Browse Topic: Vehicle health management (VHM)

Items (213)
This document contains information and guidance necessary for the development of a representative, repeatable validation program that may be utilized to assess the capability of SHM systems. The nature of SHM data differs from that seen in traditional nondestructive evaluation (NDE) applications in that the position of SHM sensors is fixed and SHM data can be available much more frequently (if not continuously) over time. This document presents methodologies that can be used to arrive at SHM capability while considering the unique nature of SHM deployment. Each SHM system must be considered independently to determine the applicability and limitations of the guidance contained here for each SHM system being assessed.
Aerospace Industry Steering Committee on Structural Health
This study investigates the forced vibration characteristics of a functionally graded material (FGM) beam possessing a square cross-section and featuring a V-shaped crack. The FGM beam exhibits a gradual transition in mechanical composition from a ceramic to a metallic surface. Employing finite element analysis software, a comprehensive numerical analysis is conducted to evaluate the frequencies and mode shapes of the cracked FGM beam under simply supported boundary conditions. The study meticulously explores the effects of various crack parameters, including crack opening width, depth, and location. The findings highlight the significant influence of the crack opening width on the frequencies, indicating that wider cracks result in decreased frequencies across all mode shapes. Conversely, the impact of crack depth and location on the dynamic behavior of the cracked FGM beam within the studied ranges appears relatively minor. These insights offer valuable perspectives into the
D, ManishC V, PrasshanthN, SuhasBhaskara Rao, Lokavarapu
State of health (SOH) estimation is essential to ensure safety and reliability in the operation of Proton Exchange Membrane Fuel Cells (PEMFCs). The aging of fuel cells results from the deterioration of multiple internal components, and the aging degree of some key components even directly determines the end of cell life. Due to the complexity of the internal reactions in fuel cell, many internal parameters cannot be measured or recorded during aging tests. In addition, external characteristics do not reflect the internal changes in the cell. Therefore, establishing a multi-scale metric based on fuel cell components is very important for fuel cell life research. During the aging process of a fuel cell, the contributions of different components to the overall aging vary significantly. Additionally, the allocation of indicator parameters presents a challenge in multi-scale modeling. To address these issues, this paper proposes a method to construct multi-scale indicators for fuel cells
Lin, YipengMin, HaitaoSheng, XiaZhang, ZhaopuSun, Weiyi
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.
E-32 Aerospace Propulsion Systems Health Management
Aluminum alloys serve a critical role in the aerospace industry, accounting for a significant amount of commercial aircraft weight. Despite the growing use of composite materials, aluminum remains important in airframe construction due to its lightweight, cost-effectiveness, and high strength potential. Structural integrity is critical in modern engineering, necessitating early diagnosis and localization of damage. To detect the flaws, cracks, and cut-out in the structures, structural health monitoring (SHM) systems are essential, with non-destructive testing (NDT) methodologies playing critical roles. Among these technologies, ultrasonic guided wave testing (UGWT) has gained popularity because of its capacity to propagate over long distances and detect subsurface faults. This article investigates the use of UGWs to identify cut-outs in aluminum plates. The numerical investigation has been carried out using commercially available finite element software Abaqus. The ultrasonic lamb
Rajput, ArunPatil, Vaibhav KailasBhosale, AniketYadav, RiteshGhatge, AdityarajPandey, Anand Ji
This SAE Aerospace Recommended Practice (ARP) provides guidance to plan and perform validation and verification of IVHM systems. The intent of this ARP is to help the reader appreciate and understand additional objectives and activities of validation and verification processes, beyond validation and verification of the vehicle, that arise due to the nature of an IVHM System. This includes an end-to-end evaluation of the entire IVHM system, noting that IVHM is a “system of systems.” In order to perform validation and verification, the user must determine what they are using IVHM for, including the criticality of the application. The process should then determine appropriateness of the data corresponding to the application criticality. This document provides validation and verification guidance for IVHM as: (1) a system of systems, (2) a system, and (3) elements within a system. While this document is not intended to be prescriptive, it is a reference guide that highlights and discusses
HM-1 Integrated Vehicle Health Management Committee
This SAE Aerospace Information Report (AIR) provides guidance on using environmental, electrochemical, and electrical resistance measurements to monitor environment spectra and corrosivity of service environments, focusing on parameters of interest, existing measurement platforms, deployment requirements, and data processing techniques. The sensors and monitoring systems provide discrete time-based records of (1) environmental parameters such as temperature, humidity, and contaminants; (2) measures of alloy corrosion of the sensor; and (3) protective coating performance of the sensor. These systems provide measurements of environmental parameters, sensor material corrosion rate, and sensor coating condition for use in assessing the risk of atmospheric corrosion of a structure. Time-based records of environment spectra and corrosivity can help determine the likelihood of corrosion to assess the risk of corrosion damage of the host structure for managed assets and aid in establishing
HM-1 Integrated Vehicle Health Management Committee
This Aerospace Recommended Practice (ARP) is a general overview of typical airborne engine vibration monitoring (EVM) systems applicable to fixed or rotary wing aircraft applications, with an emphasis on system design considerations. It describes EVM systems currently in use and future trends in EVM development. The broader scope of Health and Usage Monitoring Systems, (HUMS) is covered in SAE documents AS5391, AS5392, AS5393, AS5394, AS5395, AIR4174. This ARP also contains the essential elements of AS8054 which remain relevant and which have not been incorporated into Original Equipment Manufacturers (OEM) specifications.
