Browse Topic: Vehicle health management (VHM)
This SAE Aerospace Information Report (AIR) provides an overview of temperature measurement techniques for various locations of aircraft gas turbine engines while focusing on current usage and methods, systems, selection criteria, and types of hardware
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 A retrofittable intelligent vehicle performance and fuel economy maximization system would have widespread application to military tactical and non-tactical ground vehicles as well as commercial vehicles. Barron Associates, Inc. and Southwest Research Institute (SwRI) recently conducted a research effort in collaboration with the U.S. Army RDECOM to demonstrate the feasibility of a Fuel Usage Monitor and Economizer (FUME) – an open architecture vehicle monitoring and fuel efficiency optimization system. FUME features two primary components: (1) vehicle and engine health monitoring and (2) real-time operational guidance to maximize fuel efficiency and extend equipment life given the current operating conditions. Key underlying FUME technologies include mathematical modeling of dynamic systems, real-time adaptive parameter estimation, model-based diagnostics, and intelligent usage monitoring. The research included demonstration of the underlying FUME technologies applied to a
ABSTRACT This paper discusses the Diagnostics And System Health (DASH) embedded diagnostics software originally developed for use on the M109A7 / M992A3 Family of Vehicles (FoV). The history and background of work completed by the DEVCOM Armaments Center (AC) System Health & Interactive Future Technologies (SHIFT) Division in developing and managing the DASH program are described. The DASH software architecture and design details are also discussed in depth, with a focus on the more recent efforts to adapt DASH to use a generic core software application that can be integrated on a wide variety of current and future ground combat systems to more easily provide embedded diagnostics capability. Citation: A. Ludwig, D. Tagliente, “Enabling Custom Vehicle Diagnostics with a Common Application Platform”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 10-12, 2021
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 FBS Inc. is working with the TARDEC Electrified Armor Lab to develop a nondestructive structural health monitoring technology for composite armor panels that utilizes an array of embedded ultrasonic sensors for guided wave tomographic imaging. This technology would allow for periodic or real-time monitoring of armor integrity while being minimally intrusive and adding negligible weight. The technology is currently being developed and tested in pseudo composite armor panels and efforts are focused on reducing sensor array density, improving sensor integration procedures, and maximizing system sensitivity to damage. In addition to experimental testing and development, FBS is developing a highly-automated finite element model generation and analysis program to be used in conjunction with Abaqus/Explicit commercial finite element software. This program is specifically dedicated to modeling guided wave propagation in pseudo composite armor panels between embedded ultrasonic sensors
ABSTRACT Sharing platform health information in a disconnected environment requires the use of design strategies that consider the various systems that must participate in the creation, processing, and consuming of component health information. Using a common representation of a vehicle structure, platform health can be calculated, predicted, and communicated to end users at all levels of the enterprise. Implementing a Service Oriented Architecture (SOA) using a Grid Services approach enables a central application to manage and share data as needed; performing data integration, data cleansing, and data normalization. This design pattern facilitates holistic collaboration for platform health management on-platform, at-platform, within the tactical domain, at the national level, and at the OEM location
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
The paper presents a theoretical framework for the detection and first-level preliminary identification of potential defects on aero-structure components by employing ultrasonic-guided wave-based structural health monitoring strategies, systems and tools. In particular, we focus our study on ground inspection using a laser-Doppler scan of the surface velocity field, which can also be partly reconstructed or monitored using point sensors and actuators structurally integrated. Using direct wavefield data, we first question the detectability of potential defects of unknown location, size, and detailed features. Defects could be manufacturing defects or variations, which may be acceptable from a design and qualification standpoint; however, those may cause significant background signal artefacts in differentiating structure progressive damage or sudden failure like impact-induced damage and fracture. We consider the surface velocity field over continuous time stamps obtained from laser
Maintenance, repair, and overhaul (MRO) facilities are a major contributor to the safe, reliable, and efficient service of an aircraft. Practices have continually evolved to support complex operations and enhance performance and availability while decreasing operating costs. With technological breakthroughs in electric land vehicles revolutionizing their respective industry, MRO facilities in aviation are also adopting digital technologies in their practices. Despite this drive towards digitalization, the industry is still dominated by manual labor and subjective assessments. Operations may or may not follow the exact expected profile, and that is when sensors integrated into a maintenance system can indicate that the aircraft may or may not fly another flight. Today, several technologies, processes, and practices are being championed to resolve some of these outstanding challenges. Considering this, it is important to present current perspectives regarding where the technology stands
The process detailed within this document is generic and applies to the entire end-to-end health management capability, covering both on-board and on-ground elements, in both commercial and military applications throughout their lifecycle. This ARP addresses a gap in guidance related to usage of ground-based health management equipment for airworthiness credit, ensuring a level of integrity commensurate with the potential aircraft-level consequences of the relevant failure conditions. The practical application of this standardized process is detailed in the form of a checklist. The on-board elements described here are typically the source of the data acquisition used for off-board analysis. The on-board aspects relating to airworthiness and/or safety of flight, e.g., pilot notification, are addressed by existing guidance and policy documents. If a proposed health management capability for airworthiness credit involves modification of the on-board systems, the substantiation of those
