Browse Topic: Prognostics
This article addresses the design, testing, and evaluation of rigorous and verifiable prognostic and health management (PHM) functions applied to autonomous aircraft systems. These PHM functions—many deployed as algorithms—are integrated into a holistic framework for integrity management of aircraft components and systems that are subject to both operational degradation and incipient failure modes. The designer of a comprehensive and verifiable prognostics system is faced with significant challenges. Data (both baseline and faulted) that are correlated, time stamped, and appropriately sampled are not always readily available. Quantifying uncertainty, and its propagation and management, which are inherent in prognosis, can be difficult. High-fidelity modeling of critical components/systems can consume precious resources. Data mining tools for feature extraction and selection are not easy to develop and maintain. And finally, diagnostic and prognostic algorithms that address accurately
This Surface Vehicle & Aerospace Recommended Practice offers best practices and a methodology by which IVHM functionality relating to components and subsystems should be integrated into vehicle or platform level applications. The intent of the document is to provide practitioners with a structured methodology for specifying, characterizing and exposing the inherent IVHM functionality of a component or subsystem using a common functional reference model, i.e., through the exchange of design-time data and the application of standard vehicle data communications interfaces. This document includes best practices and guidance related to the specification of the information that must be exchanged between the functional layers in the IVHM system or between lower-level components/subsystems and the higher-level control system to enable health monitoring and tracking of system degradation severity. The intent is to provide an IVHM system that can robustly report the degradation of a given
Laminated composites are extensively used in the aerospace industry. However, structures made from laminated composites are highly susceptible to delamination failures. It is therefore imperative to consider a structure’s tolerance to delamination during design and operation. Hybrid composites with laminas containing different fibers were used earlier in laminates to achieve certain benefits in strength, stiffness, and buckling. However, the concept of mixing laminas with different fibers was not explored by researchers to enhance delamination tolerance levels. This article examines the above aspect of hybridization by employing machine learning algorithms and proposes a reliable method of analysis to study delamination, which is crucial to ensure the safety of airframe composite panels. In this article, fracture-mechanics-based structural integrity results related to mode I Strain Energy Release Rate (SERR) are obtained using geometrical non-linear three-dimensional finite element
The purpose of the OBIGGS is to reduce the amount of oxygen in the fuel tank to a 'safe' level to significantly reduce the possibility of ignition of fuel vapors. There are circumstances where equipment of OBIGGS like ASMs, Ozone Converter Catalysts, etc. gets degraded earlier than the provided MTBF. This paper studies the present conventional systems limitations, like due to memory constraints only the faults and limited shop data are being recorded, hence there is no provision to store/report the stream of data margins with which we can pass/fail the performance tests. This paper also explains how a new design of the Connected concept achieves access to real-time data from the system and how the data is pushed to the cloud network. A connected solution for the OBIGGS is the technology to access real-time data (Systems LRUs Performance data and Custom data Parameters) from the Systems controller data bus, this data is further applied to AI/ML methods for predictive/prognostics
Electrified transportation has received significant interest recently because of sustainable and clean energy goals. However, the degradation of electrical components such as energy storage systems raises system reliability and economic concerns. In this paper, a prognostic-based control strategy is proposed for hybrid electric vehicles (HEVs) to abate the degradation of energy systems. Degradation forecasting models of electrical components are developed to predict their degradation paths. The predicted results are then used to control HEVs in order to reduce the degradation of components.
An accurate voltage prediction associated with uncertainty quantification is of great importance to predict the remaining useful life for proton exchange membrane fuel cell in automobile applications. This paper achieves the remaining useful life prediction using deep neural networks, with an emphasis on uncertainty quantification in voltage prognostics for proton exchange membrane fuel cell systems. The trend and pattern of voltage degradation data was investigated by using long-short term memory and the voltage prediction trend was represented with prediction interval. The experimental results show that the deep learning model with corresponding uncertainty techniques can achieve prediction root mean square error values within 0.02 and represent the voltage prediction with a prediction interval.
