Using a high fidelity CCGT simulator for building prognostic systems

Pressure to reduce maintenance costs in power utilities has resulted in growing interest in prognostic monitoring systems. Accurate prediction of the occurrence of faults and failures would result not only in improved system maintenance schedules but also in improved availability and system efficiency. The desire for such a system has driven research into the emerging field of prognostics for complex systems. At the same time there is a general move towards implementing high fidelity simulators of complex systems especially within the power generation field, with the nuclear power industry taking the lead. Whilst the simulators mainly function in a training capacity, the high fidelity of the simulations can also allow representative data to be gathered. Using simulators in this way enables systems and components to be damaged, run to failure and reset all without cost or danger to personnel as well as allowing fault scenarios to be run faster than real time. Consequently, this allows failure data to be gathered which is normally otherwise unavailable or limited, enabling analysis and research of fault progression in critical and high value systems. This paper presents a case study of utilising a high fidelity industrial Combined Cycle Gas Turbine (CCGT) simulator to generate fault data, and shows how this can be employed to build a prognostic system. Advantages and disadvantages of this approach are discussed

Remote monitoring and failure prediction of guiding elements and diverting pulleys in passenger elevators

Accelerated urbanization has lead to the rising height of buildings and demand for intensive high performance of elevators in recent years. Consequently, condition monitoring has become a highly desirable capability as the complexity of elevator systems increased. The goal of this study is to develop a monitoring method for elevator components which are subjected to mechanical degradation and failures. The method is capable of indicating the current health condition, predicting future failure as well as detecting emerging issues during operation. Studies of the fundamental principle of elements of condition monitoring such as measurement and measuring equipment, remaining useful life models laid the foundation for new method developing. Moreover, there were reviews of the implementation of health management systems in aerospace and marine industry. A prototype was built from the inductive sensor and open sources embedded system. The device has been installed in two different elevators for data acquisition. Basic data visualization and analysis models were employed for current health state assessment and failure trend prediction. The results include validation of the condition monitoring method and prediction of time-to-failure. Arithmetic means of displacement data determined operating condition whereas the linear regression model was used to predict failure event. Moreover, while suggesting the potential usefulness of the method for system condition assessment, the analysis of the data also exposed challenges inconsistency of the measuring method, data filtering technique as well as large data size requirement

State-of-the-Art Review and Synthesis: A Requirement-based Roadmap for Standardized Predictive Maintenance Automation Using Digital Twin Technologies

Recent digital advances have popularized predictive maintenance (PMx), offering enhanced efficiency, automation, accuracy, cost savings, and independence in maintenance. Yet, it continues to face numerous limitations such as poor explainability, sample inefficiency of data-driven methods, complexity of physics-based methods, and limited generalizability and scalability of knowledge-based methods. This paper proposes leveraging Digital Twins (DTs) to address these challenges and enable automated PMx adoption at larger scales. While we argue that DTs have this transformative potential, they have not yet reached the level of maturity needed to bridge these gaps in a standardized way. Without a standard definition for such evolution, this transformation lacks a solid foundation upon which to base its development. This paper provides a requirement-based roadmap supporting standardized PMx automation using DT technologies. A systematic approach comprising two primary stages is presented. First, we methodically identify the Informational Requirements (IRs) and Functional Requirements (FRs) for PMx, which serve as a foundation from which any unified framework must emerge. Our approach to defining and using IRs and FRs to form the backbone of any PMx DT is supported by the track record of IRs and FRs being successfully used as blueprints in other areas, such as for product development within the software industry. Second, we conduct a thorough literature review spanning fields to determine the ways in which these IRs and FRs are currently being used within DTs, enabling us to point to the specific areas where further research is warranted to support the progress and maturation of requirement-based PMx DTs.Comment: (1)This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A Concept of Operations for an Integrated Vehicle Health Assurance System

This document describes a Concept of Operations (ConOps) for an Integrated Vehicle Health Assurance System (IVHAS). This ConOps is associated with the Maintain Vehicle Safety (MVS) between Major Inspections Technical Challenge in the Vehicle Systems Safety Technologies (VSST) Project within NASA s Aviation Safety Program. In particular, this document seeks to describe an integrated system concept for vehicle health assurance that integrates ground-based inspection and repair information with in-flight measurement data for airframe, propulsion, and avionics subsystems. The MVS Technical Challenge intends to maintain vehicle safety between major inspections by developing and demonstrating new integrated health management and failure prevention technologies to assure the integrity of vehicle systems between major inspection intervals and maintain vehicle state awareness during flight. The approach provided by this ConOps is intended to help optimize technology selection and development, as well as allow the initial integration and demonstration of these subsystem technologies over the 5 year span of the VSST program, and serve as a guideline for developing IVHAS technologies under the Aviation Safety Program within the next 5 to 15 years. A long-term vision of IVHAS is provided to describe a basic roadmap for more intelligent and autonomous vehicle systems

