Browse Topic: Prognostics
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
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
ABSTRACT Curtiss-Wright has developed an acoustic based sensor technology for measuring friction, shock, and dynamic load transfer between moving parts in machinery. This technology provides a means of detecting and analyzing machine structure borne ultrasonic frequency sounds caused by friction and shock events between the moving parts of the machine. Electrical signals from the sensors are amplified and filtered to remove unwanted low frequency vibration energy. The resulting data is analyzed as a computed stress wave energy value that considers the amplitude, shape, duration and rates of all friction and shock events that occur during a reference time interval. The ability to separate stress waves from the lower frequency operational noise makes this technology capable of detecting damaged gears/bearings and changes in lubrication in equipment earlier than other techniques, and before failure progression increases cost of repair. Already TRL9 in adjacent industries, this technology
ABSTRACT Curtiss-Wright has developed an advanced Smart Power Architecture for Intelligent Power Distribution, based on our Intelligent Power Distribution Demonstration (iPDD) and experience in providing power distribution components specifically for Heavy Brigade Combat Team (HBCT) vehicles. The challenges of power distribution and management in ground vehicles are presented, including issues of scalability, warfighter burden, and the complexity of distributing multiple vehicle power sources. The fundamental building blocks of Smart Power are described, including Power Distribution Units, Power Conditioning Units, and types of Power Conversion Units (AC/DC, DC/DC, DC/AC). A Smart Power Reference Architecture will be presented, showing how it enables scalable and modular power distribution systems. How modular Smart Power Architecture can enable commonality across vehicles and applications. How it can provide automatic and programmable load management, including startup and shutdown
ABSTRACT This paper is a technology update of the continued leveraging of using the newest vehicle diagnostics system, the Smart Wireless Internal Combustion Engine (SWICE) interface as the Mini-VCS (Vehicle Computer System). The objective is to further enhance Conditioned Based Maintenance Plus (CBM+) secure diagnostics, data logging, prognostics and sensor integration to support improvement of the US military ground vehicle fleet’s uptime to enhance operational readiness. Evolving advancements of the SWICE initiative will be presented, including how the SWICE “At Platform” Test System can readily be deployed as a multiple-use Mini-VCS. The application of the Mini-VCS integrates the best practices of diagnostics and prognostics, coupled with specialized sensor integration, into a solution that optimally benefits the military ground vehicle fleet. These benefits include increased readiness and operational availability, reduced maintenance costs, lower repair part inventory levels
ABSTRACT Machinery health management is becoming increasingly important and the diagnosis of failures based on machinery condition has been analyzed in-depth in the last few decades, and is relatively well understood. However, prognostic evaluation of faults in a machine is a harder task that involves predicting impending faults in the system and determining remaining useful life of the machinery. A survey of algorithms, and a detailed description of a hybrid CBM prognostic techniques being investigated for use in ground vehicle systems will be presented. The system incorporates a number of techniques to process and analyze the current condition of a ground vehicle, and to generate a prognosis for each subsystem in the vehicle. The discussion will describe a means of testing, verifying and iteratively improving prognostic capabilities throughout the lifecycle of the platform
ABSTRACT Materials and parts in complex systems, such as ground vehicles, can suffer from fatigue due to use, age and other stresses experienced during service. It is therefore essential to evaluate damage and predict the remaining life, reliability and safety of the vehicle. This paper describes the design of a wireless system for real-time monitoring of ground vehicles using Lamb waves. The proposed approach integrates sensor technology, signal processing and wireless networking into a single solution for online structural health monitoring (SHM). Lamb wave inspection is accomplished by inexpensive piezoelectric transducer patches (PZT), which are surface-mounted on the critical components of the vehicle without interrupting its operation. Lamb wave scattering from damage is obtained by comparing the recorded signal with the healthy sample and then damage-related features are identified using Probability Diagnostic Imaging (PDI). The problem of multiple Lamb wave modes is addressed
ABSTRACT Vehicle prognostics are used to estimate the remaining useful life of components or subsystems, based on measured vehicle parameters. This paper presents an overview of a vehicle prognostic system, including the critical tasks associated with configuring such a system. The end user of a vehicle prognostic system focuses on the reports generated by the system that provide indications of vehicle readiness, condition and remaining useful life. These reports are based on measurements recorded from sensors on the vehicle and analyzed either on the vehicle or remotely by a “back office” information management system; the latter also provides usage severity trends. To implement such a system, an engineer must first define the vehicle components of interest and determine “damage correlates”: the relationship between damage occurring on key component(s) and key vehicle parameters that can be obtained from vehicle “bus data”. These “damage correlates” and the associated analysis methods
ABSTRACT A combination of real world experience and new research initiatives will open up the universe of prognostic and diagnostic algorithms that can be created in the future. This presents the challenge of creating a system architecture that enables effective support of an infinite set of future algorithms even before they have been conceived, designed, implemented, tested, and approved for use. The Arbor architecture enables five critical elements to meet this challenge: (1) clean integration between legacy and new software, (2) remote, over the air provisioning of algorithms, (3) flexible data structures capable of evolving, (4) control points for the algorithm to report findings to in-vehicle occupants, and (4) a data collection strategy for failure incident reporting. Many algorithms are impossible to develop until we collect real world performance and failure information from on the vehicle. The Arbor system collects this information and feeds it off-board for analysis
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 Camber Corporation, under contract with the TACOM Life Cycle Management Command Integrated Logistics Support Center, has developed an innovative process of data mining and analysis to extract information from Army logistics databases, identify top cost and demand drivers, understand trends, and isolate environmental issues. These analysis techniques were initially used to assess TACOM-managed equipment in extended operations in Southwest Asia (SWA). In 2009, at the request of TACOM and the Tank Automotive Research, Development and Engineering Center (TARDEC), these data mining processes were applied to four tactical vehicle platforms in support of Condition Based Maintenance (CBM) initiatives. This paper describes an enhanced data mining and analysis methodology used to identify and rank components as candidates for CBM sensors, assess total cost of repair/replacement and determine potential return on investment in applying CBM technology. Also discussed in this paper is the
ABSTRACT As the industry looks towards Condition Based Maintenance (CBM) as the next maintenance paradigm, OEMs and suppliers are looking into their readiness in meeting the CBM challenges for the future. The US armed forces are currently investigating CBM for their Tactical and Combat vehicles as a means of improving combat readiness & equipment reliability, and reducing maintenance costs. Many cutting-edge technologies will have to be integrated in designing the CBM systems that will support the next generation of vehicles. While most of the required technologies exist, a comprehensive design will be required to make CBM systems feasible and economical
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
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
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