A Survey of Health Management User Objectives Related to Diagnostic and Prognostic Metrics

One of the most prominent technical challenges to effective deployment of health management systems is the vast difference in user objectives with respect to engineering development. In this paper, a detailed survey on the objectives of different users of health management systems is presented. These user objectives are then mapped to the metrics typically encountered in the development and testing of two main systems health management functions: diagnosis and prognosis. Using this mapping, the gaps between user goals and the metrics associated with diagnostics and prognostics are identified and presented with a collection of lessons learned from previous studies that include both industrial and military aerospace applications

ELECTRONIC PROGNOSTICS AND HEALTH MANAGEMENT: A RETURN ON INVESTMENT ANALYSIS

Prognostics and Health Management (PHM) provides the potential to lower sustainment costs, to improve maintenance decision-making, and to provide product usage feedback into the product design and validation process. A case analysis was developed using a discrete event simulation to determine the benefits and the potential cost avoidance resulting from the use of PHM in avionics. The model allows for variability in implementation costs, operational profile, false alarms, random failure rates, and system composition to enable a comprehensive calculation of the Return on Investment (ROI) in support of acquisition decision making. The case analysis compared the life cycle costs using unscheduled maintenance to the life cycle costs using two types of PHM approaches

State-of-the-art in integrated vehicle health management

Integrated vehicle health management (IVHM) is a collection of data relevant to the present and future performance of a vehicle system and its transformation into information can be used to support operational decisions. This design and operation concept embraces an integration of sensors, communication technologies, and artificial intelligence to provide vehicle-wide abilities to diagnose problems and recommend solutions. This article aims to report the state-of-the-art of IVHM research by presenting a systematic review of the literature. The literature from different sources is collated and analysed, and the major emerging themes are presented. On this basis, the article describes the IVHM concept and its evolution, discusses configurations and existing applications along with main drivers, potential benefits and barriers to adoption, summarizes design guidelines and available methods, and identifies future research challenges

A "DESIGN FOR AVAILABILITY" METHODOLOGY FOR SYSTEMS DESIGN AND SUPPORT

Prognostics and Health Management (PHM) methods are incorporated into systems for the purpose of avoiding unanticipated failures that can impact system safety, result in additional life cycle cost, and/or adversely affect the availability of a system. Availability is the probability that a system will be able to function when called upon to do so. Availability depends on the system's reliability (how often it fails) and its maintainability (how efficiently and frequently it is pro-actively maintained, and how quickly it can be repaired and restored to operation when it does fail). Availability is directly impacted by the success of PHM. Increasingly, customers of critical systems are entering into "availability contracts" in which the customer either buys the availability of the system (rather than actually purchasing the system itself) or the amount that the system developer/manufacturer is paid is a function of the availability achieved by the customer. Predicting availability based on known or predicted system reliability, operational parameters, logistics, etc., is relatively straightforward and can be accomplished using several methods and many existing tools. Unfortunately in these approaches availability is an output of the analysis. The prediction of system's parameters (i.e., reliability, operational parameters, and/or logistics management) to meet an availability requirement is difficult and cannot be generally done using today's existing methods. While determining the availability that results from a set of events is straightforward, determining the events that result in a desired availability is not. This dissertation presents a "design for availability" methodology that starts with an availability requirement and uses it to predict the required design, logistics and operations parameters. The method is general and can be applied when the inputs to the problem are uncertain (even the availability requirement can be represented as a probability distribution). The method has been demonstrated on several examples with and without PHM

Safety Return on Investment (ROI): The Broader Adoption of Rotorcraft CFIT-Avoidance Technology

This dissertation provided a method of estimating the potential return on investment (ROI) that could be achieved if operators were to adopt the readily available controlled flight into terrain (CFIT) avoidance technology more broadly. Previous research explored the costs and benefits of different safety initiatives but did not evaluate from an operators’ perspective. For the operators, a private ROI that excludes societal costs and benefits was therefore considered the suitable metric. For the rotorcraft industry, the ROI estimation methodology was not readily available, and this study sought to fill that gap. The purpose of this study was to estimate the potential ROI by determining the costs associated with the outcomes of CFIT-accidents, the costs of adopting the technology, the current accident rate, the benefits expressed as costs avoided through a reduction in the number of accidents, and application of the appropriate ROI formula. The dissertation was conducted as a mixed method study that used qualitative data from historical CFIT-related accident reports to identify the accident outcomes and estimate the associated accident costs plus the available quantitative data to estimate the CFIT-avoidance technology adoption costs. The accident cost categories were based on categories used in airline research and modified for the rotorcraft industry. Using the formula, ROI = Net benefits divided by safety technology adoption costs, ROI values were generated in multiple iterations of the Monte Carlo simulation. The net benefits were evaluated as the difference between the potential accident costs avoided with a reduction in CFIT accidents and the technology adoption costs. The simulation results for the three rotorcraft categories showed that the turbinesingle would experience the highest ROI, followed by the piston category and the twinturbines. When all rotorcraft categories were considered, the ROI was positive but could turn negative if the technology adoption costs grew by a factor of more than three. The broad range in the ROI values for both the piston and single-turbine categories were largely driven by the high variation of the individual cost categories, especially the direct costs: occupant death and injuries, aircraft damage, and leasing costs. From the results of the study, it was recommended that CFIT-avoidance technology should be more broadly adopted by piston and single-turbine rotorcraft operators. For twin-turbines, the adoption should be evaluated against the impact of the regulatory changes for helicopter air ambulance (HAA) operations, which may reduce the number of accidents and generate a positive ROI before further action from operators. Future research should focus on validating the methodology by using it as a starting point for evaluating the ROI for safety initiatives that have already been implemented, whether technology or operational programs. The industry should also improve the methodology by defining or proposing better processes for estimating rotorcraft accident costs, especially indirect costs estimated to be the of the same magnitude as the direct costs. The rotorcraft industry should find ways to make costs data, such as accident investigation costs, more accessible in order to apply the ROI estimation methodology to achieve more accurate results

