Browse Topic: Prognostics

Items (15)
Find
Electric Vehicle Battery SOH and RUL prediction using Digital Twin Hybrid ML Model considering effect of Driving Behaviour2024-26-012201/16/2024
The adoption of Electric Vehicles (EVs) is primarily limited by their dependence on batteries, which have lesser power density as compared to conventional fossil fuels as well as its ageing deterioration issues over time. Therefore, there is an urgent need to understand the modifications in battery performance characteristics with respect to changes in temperature, charging behaviour and usage pattern, low and high charge states, current variations etc. To resolve such issues, this work proposes the development of a battery digital twin model to accurately reflect battery dynamics during run time. A digital twin is a virtual model replicating a physical system's characteristics. The digital twin is developed using a physics and machine learning model trained with bench-level and vehicle level actual test data. It uses an equivalent circuit model (ECM) to predict the battery's internal resistance and polarization effect due to ionic diffusion process in the cell. ECM model generates
Dongaonkar, Saharsh, Sarkar, Prasanta, Patil, Gaurav, KONDHARE, MANISH, nath, Subhrajyoti
Determination of Costs and Benefits from Implementing an Engine Health Management SystemARP4176A (Current)04/28/2020
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)04/21/2020
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
Rationale, Considerations, and Framework for Data Interoperability for Health Management within the Aerospace EcosystemAIR6904-TEST-REC (Historical)04/21/2020
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 Components2019-01-134103/19/2019
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
Lesmerises, Alan
Framework and Platform for Next Generation Aircraft Health Management System2017-01-212609/19/2017
In aerospace industry, the concept of Integrated Vehicle Health Management (IVHM) has gained momentum and is becoming need of the hour for entire value chain in the industry. The expected benefits of lesser time for maintenance reduced operating cost and ever busy airports are motivating aircraft manufacturers to come up with tools, techniques and technologies to enable advanced diagnostic and prognostic systems in aircrafts. At present, various groups are working on different systems and platforms for health monitoring of an aircraft e.g. SHM (Structural Health Monitoring), PHM (Prognostics Health Monitoring), AHM (Aircraft Health Monitoring), and EHM (Engine Health Monitoring) and so on. However, these approaches are mostly restricted to federated architecture where faults and failures for standalone line replaceable units (LRUs) are logged inside the unit in fault storage area and are retrieved explicitly using maintenance based applications for fault and failure diagnostics. With
Jha, Ashutosh Kumar, Sahay, Gaurav, Sivaramasastry, Adishesha
Design and Implementation of Aircraft System Health Management (ASHM) Utilizing Existing Data Feeds2015-01-258709/15/2015
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
Integration Issues for Vehicle Level Distributed Diagnostic Reasoners2013-01-229409/17/2013
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
Khan, Faisal, Jennions, Ian, Sreenuch, Tarapong
Keynote: Reducing Vehicle Emissions to Meet Environmental Goals10VOBD1211/22/2011
The goal of experiencing clean air every day in California?s urban areas is in sight, and a major contributor to this is durable, low emitting vehicles resulting in part from use of sophisticated OBD systems. The next challenge is reducing the carbon footprint of passenger vehicles. The objective is to reduce emissions from all sources enough to stabilize climate temperature. Most scientists believe this is about an 80% reduction in emissions by 2050. Because transportation causes upward of a third of all greenhouse gas emissions, it is clear that passenger vehicles will have to do their fair share in helping meet this goal. This presentation will address the emergence of low carbon emitting vehicles and low carbon fuels, and how fast we need to see them become commercial successes if we are to meet the climate challenge. Presenter Thomas Cackette, California Air Resources Board
Cackette, Thomas
Prognostic Health Management of an RF System Using GPSTBMG-3651602/01/2009
Prognostic health management (PHM) of electronic systems presents challenges that traditionally were not worth the cost of pursuit. Recent changes in weapons platform acquisition and support requirements have spurred renewed interest in electronics PHM, revealing accessible data sources and previously unexplored predictive techniques. This article discusses the development and validation of electronic prognostics for a radio frequency (RF) system. The solution has applicability to a broad class of mixed digital/analog circuitry, including radar and software-defined radio.
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
Johnson, Timothy L., Ganesh, Meena
A Statistical Approach for Real-Time Prognosis of Safety-Critical Vehicle Systems2007-01-149704/16/2007
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 Vehicle2006-01-052104/03/2006
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
Abou-El-Nour, A.M.A.A.
Developing Condition-Based MaintenanceTBMG-464604/01/2006
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
"Intelligent" Transceivers Would Predict Failures of PartsTBMG-685306/01/2000
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.
Items per page:
1 – 15 of 15