E-32 Aerospace Propulsion Systems Health Management
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
Kolappan Geetha, GaneshRavi, Nitin. BRoy Mahapatra, Debiprosad
Aviation industry is striving to leverage the technological advancements in connectivity, computation and data analytics. Scalable and robust connectivity enables futuristic applications like smart cabins, prognostic health management (PHM) and AI/ML based analytics for effective decision making leading to flight operational efficiency, optimized maintenance planning and aircraft downtime reduction. Wireless Sensor Networks (WSN) are gaining prominence on the aircraft for providing large scale connectivity solution that are essential for implementing various health monitoring applications like Structural Health Monitoring (SHM), Prognostic Health Management (PHM), etc. and control applications like smart lighting, smart seats, smart lavatory, etc. These applications help in improving passenger experience, flight operational efficiency, optimized maintenance planning and aircraft downtime reduction. Intra Aircraft WSNs (IAWSN) used for such applications are expected to provide robust
C S, AdisheshaRamamurthy, PrasannaBanerjee, KumardebBarik, Mridul Sankar
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
Khan, SamirWalthall, RhondaRajamani, RaviHolland, Steve
This paper presents deep learning-based prognostics and health management (PHM) for predicting fractures of an electric propulsion (eP) drivetrain system using real-time CAN signals. The deep learning algorithm, based on autoencoders, resamples time-series signals and converts them into 2D images using recurrence plots (RP). Subsequently, through unsupervised learning of DeepSVDD, it detects anomalies in the converted 2D images and predicts the failure of the system in real-time. Also, reliability analysis based on fracture mechanics was performed using the detected signals and big data. In particular, the severity of the eP drivetrain system is proportional to the maximum shear stress (τmax) in terms of linear elastic fracture mechanics (LEFM) and can be calculated by summarizing the relationship between cracks (a) and the stress intensity factor (KIII). During this process, the system status can be checked by comparing the stress intensity factor and fracture toughness (KIIIc), and
Moon, ByungwooLee, SangWonNam, DongJinKim, JeonghwanBae, JaeWoongShin, JeongMin
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
E-32 Aerospace Propulsion Systems Health Management
To many, a digital twin offers “functionality,” or the ability to virtually rerun events that have happened on the real system and the ability to simulate future performance. However, this requires models based on the physics of the system to be built into the digital twin, links to data from sensors on the real live system, and sophisticated algorithms incorporating artificial intelligence (AI) and machine learning (ML). All of this can be used for integrated vehicle health management (IVHM) decisions, such as determining future failure, root cause analysis, and optimized energy performance. All of these can be used to make decisions to optimize the operation of an aircraft—these may even extend into safety-based decisions. The Adoption of Digital Twins in Integrated Vehicle Health Management, however, still has a range of unsettled topics that cover technological reliability, data security and ownership, user presentation and interfaces, as well as certification of the digital twin’s
Phillips, Paul
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.
E-32 Aerospace Propulsion Systems Health Management
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.
E-32 Aerospace Propulsion Systems Health Management
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
AMS K Non Destructive Methods and Processes Committee
This SAE Aerospace Recommended Practice (ARP) provides guidance when creating integrated vehicle health management (IVHM) system architecture. IVHM covers a vehicle’s monitoring and data processing functions inherent within its sub-systems, and the tools and processes used to manage and restore the vehicle health. These guidelines are drawn from experience within both defense and commercial IVHM initiatives and implementations. The document identifies a step-by-step methodology to expose functional and non-functional requirements, mature the architecture and support organizational business goals and objectives.