A Framework for Prognostics Reasoning

The use of system data to make predictions about the future system state commonly known as prognostics is a rapidly developing field. Prognostics seeks to build on current diagnostic equipment capabilities for its predictive capability. Many military systems including the Joint Strike Fighter (JSF) are planning to include on-board prognostics systems to enhance system supportability and affordability. Current research efforts supporting these developments tend to focus on developing a prognostic tool for one specific system component. This dissertation research presents a comprehensive literature review of these developing research efforts. It also develops presents a mathematical model for the optimum allocation of prognostics sensors and their associated classifiers on a given system and all of its components. The model assumptions about system criticality are consistent with current industrial philosophies. This research also develops methodologies for combine sensor classifiers to allow for the selection of the best sensor ensemble

On-line health monitoring of passive electronic components using digitally controlled power converter

This thesis presents System Identification based On-Line Health Monitoring to analyse the dynamic behaviour of the Switch-Mode Power Converter (SMPC), detect, and diagnose anomalies in passive electronic components. The anomaly detection in this research is determined by examining the change in passive component values due to degradation. Degradation, which is a long-term process, however, is characterised by inserting different component values in the power converter. The novel health-monitoring capability enables accurate detection of passive electronic components despite component variations and uncertainties and is valid for different topologies of the switch-mode power converter. The need for a novel on-line health-monitoring capability is driven by the need to improve unscheduled in-service, logistics, and engineering costs, including the requirement of Integrated Vehicle Health Management (IVHM) for electronic systems and components. The detection and diagnosis of degradations and failures within power converters is of great importance for aircraft electronic manufacturers, such as Thales, where component failures result in equipment downtime and large maintenance costs. The fact that existing techniques, including built-in-self test, use of dedicated sensors, physics-of-failure, and data-driven based health-monitoring, have yet to deliver extensive application in IVHM, provides the motivation for this research ... [cont.]

Assessment of the State of the Art of Integrated Vehicle Health Management Technologies as Applicable to Damage Conditions

A survey of literature from academia, industry, and other Government agencies assessed the state of the art in current integrated vehicle health management (IVHM) aircraft technologies. These are the technologies that are used for assessing vehicle health at the system and subsystem level. This study reports on how these technologies are employed by major military and commercial platforms for detection, diagnosis, prognosis, and mitigation. Over 200 papers from five conferences from the time period of 2004 to 2009 were reviewed. Over 30 of these IVHM technologies are then mapped into the 17 different adverse event damage conditions identified in a previous study. This study illustrates existing gaps and opportunities for additional research by the NASA IVHM Project

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

The US operating fleet of light water reactors (LWRs) is currently undergoing life extensions from the original 40-year license to 60 years of operation. In the US, 74 reactors have been approved for the first round license extension, and 19 additional applications are currently under review. Safe and economic operation of these plants beyond 60 years is now being considered in anticipation of a second round of license extensions to 80 years of operation.Greater situational awareness of key systems, structures, and components (SSCs) can provide the technical basis for extending the life of SSCs beyond the original design life and supports improvements in both safety and economics by supporting optimized maintenance planning and power uprates. These issues are not specific to the aging LWRs; future reactors (including Generation III+ LWRs, advanced reactors, small modular reactors, and fast reactors) can benefit from the same situational awareness. In fact, many SMR and advanced reactor designs have increased operating cycles (typically four years up to forty years), which reduce the opportunities for inspection and maintenance at frequent, scheduled outages. Understanding of the current condition of key equipment and the expected evolution of degradation during the next operating cycle allows for targeted inspection and maintenance activities. This article reviews the state of the art and the state of practice of prognostics and health management (PHM) for nuclear power systems. Key research needs and technical gaps are highlighted that must be addressed in order to fully realize the benefits of PHM in nuclear facilities