Browse Topic: Prognostics
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 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 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
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 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 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 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
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 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
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
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
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
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
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
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
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
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