HM-1 Integrated Vehicle Health Management Committee
Structural Health Monitoring (SHM), especially in the field of rotary machinery diagnosis, plays a crucial role in determining the defect category as well as its intensity in a machine element. This paper proposes a new framework for real-time classification of structural defects in a roller bearing test rig using time domain-based classification algorithms. Along with the bearing defects, the effect of eccentric shaft loading has also been analyzed. The entire system comprises of three modules: sensor module – using accelerometers for data collection, data processing module – using time-domain based signal processing algorithms for feature extraction, and classification module – comprising of deep learning algorithms for classifying between different structural defects occurring within the inner and outer race of the bearing. Statistical feature vectors comprising of Kurtosis, Skewness, RMS, Crest Factor, Mean, Peak-peak factor etc. have been extracted from the 1-D time series data
Gorantiwar, AnishTaheri, SaiedZahiri, FeraidoonMoslehi, Bijan
The automotive industry changes rapidly. New players, concepts, and technologies from the Information Technology (IT) domain enter the market and software receives a high priority. Inside the vehicle, the number of components, which consist mostly of software, are increasing and more and more software-based functions are offered. In addition, High Performance Computers (HPCs) are continuing to be integrated into vehicles. These aspects lead to several challenges with current vehicle diagnostics, but also enable new opportunities in that field. However, in the specific area of vehicle diagnostics, there exists only very limited literature that considers current challenges and new possibilities for future vehicle diagnostics. Some literature deals with the general automotive system design or shows results from about five years ago. The viewpoints of an Original Equipment Manufacturer (OEM) are not included there. This paper presents results from an expert survey in order to identify what
Bickelhaupt, SandraHahn, MichaelNuding, NikolaiMorozov, AndreyWeyrich, Michael
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
HM-1 Integrated Vehicle Health Management Committee
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.
E-32 Aerospace Propulsion Systems Health Management
Simulations play an important role in the continuing effort to reduce development time and risks. However, large and complex models are necessary to accurately simulate the dynamic behavior of complex engineering systems. In recent years, the use of data-driven models based on machine learning (ML) algorithms has become popular for predicting the structural dynamic behavior of mechanical systems. Due to their advantages in capturing non-linear behavior and efficient calculation, data-driven models are used in a variety of fields like uncertainty quantification, optimization problems, and structural health monitoring. However, the black box structure of ML models reduces the interpretability of the results and complicates the decision-making process. Hierarchical Bayesian Networks (HBNs) offer a framework to combine expert knowledge with the advantages of ML algorithms. In general, Bayesian Networks (BNs) allow connecting inputs, parameters, outputs, and experimental data of various
Hülsebrock, MoritzSchmidt, HendrikStoll, GeorgAtzrodt, Heiko
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.
Apartsin, SashaStein, HilikReiter, GilWilliams, KyleMoscovich, Noam
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.
Accelerometers are transducers, or sensors, that convert acceleration into an electrical signal that can be used for airframe, drive, and propulsion system vibration monitoring and analysis within vehicle health and usage monitoring systems. This document defines interface requirements for accelerometers and associated interfacing electronics for use in a helicopter Health and Usage Monitoring System (HUMS). The purpose is to standardize the accelerometer-to-electronics interface with the intent of increasing interchangeability among HUMS sensors/systems and reducing the cost of HUMS accelerometers. Although this interface was specified with an internally amplified piezoelectric accelerometer in mind for Airframe and Drive Train accelerometers, this does not preclude the use of piezoelectric accelerometer with remote charge amplifier or any other sensor technology that meets the requirements given in this specification. This SAE HUMS Accelerometer Interface Specification includes the
HM-1 Integrated Vehicle Health Management Committee
This document is applicable to civil aerospace airframe structural applications where stakeholders are seeking guidance on the definition, development, and certification of structural health monitoring (SHM) technologies for aircraft health management applications. Inputs to the structural health management are obtained from SHM equipment and/or from onboard sensors, delivering the detection and characterization of damage, load, or environmental parameters for operational and damage monitoring. For the purpose of this document, SHM is defined as “the process of acquiring and analyzing data from on-board sensors to characterize the health of a structure.” The suite of on-board sensors could include any presently installed aircraft sensors, as well as new sensors to be defined in the future.
Aerospace Industry Steering Committee on Structural Health
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
Bond, W. GlennPokoyoway, AndrewDaniszewski, DavidLucas, CesarArnold, Thomas L.Dozier, Haley R.
