Browse Topic: Prognostics

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Prognostics Framework to Enable a Maintenance Free Operating Period F-0077-2021-168035/10/2021
ABSTRACT
DeWind, Eric, Sopko,  Richard
Automated Component Tracking Technologies for Future Vertical LiftF-0076-2020-1627310/5/2020
Over the last decade or more there has been a concerted push to move from on condition to predictive maintenance to improve rotorcraft availability and cost competitiveness of sustainment (Ref. 1-2). The US Army, along with industry partners, have been working on the development of prognostics for complete rotorcraft coverage. It has been identified that accurately capturing maintenance actions is needed to improve the accuracy of prognostics for better component health state awareness. Further to achieve the Army's vision for Zero Maintenance rotorcraft and meet the Maintenance Free Operating Period (MFOP) (Ref. 3) requirements for the Future Vertical Lift (FVL) program, it's essential to have an automated configuration management system. To help meet these objectives, the Army and Honeywell are working on the Rotorcraft Automated Component Tracking (RACT) Science and Technology (S&T) development program. This paper discusses the research being conducted to enable the Army's RACT concept done by the Honeywell team and the CCDC AvMC. It identifies the current state of RACT technologies and challenges of integrating such technologies into the rotorcraft environment.
Bharadwaj, Raj, Moffatt, John, Borck, Hayley
Modeling a Maintenance Free Operating Period Strategy for Future Vertical LiftF-0076-2020-1627010/5/2020
The U.S. Department of Defense has begun the acquisition of the next generation of military rotorcraft, named Future Vertical Lift (FVL), to replace its aging fleet. U.S. Army Futures Command intends to sustain FVL under a new strategy of maintenance free operating periods (MFOP). This study developed a discrete event simulation to evaluate MFOP success given component reliabilities, desired MFOP duration, and operational tempo of a battalion with thirty aircraft. The simulation compared notional FVL aircraft with improved reliability to today's fleet. Results indicated that inherent reliability alone was insufficient to achieve MFOP goals and that prognostics and diagnostics with robust information management are necessary. Sensitivity studies found the recovery effort after an MFOP was linked to the MFOP duration. Recovery downtime was tied to both the duration and operational tempo. Availability and cost improved with moderate gains in MFOP duration by eliminating unnecessary preventive maintenance but overextending the MFOP sacrificed aircraft dependability for marginally greater availability and savings.
Beigh, Matthew, Burgess, James, Schrage, Daniel, Bellocchio, Andrew
Determination of Costs and Benefits from Implementing an Engine Health Management SystemARP4176A (Current)4/28/2020
This ARP provides an insight into how to approach a cost benefit analysis (CBA) to determine the return on investment (ROI) that would result from implementing a propulsion Prognostics and Health Management (PHM) system on an air vehicle. It describes the complexity of features that can be considered in the analysis, the different tools and approaches for conducting a CBA and differentiates between military and commercial applications. This document is intended to help those who might not necessarily have a deep technical understanding or familiarity with PHM systems but want to either quantify or understand the economic benefits (i.e., the value proposition) that a PHM system could provide.
E-32 Aerospace Propulsion Systems Health Management
Rationale, Considerations, and Framework for Data Interoperability for Health Management within the Aerospace EcosystemAIR6904 (Current)4/21/2020
This SAE Aerospace Information Report (AIR) offers an overview of the aspects of intellectual property (IP) protection, legislative compliance, business model, and technologies which need to be considered and addressed to implement a data interoperability, secure business model and technology platform to enable prognostics and health management (PHM) in the digital age. While this information report is restricted to the aerospace domain and also to commercial aviation, the concepts are applicable to any other domain that employs data for supporting health management functionality.
G-31 Electronic Transactions for Aerospace Committee
Improvement of Aircraft Availability and Optimization of Component Costs by Pre-Emptive Removal of Targeted Components2019-01-13413/19/2019
Availability of large repairable systems, like aircraft, are critical for commercial operators to generate revenue, and for military organizations to achieve their mission readiness objectives. Of the relatively few studies that deal with improving availability, most have focused on increasing reliability, and not on the biggest driver of low availability - Unscheduled Maintenance Events (UMEs). The cost of maintenance has long been a target of cost-cutting measures, and one common strategy focuses on extracting as much service life as possible out of various non-critical system components by letting those components “run to failure” (as defined in SAE JA1012). However, one of the biggest drawbacks of the “run to failure” approach is that it comes at the cost of lower asset availability because the failure of one of those components will nearly always lead to a UME, typically just when the operator wants to use, or is currently using, that asset. To combat the impact of UMEs, many OEMs, operators, and component manufacturers are looking to prognostics to get advanced notice of impending failures, so monitored components can be replaced before they completely fail. But, for technological and/or economic reasons, prognostics are not a viable option for the vast majority of components. Furthermore, the idea that running components to failure will reduce costs is fundamentally flawed because it fails to account for the extra operational costs incurred from those UMEs. As an alternative to running components to failure or relying only on prognostics, asset operators and maintainers need other strategies to minimize the operational impact of UMEs for components without prognostics that also balances component utilization and the operational costs associated with UMEs against overall asset availability. This paper presents a methodology for evaluating the trade-offs between these factors and shows how this approach can potentially reduce overall asset life-cycle costs.