AIR5317 establishes the foundation for developing a successful APU health management capability for any commercial or military operator, flying fixed wing aircraft or rotorcraft. This AIR provides guidance for demonstrating business value through improved dispatch reliability, fewer service interruptions, and lower maintenance costs and for satisfying Extended Operations (ETOPS) availability and compliance requirements
This SAE Aerospace Information Report (AIR) provides an overview of temperature measurement techniques for various locations of aircraft gas turbine engines while focusing on current usage and methods, systems, selection criteria, and types of hardware
This SAE Aerospace Standard defines the requirements for establishing a nondestructive inspection (NDI) program for aerospace systems to include but not limited to aircraft structure, aircraft stores (external structures such as antennas, pods, fuel tanks, weapons, radomes, etc.) and missile/rocket structural components when an NDI Program Plan is required by contract. NDI Programs are essential to ensuring NDI processes are implemented to support the lifecycle design requirements of the system and its components. NDI Programs are applicable to all phases of the system life cycle, including acquisition, modification, and sustainment. This standard may also be applicable to mechanical equipment, subsystems, and propulsion systems, but the requirements defined by the NDI Program Plan should be tailored by the contracting agency for such use. An NDI Program Plan shall be developed at the beginning of the technology development phase and shall define all NDI requirements to be adhered to
Distributed fiber sensors are a powerful tool for structural health monitoring and environmental sensing due to their ability to remotely monitor the strain at 1,000s of locations using low-cost optical fiber. Sensors based on Brillouin scattering are uniquely suited to these tasks since they can make completely distributed, absolute measurements of strain, with a long range (>100 km), small sensing size (<1 cm), and a huge absolute dynamic range, all in standard off-the-shelf telecom fiber. These sensors function by measuring the resonance frequency of the non-linear Brillouin interaction in fiber which shifts linearly with strain and temperature
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
Bally Ribbon Mills Bally, PA
This SAE Aerospace Information Report (AIR) reviews the precautions that must be taken and the corrections which must be evaluated and applied if the experimental error in measuring the temperature of a hot gas stream with a thermocouple is to be kept to a practicable minimum. Discussions will focus on Type K thermocouples, as defined in National Institute of Standards and Technology (NIST) Monograph 175 as Type K, nickel-chromium (Kp) alloy versus nickel-aluminium (Kn) alloy (or nickel-silicon alloy) thermocouples. However, the majority of the content is relevant to any thermocouple type used in gas turbine applications
As vehicle warranty claims, recalls, and maintenance costs continue to grow, new methods are needed to predict, detect, and diagnose vehicle health issues. By integrating artificial intelligence (AI) technology into the vehicle’s embedded electronics, automakers and fleet owners can benefit from highly effective and adaptable vehicle health management capabilities that are not available today. This paper describes how embedded AI-based signal integrity monitoring can be used to detect complex anomalous patterns in engines. It introduces a novel end-to-end signal integrity monitoring solution, which is based on a pipeline of machine learning models that are trained in an unsupervised manner. It also describes how unsupervised deep learning technology can simplify the data collection and labeling process that is needed to train the AI-based vehicle health management models
The purpose of this research was to develop detection, interrogation, and data processing techniques that leverage the unique features of multimode fibers to build next-generation fiber sensors with increased functionalities and performance
This SAE Aerospace Recommended Practice (ARP) is intended to document the process of landing gear system development. This document includes landing gear system development plans for commercial/military, fixed wing, and rotary wing air vehicles
This SAE Aerospace Standard defines the requirements for establishing a Nondestructive Inspection (NDI) program for aerospace systems to include but not not be limited to aircraft structure, aircraft stores (external structures such as antennas, pods, fuel tanks, weapons, radomes, etc.) and missile/rocket structural components when an NDI Program Plan is required by contract. NDI Programs are essential to ensuring NDI processes are implemented to support the lifecycle design requirements of the system and its components. NDI Programs are applicable to all phases of the system life cycle, including acquisition, modification, and sustainment. This standard may also be applicable to mechanical equipment, subsystems, and propulsion systems, but the requirements defined by the NDI Program Plan should be tailored by the contracting agency for such use. An NDI Program Plan shall be developed at the beginning of the technology development phase and shall define all NDI requirements to be
Load-time histories can be used to predict vehicle durability by calculating the pseudo damage (PD) through one or more load paths for a vehicle. When the dynamics of each load path are taken into account, a PD density (damage per distance traveled) can be expressed for each load path for any given road input to a vehicle. When damage is expressed as a PD density for a segment of road, separable damaging events can be identified using the PD density in all load paths of interest for a vehicle. However, it would be beneficial if events with similar damage characteristics can be identified and grouped together to provide an additional level of durability information. The objective of this work is to develop a similarity test for identifying the similarity/dissimilarity between multiple damaging events using the damage characteristics in multiple load paths. The damage characteristics for events are defined using the distribution of PD density samples for all known load paths. The
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