Prognostic health management (PHM) of electronic systems presents challenges traditionally viewed as either insurmountable or not worth the cost of pursuit, but recent changes in weapons platform acquisition and support requirements has spurred renewed interest in electronics PHM, revealing possible applications, accessible data sources, and previously unexplored predictive techniques. Naval Air Warfare Center, Patuxent River, Maryland Many types of circuits compose avionics systems. One of the following categories can be used to classify each circuit topology at the time this research was performed: High frequency analog Low frequency analog Low impedance High impedance Common failure mode mechanisms for analog circuits depend largely on the architecture and relative operating frequencies of the circuit. In this research, high frequency analog circuits are categorized as operating above 1GHz, while low frequency analog circuits operate below 1GHz. High frequency analog circuits are
ABSTRACT Implementing Prognostic and Predictive Maintenance (PPMx) for the U.S. Army’s ground vehicle fleet requires the design and integration of on-platform predictive analytics. To support the design process, U.S. Army DEVCOM Ground Vehicle Systems Center (GVSC) and Applied Research Laboratory (ARL) Penn State researchers are developing a systematic approach that uses reliability modeling in a guiding role. The key steps of the process are building the initial reliability model from available data (e.g., system diagrams and physical layouts), augmenting with information on observed states and failure modes via subject matter experts, and then conducting trades on additional sensors and algorithms to determine a suitable predictive analytics capability. In this paper we provide an example of this process as applied to an Army ground vehicle, first focusing on a simplified sub-problem to demonstrate the technique, then providing statistics on the large scale process. Citation: M
ABSTRACT As the Army leverages Prognostic and Predictive Maintenance (PPMx) models to migrate ground vehicle platforms toward health monitoring and prescriptive maintenance, the need is imminent for a pipeline to quickly and constantly move operational and maintenance data off the platform, through analytic models, and push the insights gained back out to the edge. This process will reduce data-to-decision time and operation and sustainment costs while increasing reliability for the platform and situational awareness for analysts, subject matter experts, maintainers, and operators. The US Army Ground Vehicle Systems Center (GVSC) is collaborating with The US Army Engineer Research and Development Center (ERDC) to develop a system of systems approach to stream operational and maintenance data to appropriate computing resources, collocating the data with DoD High-Performance Computing (HPC) processing capabilities where appropriate, then channeling the generated insights to maintainers
Reducing the power consumption—and hence, the fuel burn—is a major target for the next generation of aircraft, and electrical actuation is perceived as a technological area able to provide power saving. Electrical actuation can in fact contribute to the reduction of the non-propulsive power because electro-mechanical actuators, when compared to the conventional hydraulic actuators, rely on a form of power subjected to lower distribution losses and in general can lead to a weight savings at the aircraft level if the required power remains under a break-over point. Moreover, electro-mechanical actuators (EMAs) present higher reliability and maintainability with a lower life-cycle cost. Two critical issues with electrically powered actuation are the temperature rise in the electric motor windings and in the power electronics, and the sensitivity to certain single point of failures that can lead to mechanical seizures, that has so far thwarted the use of EMAs for safety-critical
This document applies to prognostics of aerospace propulsion systems. Its purpose is to define the meaning of prognostics in this context, explain their potential and limitations, and to provide guidelines for potential approaches for use in existing condition monitoring environments. This document also includes some examples. The current revision does not provide specific guidance on validation and verification, nor does it address implementation aspects such as computational capability or certification.
Ability to have least failures in products on the field with minimum effort from the manufacturers is a major area of focus driven by Industry 4.0 initiatives. Amidst traditional methods of performing system/subsystem level tests often does not enable the complete coverage of a machine health performance predictions. This paper highlights a workable workflow that could be used as a template while considering system design especially employing Digital Twins that help in mimicking real-life scenarios early in the design cycle to increase product’s reliability as well as tend to near zero defects. With currently available disruptive technologies, systems integrated multi-domain 'mechatronics' systems operating in closed-loop/close-interaction. This poses great challenge to system health monitoring as failure of any component can trigger catastrophic system failures. It may be the reason that component failures, as per some aerospace reports, are found to be major contributing factors to
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
NASA’s Langley Research Center has developed a new technology for measuring the junction temperature of laser diode arrays (LDAs) that can support dramatically improved LDA fault analysis and lifetime estimates. This technology provides better spatial and temporal resolution than spectral chirp or thermal imaging methods, and can be integrated into existing LDA systems, such as laser diode drivers, without significant additional costs (including weight, power, and space). Potential applications include quality control and screening of LDAs for maximum lifetime, optimizing development of operational parameters, or providing real-time operational diagnostics/prognostics.
