equipment safety and reliability, which can eliminate faults in the embryonic state, has a profound significance for condition monitoring and in-orbit maintenance of aerospace equipment. By analyzing the research status and application of PHM technology both at home and abroad, the manuscript majorly addressed the PHM technical framework and key technologies about spacecraft. In the meanwhile, integrated with cloud service platform for equipment management, the development tendency of PHM was forecast. At last, the existing challenges of PHM technology was expounded and corresponding countermeasures were put forward.

An Adaptive Maintenance Policy With Nonlinear Degradation Modeling Based on Prognostic Information

Prognostics and health management (PHM) technology is an extremely important research focus in the field of reliability engineering. The ultimate goal of applying PHM technology is health management. Aiming at nonlinear degradation systems, an adaptive maintenance policy based on prognostic information is proposed herein. First, a nonlinear degradation model with an adaptive updating mechanism is used to predict the remaining useful life (RUL) of the degrading system. Then, based on the predicted RUL distribution, a multi-objective optimization model is established to address the trade-off between operating cost and availability through a constructed decision boundary, instead of the approach used in previous studies, which considers cost as a single indicator. Using this multi-objective optimization model, an adaptive decision criterion is proposed to evaluate the advantages and disadvantages of different replacement policies, in order to determine the optimal replacement time and dynamic condition monitoring (CM) interval of the degrading system. Finally, an example of gyroscope in an inertial navigation system (INS) is used to verify the effectiveness of the proposed method.

Prognostics and health management technology for radar system

Information-based war in the future has a higher requirement to the maintenance and support ability of radar system. Prognostics and Health Management(PHM) technology represents the research hotspot of maintenance system, and following key techniques need to be resolved to research on the radar PHM technology such as the acquirement and selection of health information and fault signs of a radar’s electronical components, mass data warehousing and mining, fusion of multi-source test data and multi-field characteristic information, failure model building and forecasting, automatic decision-making on maintenance, and at the same time improving the self built-in test abilities of radar’s components based on the optimization of Design For Testability(DFT). The radar PHM technology has the trend of “built-in to integrate”, “together with DFT” and “long-distance and distributed”. However, subjected to radar’s complexity and current PHM technique level, radar PHM engineering still meets many challenges, but has bright future.

Degrader Analysis for Diagnostic and Predictive Capabilities: A Demonstration of Progress in DoD CBM+ Initiatives

Abstract This paper presents a modified reliability centered maintenance (RCM) methodology developed by The Applied Research Laboratory at The Pennsylvania State University (ARL Penn State) to meet challenges in decreasing life cycle sustainment costs for critical Naval assets. The focus of this paper is on the requirements for the development of the on-board Prognostics and Health Management (PHM) system with a discussion on the implementation progress for two systems: the high pressure air compressor (HPAC), and the advanced carbon dioxide removal unit (ACRU). Recent Department of Defense (DoD) guidance calls for implementing Condition Based Maintenance (CBM) as an alternative to traditional reactive and preventative maintenance strategies that rely on regular and active participation from subject matter experts to evaluate the health condition of critical systems. The RCM based degrader analysis utilizes data from multiple sources to provide a path for selecting systems and components most likely to benefit from the implementation of diagnostic and predictive capabilities for monitoring and managing failure modes by determining various options of possible CBM system designs that provide the highest potential ROI. Sensor data collected by the PHM system can be used with machine learning applications to develop failure mode predictive algorithms with greatest benefit in terms of performance, sustainment costs, and increasing platform operational availability. The approach supports traditional maintenance strategy development by assessing the financial benefit of the PHM technology implementation with promising potential for many industrial and military complex adaptive system applications.

Data-driven Application of PHM to Asset Strategies

There are many benefits from implementing a prognostics and health management (PHM) initiative in an industrial facility, such as realizing potentials from reducing unplanned downtime and increased asset efficiency. Many industrial companies would like to take advantage of PHM technologies and algorithms to meet their business objectives, but identifying how to get started can be a daunting challenge. The classical approach is to begin with a Reliability Centered Maintenance (RCM) program supported by failure modes and effects analysis (FMEA) where all possible failure modes, their risks, and mitigating actions are evaluated in the context of asset function. In this framework, application of PHM technologies is viewed as a maintenance strategy effective at mitigating certain failure modes in specific cases that are both feasible and costeffective. However, there are many challenges and limitations to traditional RCM where data-driven analytics embedded in these work processes can help overcome and/or automate. On the other hand, the use of data-driven approaches introduces new challenges surrounding available data, data quality, and identifying numerical methods that are scalable across large datasets. In this paper, we present a case study applied to historical maintenance data for identifying and prioritizing where to start a PHM initiative, and discuss the work processes and various challenges encountered when embedding data analytics in classical reliability approaches.