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

Failure analysis informing intelligent asset management

With increasing demands on the UK’s power grid it has become increasingly important to reform the methods of asset management used to maintain it. The science of Prognostics and Health Management (PHM) presents interesting possibilities by allowing the online diagnosis of faults in a component and the dynamic trending of its remaining useful life (RUL). Before a PHM system can be developed an extensive failure analysis must be conducted on the asset in question to determine the mechanisms of failure and their associated data precursors that precede them. In order to gain experience in the development of prognostic systems we have conducted a study of commercial power relays, using a data capture regime that revealed precursors to relay failure. We were able to determine important failure precursors for both stuck open failures caused by contact erosion and stuck closed failures caused by material transfer and are in a position to develop a more detailed prognostic system from this base. This research when expanded and applied to a system such as the power grid, presents an opportunity for more efficient asset management when compared to maintenance based upon time to replacement or purely on condition

Prognostics and health management of power electronics

Prognostics and health management (PHM) is a major tool enabling systems to evaluate their reliability in real-time operation. Despite ground-breaking advances in most engineering and scientific disciplines during the past decades, reliability engineering has not seen significant breakthroughs or noticeable advances. Therefore, self-awareness of the embedded system is also often required in the sense that the system should be able to assess its own health state and failure records, and those of its main components, and take action appropriately. This thesis presents a radically new prognostics approach to reliable system design that will revolutionise complex power electronic systems with robust prognostics capability enhanced Insulated Gate Bipolar Transistors (IGBT) in applications where reliability is significantly challenging and critical. The IGBT is considered as one of the components that is mainly damaged in converters and experiences a number of failure mechanisms, such as bond wire lift off, die attached solder crack, loose gate control voltage, etc. The resulting effects mentioned are complex. For instance, solder crack growth results in increasing the IGBT’s thermal junction which becomes a source of heat turns to wire bond lift off. As a result, the indication of this failure can be seen often in increasing on-state resistance relating to the voltage drop between on-state collector-emitter. On the other hand, hot carrier injection is increased due to electrical stress. Additionally, IGBTs are components that mainly work under high stress, temperature and power consumptions due to the higher range of load that these devices need to switch. This accelerates the degradation mechanism in the power switches in discrete fashion till reaches failure state which fail after several hundred cycles. To this end, exploiting failure mechanism knowledge of IGBTs and identifying failure parameter indication are background information of developing failure model and prognostics algorithm to calculate remaining useful life (RUL) along with ±10% confidence bounds. A number of various prognostics models have been developed for forecasting time to failure of IGBTs and the performance of the presented estimation models has been evaluated based on two different evaluation metrics. The results show significant improvement in health monitoring capability for power switches.Furthermore, the reliability of the power switch was calculated and conducted to fully describe health state of the converter and reconfigure the control parameter using adaptive algorithm under degradation and load mission limitation. As a result, the life expectancy of devices has been increased. These all allow condition-monitoring facilities to minimise stress levels and predict future failure which greatly reduces the likelihood of power switch failures in the first place

A Review of the Roles of Digital Twin in CPS-based Production Systems

The Digital Twin (DT) is one of the main concepts associated to the Industry 4.0 wave. This term is more and more used in industry and research initiatives; however, the scientific literature does not provide a unique definition of this concept. The paper aims at analyzing the definitions of the DT concept in scientific literature, retracing it from the initial conceptualization in the aerospace field, to the most recent interpretations in the manufacturing domain and more specifically in Industry 4.0 and smart manufacturing research. DT provides virtual representations of systems along their lifecycle. Optimizations and decisions making would then rely on the same data that are updated in real-time with the physical system, through synchronization enabled by sensors. The paper also proposes the definition of DT for Industry 4.0 manufacturing, elaborated by the European H2020 project MAYA, as a contribution to the research discussion about DT concept

Prognostics and Health Management in Nuclear Power Plants: A Review of Technologies and Applications

This report reviews the current state of the art of prognostics and health management (PHM) for nuclear power systems and related technology currently applied in field or under development in other technological application areas, as well as key research needs and technical gaps for increased use of PHM in nuclear power systems. The historical approach to monitoring and maintenance in nuclear power plants (NPPs), including the Maintenance Rule for active components and Aging Management Plans for passive components, are reviewed. An outline is given for the technical and economic challenges that make PHM attractive for both legacy plants through Light Water Reactor Sustainability (LWRS) and new plant designs. There is a general introduction to PHM systems for monitoring, fault detection and diagnostics, and prognostics in other, non-nuclear fields. The state of the art for health monitoring in nuclear power systems is reviewed. A discussion of related technologies that support the application of PHM systems in NPPs, including digital instrumentation and control systems, wired and wireless sensor technology, and PHM software architectures is provided. Appropriate codes and standards for PHM are discussed, along with a description of the ongoing work in developing additional necessary standards. Finally, an outline of key research needs and opportunities that must be addressed in order to support the application of PHM in legacy and new NPPs is presented