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
Majcher, MonicaBennett, Lorri A.Banks, JeffreyLukens, MatthewNulton, EricYukish, Michael A.Merenich, John J.
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.
Ludwig, AndrewTagliente, Daniel
This document collates the ways and means that existing sensors can identify the platform’s exposure to volcanic ash. The capabilities include real-time detection and estimation, and post flight determinations of exposure and intensity. The document includes results of initiatives with the Federal Aviation Administration (FAA), the European Aviation Safety Agency (EASA), the International Civil Aviation Organization (ICAO), Transport Canada, various research organizations, Industry and other subject matter experts. The document illustrates the ways that an aircraft can use existing sensors to act as health monitoring tools so as to assess the operational and maintenance effects related to volcanic ash incidents and possibly help determine what remedial action to take after encountering a volcanic ash (VA) event. Finally, the document provides insight into emerging technologies and capabilities that have been specifically pursued to detect volcanic ash encounters but are not yet a part
HM-1 Integrated Vehicle Health Management Committee
This SAE Aerospace Recommended Practice (ARP) offers an overview of the many key processes that are being transformed as the aerospace industry is rapidly digitalizing. The G-31 Electronic Transactions in Aerospace committee has been established to develop standards related to these processes. This report, also known as the “cornerstone” document for the committee, is a comprehensive look at processes associated with commercial aviation. Because of universal convergence of these technologies, the technologies described here are applicable to other domains as well.
G-31 Digital Transactions for Aerospace
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.
A-5 Aerospace Landing Gear Systems Committee
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
AMS K Non Destructive Methods and Processes Committee
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
HM-1 Integrated Vehicle Health Management Committee
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
Altmann, CraigFerris, John
This SAE Aerospace Information Report (AIR) provides guidance on the definition, development, integration, qualification/certification, and deployment of Structural Health Monitoring (SHM) technologies applied to commercial and military rotorcraft. Increased implementation of SHM is believed to have numerous potential benefits, including enhanced operational safety and reduced maintenance burden. The focus is on augmenting ARP6461 to address specific unique aspects of implementing SHM on rotorcraft without unnecessarily duplicating guidance already contained in the ARP that is generally applicable to both fixed-wing and rotary-wing aircraft. For the purpose of this document, SHM is defined as “the process of acquiring and analyzing data from on-board sensors in order to determine the health of a structure”. Note that this is irrespective of whether the on-board sensors are a permanent or temporary installation. On-board sensors could include any presently installed aircraft sensors as
Aerospace Industry Steering Committee on Structural Health
This Aerospace Information Report (AIR) presents metrics for assessing the performance of diagnostic and prognostic algorithms and systems delivering propulsion health management functions.
E-32 Aerospace Propulsion Systems Health Management
To realize a fast and high-precision online state-of-health (SOH) estimation of lithium-ion (Li-Ion) battery, this article proposes a novel SOH estimation method. This method consists of a new SOH model and parameters identification method based on an improved genetic algorithm (Improved-GA). The new SOH model combines the equivalent circuit model (ECM) and the data-driven model. The advantages lie in keeping the physical meaning of the ECM while improving its dynamic characteristics and accuracy. The improved-GA can effectively avoid falling into a local optimal problem and improve the convergence speed and search accuracy. So the advantages of the SOH estimation method proposed in this article are that it only relies on battery management systems (BMS) monitoring data and removes many assumptions in some other traditional ECM-based SOH estimation methods, so it is closer to the actual needs for electric vehicle (EV). By comparing with the traditional ECM-based SOH estimation method
Fang, LiuXinyi, LiuWeixing, SuHanning, ChenMaowei, HeXiaodan, Liang
Computational models directly derived from data gained increased interest in recent years. Data-driven approaches have brought breakthroughs in different research areas such as image-, video- and audio-processing. Often denoted as Machine Learning (ML), today these approaches are not widely applied in the field of vehicle Noise, Vibration and Harshness (NVH). Works combining ML and NVH mainly discuss the topic with respect to psychoacoustics, traffic noise, structural health monitoring and as improvement to existing numerical simulation methods. Vehicle interior noise is a major quality criterion for today’s automotive customers. To estimate noise levels early in the development process, deterministic system descriptions are created by utilizing time-consuming measurement techniques. This paper examines whether pattern-recognizing algorithms are suitable to conduct the prediction process for a steering system. Starting from operational measurements, a procedure to calculate the sound
Tsokaktsidis, Dimitrios ErnstNau, ClemensMarburg, Steffen
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