Lesmerises, Alan
Gear Health Algorithm Solution For Drive System Design And OperationsF-0072-2016-114835/17/2016
This paper summarizes the development and implementation of material-based prognostic technology for modeling fatigue damage in rotorcraft drive system rotating components considering manufacturing process, contact pattern, lubrication effects, stresses, microstructure, and material variability. The AMS 6265 and AMS 6308 prognostic models were developed and applied to predict contact and bending fatigue damage in the planetary gear system. Prognostic model was verified by comparing fatigue life simulation results with industry test data. Prognostic model results were demonstrated and designed for integration with onboard and offboard elements of industry health monitoring/management systems. This paper includes details on predicting contact fatigue and bending fatigue life, endurance limits, maximum continuous power (MCP) rating, and overload effects. Prognostic model results show the application of this gear health algorithm solution in rotorcraft drive system transmission gear design, inspection, maintenance, and recommendation of safe operational powers.
Pulikollu, Rajasekhar
Leveraging Massively Scalable Data Analytics Technologies to Enable Rapid HUMS-Based Fleet Management Decision SupportF-0072-2016-114775/17/2016
Health and Usage Monitoring Systems (HUMS) generate a significant amount of data used for on-board and off-board monitoring of the health of the aircraft and its components. When this data is aggregated over the life of an aircraft, it becomes an invaluable resource that enables decision making for diagnostics, prognostics, and fleet management. At the fleet level, the amount of data being ingested, stored, and processed becomes a challenge in itself. The capability to easily handle data of this size is critical to be responsive to time-critical inquiries, iterate on data modeling, and enable efficient diagnostics and prognostics algorithm development. This paper discusses how massively scalable data analytics technologies have been used to enable rapid decision support using HUMS and other data sources. Several use cases are highlighted to show the novel opportunities enabled by these technologies along with associated challenges.
Koelemay, Michael, Sulcs, Peter
Design and Implementation of Aircraft System Health Management (ASHM) Utilizing Existing Data Feeds2015-01-25879/15/2015
The Aircraft System Health Management (ASHM) tool is a UTC developed web application that provides access to Aircraft Condition Monitoring Function (ACMF) reports and Flight Deck Effects (FDE) records for Boeing 787®, A320®, and A380® aircraft. The tool was built with a flexible architecture to field a range of off-board diagnostics and prognostics modules designed to transform an abundance of data into actionable and timely knowledge about fleet health. This paper describes the system architecture and implementation with a focus on “lessons learned” in applying diagnostic and prognostics algorithms to available fleet data. Key topics include ensuring analytic robustness, design for cross-enterprise collaboration and defining a workable approach to testing, validating and deploying prognostics and diagnostics models with various degrees of complexity. A case study is provided related to fluid leak detection within an environmental control subsystem.
Smith, Matthew, Sulcs, Peter F., Walthall, Rhonda, Mosher, Mark, Kacprzynski, Gregory
Generalized Prognostic Algorithm Implementing Kalman SmootherF-0071-2015-101955/5/2015
ABSTRACT The ability to prognosticate the future state of a mechanical component can greatly improve the ability of a helicopter operator to manage their assets. Fundamentally, prognostics can change the logistics support of a helicopter by: reducing spares, improving the likelihood of a deployment meeting its mission requirements, and reducing unscheduled maintenance events. A successful prognosis is based on applying a fault model and usage metrics (torque) to a diagnostic. This paper addresses a generalized fault and usage model through simplification of Paris' Law and the use of a Kalman Smoother. This state observer technique is a backward/forward filtering technique that has no phase delay. This allows a generalized, zero tuning model that provides an improved component health trend, and a better estimate of the current remaining useful life (RUL).
Bechhoefer, Eric, Schlanbusch, Rune
Integration Issues for Vehicle Level Distributed Diagnostic Reasoners2013-01-22949/17/2013
In today's aircraft the diagnostic and prognostic systems play a crucial part in aircraft safety while reducing the operating and maintenance costs. Aircraft are very complex in their design and require consistent monitoring of systems to establish the overall vehicle health status. Most diagnostic systems utilize advanced algorithms (e.g. Bayesian belief networks or neural networks) which usually operate at system or sub-system level. The sub-system reasoners collect the input from components and sensors to process the data and provide the diagnostic/detection results to the flight advisory unit. Several sources of information must be taken into account when assessing the vehicle health, to accurately identify the health state in real time. These sources of information are independent system-level diagnostics that do not exchange any information/data with the surrounding systems. This limits the system by preventing cross check or health status information exchange amongst the related sub-systems. This article discusses the issues related to the integration of a vehicle level diagnostic reasoner to sub-system level reasoners and information exchange between the sub-system reasoners and the vehicle level reasoner.