Within the aviation industry analysis of wear debris particles recovered from magnetic plugs and lubricating fluids is an essential condition monitoring tool. However, in large organisations, high staff turnover in remote work environments often leaves dangerous gaps in on-site support and background knowledge. The current work develops interactive software for wear debris particle classification, root cause diagnosis and serviceability prognostics. During the research several hundred wear debris particle images were collected, analysed and classified by a number of experts. At each stage of the analysis the experts were questioned about the knowledge and experience used to make their diagnoses and prognoses. The end result is an extensive knowledge base representing the combined expertise of a number of highly trained engineers, each with decades of hands-on experience. Access to this knowledge base is provided at each stage of interaction with the software, as automatically generated
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
ABSTRACT All CBM+ solutions must establish a business case considering cost of implementation and sustainment of value with a quantifiable return on investment. The business case must be traceable to specific failure modes, associated failure effects, criticality, and risk. Risk is not limited to safety and operational risks. Predictive systems by definition return both true and false predictions representing operational and financial risk from high false positive rates. There is also risk of losing operator confidence in predictive systems when there is a high false positive rate. All of these risks must be quantified and considered in the design and development of CBM+ systems. Model based approaches are effective in accelerating development, defining advanced functional characteristics, and efficiently testing dynamic effects of complex systems. CBM+ maintenance strategies rely on performance of complex systems.
Suppliers and integrators are working with SAE’s HM-1 standards team to develop a mechanism to allow “Health Ready Components” to be integrated into larger systems to enable broader IVHM functionality (reference SAE JA6268). This paper will discuss how the design data provided by the supplier of a component/subsystem can be integrated into a vehicle reference model with emphasis on how each aspect of the model is transmitted to minimize ambiguity. The intent is to enhance support for the analytics, diagnostics and prognostics for the embedded component. In addition, we describe functionality being delegated to other system components and that provided by the supplier via syndicated web services. As a specific example, the paper will describe the JA6268 data submittal for a typical automotive turbocharger and other engine air system components to clarify the data modeling and integration processes. It will describe how data submittals are structured to support IVHM analytics
NASA has developed a method that prevents total system failure during emergencies, allowing parts of the system to continue to function, and making overall system recovery faster. A heterogeneous set of system components monitored by a varied suite of sensors and a health monitoring framework has been developed with the power and flexibility to adapt to different diagnostic and prognostic needs. Current state-of-the-art monitoring and health management systems are mostly centralized in nature, where all the processing is reliant on a single processor. This requires information to be sent and processed in one location. With increases in the volume of sensor data as well as the need for associated processing, traditional centralized systems tend to be somewhat ungainly; in particular, when faced with multi-tasking of computationally heavy algorithms. The distributed architecture is more efficient, allows for considerable flexibility in number and location of sensors placed, scales up
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.
Modern aircraft, such as A380 or A350 for Airbus, are very well connected in flight to ground stations through wireless communications. For maintenance and operations purpose, the aircraft is programmed to send regularly information such as flight reports based on the BITE messages (Built-In Test Equipment) or standard reports based on the value of physical parameters. Moreover, Airbus is capable of sending requests (called uplinks) to the aircraft to retrieve the value of different parameters in almost real-time. This ability, associated with adequate process, improves significantly the reaction time of the diagnostic and prognostic solutions that Airbus can provide to its customers. Traditionally Health Monitoring is considered useful when the Potential to Functional failure (P-F) interval is greater than one flight cycle. This is due to the fact that it usually takes at least a flight cycle to collect and analyze data, and because on-board systems are responsible for managing
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