Hierarchical modeling of a manufacturing work cell to promote contextualized PHM information across multiple levels

With the ever-increasing demand for reconfigurability and modularity in manufacturing, industrial work cells are increasingly integrating newer and more diverse technologies to not only support the production of a wider range of parts, but also ease the repair or replacement of faulty systems and components. Complex relationships between different elements of a work cell originate from the integration of multiple layers of hardware and software needed to successfully execute the complicated manufacturing processes. Much work within the science of PHM (prognostics and health management) has been dedicated towards the management of some of this complexity via monitoring, diagnostic, and prognostic technologies. The strategic application of PHM technologies has been shown to effectively reduce equipment/process downtime and lower maintenance costs. Part of the challenge of PHM, particularly for manufacturers, is to know exactly where, and how to apply PHM within their work cell operations to gain the maximum actionable information. This problem is further compounded for small to medium-sized manufacturers (SMMs) who are typically limited in their resources and investment capital. Effectively designing and implementing PHM requires a fundamental understanding of the overall work cell and its constituent physical components and sub-components. Likewise, understanding the relationships between these physical elements and how these elements relate to one another is critical to determining how the degradation of one element will impact the degradation of another. The National Institute of Standards and Technology (NIST) is researching various PHM technologies that aim to enhance decision-making at the factory floor to promote smarter maintenance and control strategies. Part of NIST’s research focuses on the decomposition of a work cell into a hierarchical structure to understand the physical and functional relationships among the overall system’s critical elements. This physical and functional decomposition is a necessity to promote a meaningful rollup of diagnostic and prognostic information from the lower levels to the higher levels of the hierarchy. The hierarchy seeks to encapsulate how the overall system, and its subsequent components, will behave when an element within the system is compromised or begins to fail. Neighboring components and sub-components could be subject to the ‘domino effect’ or the ‘ripple effect’, making diagnosing the root cause of a cascade alarms difficult without some reflective model of the system. This paper presents NIST’s efforts to develop a hierarchical decomposition methodology that will support PHM design and implementation within a complex work cell.

A Review on Prognostics and Health Management

Abstract The health management of complex equipment is crucial to ensure the reliability, maintainability and safety of complex equipment. This paper introduces the basic concept and research connotation of prognostics and health management (PHM), analyzes the significance of PHM technology in equipment maintenance. For the fault prediction architecture and method, this paper focuses on the discussion, analysis, and summarizes the current research’s hot spots and existing technical difficulties. Finally, the future research directions are also prospected.

Preliminary Study of PHM System Based on Data Driven

Complex mechanical and electrical equipment contains a large amount of data with the implicit information. According to the development of PHM (Prognostics and Health Management) technology at home and abroad, and the wide application prospects of data driving methods, the overall framework of data driven PHM system for complex electromechanical equipment was designed. The data driven PHM implementation process of the complex mechanical and electrical equipment was described step by step, which provides important theoretical significance and application value for the PHM research of the complex mechanical and electrical equipment. Finally, the development trend and research challenges of data driven PHM method were analyzed.

The present status and future growth of maintenance in US manufacturing: results from a pilot survey.

A research study was conducted (1) to examine the practices employed by US manufacturers to achieve productivity goals and (2) to understand what level of intelligent maintenance technologies and strategies are being incorporated into these practices. This study found that the effectiveness and choice of maintenance strategy were strongly correlated to the size of the manufacturing enterprise; there were large differences in adoption of advanced maintenance practices and diagnostics and prognostics technologies between small and medium-sized enterprises (SMEs). Despite their greater adoption of maintenance practices and technologies, large manufacturing organizations have had only modest success with respect to diagnostics and prognostics and preventive maintenance projects. The varying degrees of success with respect to preventative maintenance programs highlight the opportunity for larger manufacturers to improve their maintenance practices and use of advanced prognostics and health management (PHM) technology. The future outlook for manufacturing PHM technology among the manufacturing organizations considered in this study was overwhelmingly positive; many manufacturing organizations have current and planned projects in this area. Given the current modest state of implementation and positive outlook for this technology, gaps, future trends, and roadmaps for manufacturing PHM and maintenance strategy are presented.

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.