Khan, Faisal, Jennions, Ian, Sreenuch, Tarapong
Physics and Measurement of Early Wire Insulation Chafing2008-01-293111/11/2008
This paper discusses the physics of development of electrical defects as the result of wire chafing, and how this can be used to extend the prognostics capability of GE's Smartwire Diagnostic System (SWDS). Chafing, a top symptom of failures (37% in one study [4]) is frequently caused by mechanical vibration of wiring harnesses, which are often strapped to the aircraft structure or run through other types of cable supports (trays, bends, etc.); ambient aircraft structural vibrations are a primary mechanical driver of long term chafing. The US Navy's charter to reduce annual wiring maintenance expenditures by an estimated $57 million provided the driving force for this research effort. No system is available today that can detect and locate a wire insulation chafe without the user disconnecting the wires or passing a high voltage through them. The overall objective of SWDS is to address this gap in wire health monitoring. One of the main goals of SWDS is to detect small wire chafes before they can cause catastrophic electrical failures. The SWDS consists of GE flex sensors, a high-speed data acquisition system and advanced algorithms. In this paper we assess the capability of the sensor to assess various stages of insulation damage, including small but developing defects. The ability to detect very early stages of insulation damage prior to conventionally detected electrical failure is termed “prognostics”. In this paper we will also summarize the present technological barriers to effective and reliable detection and diagnosis of early wire insulation chafing and will discuss the future of prognostic system development for wire chafing in aircraft.
Johnson, Timothy L., Ganesh, Meena
A Statistical Approach for Real-Time Prognosis of Safety-Critical Vehicle Systems2007-01-14974/16/2007
The paper describes the development of a vehicle stability indicator based on the correlation between various current vehicle chassis sensors such as hand wheel angle, yaw rate and lateral acceleration. In general, there is a correlation between various pairs of sensor signals when the vehicle operation is linear and stable and a lack of correlation when the vehicle is becoming unstable or operating in a nonlinear region. The paper outlines one potential embodiment of the technology that makes use of the Mahalanobis distance metric to assess the degree of correlation among the sensor signals. With this approach a single scalar metric provides an accurate indication of vehicle stability.
D'Silva, Siddharth H., Jalics, Laci, Krage, Mark
Dynamic Lively Model to Utilize the Resources in a Vehicle2006-01-05214/3/2006
The work presented here is to develop a monitoring life mathematical model to manage periodically the operations job orders of vehicle service station. This period is occasionally an hour, a day or a longer period than that, and is normally determined by the service manager. Model parameters are changeable over these periods due to dynamic movable situation of a vehicle markets. The objective function is to maximize the total income from vehicles service operations at all considered conventional model or with self expert prognostic system by taken into account least stop of vehicles, while keeping in mind the satisfying the customer demands and the service quality. The decision variables indicate the number of various technical operations to be performed for different types of vehicles. The constrains represent the workforce capacity limitations, the recommended expenditure, the budget limit for spare parts stock, and for immediate needed the purchasing ones, and the available resources for each type of performed service operation. The model deals with the fault code received from vehicles customers through telecommunication capabilities and can reserve available resources to repair the breakdown occur whatsoever emergency or not, can also fix suitable accepted appointments. By applying the system to a sample real data, the results show an increase of the total income by 22 %, and an increase in the utilization of the workforce by 15 % compared by the results exercised in the traditional service station.
Abou-El-Nour, A.M.A.A.
Developing Condition-Based MaintenanceTBMG-46464/1/2006
Like any manufacturing equipment, semiconductor fabrication systems have a finite lifetime. Technicians normally perform maintenance on these hardware systems according to preset schedules and regardless of actual need, which results in unnecessary equipment downtime and needless costs incurred as a result of lost production time and additional maintenance labor. AFRL scientists teamed with researchers from the University of New Mexico (UNM) to examine the feasibility of establishing prognostics for such expensive and valuable machinery and to devise a mechanism for scheduling equipment maintenance based on needs rather than calendar cycles. This so-called condition-based maintenance has the potential to increase equipment availability, improve productivity, enhance safety, and reduce expenses. The ultimate objective of the AFRL/UNM collaboration is to develop a data-driven prognostic system that provides advanced warning of failures, faults, and other error events that occur in complex systems.
"Intelligent" Transceivers Would Predict Failures of PartsTBMG-68536/1/2000
Special-purpose, "intelligent," computer-controlled, highly miniaturized radio transceivers have been proposed for use in monitoring critical and/or valuable pieces of equipment. These transceivers and associated electronic circuits would perform both diagnostic and prognostic monitoring functions: They would acquire and provide information on the history and current status of the monitored equipment. On the basis of this information, they would predict impending failures of components. Then they would order replacements for components expected to fail soon or for consumable supplies (e.g., fuel) expected to be exhausted soon; these orders would be submitted so that the needed replacements would arrive just in time.
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