Browse Topic: Diagnostics
Traditional vehicle diagnostics often rely on manual inspections and diagnostic tools, which can be time-consuming, inconsistent, and prone to human error. As vehicle technology evolves, there is a growing need for more efficient and reliable diagnostic methods. This paper introduces an innovative AI-based diagnostic system utilizing Artificial Intelligence (AI) to provide expert-level analysis and solutions for automotive issues. By inputting various details such as the vehicle’s make, model, year, mileage, problem description, and symptoms, the AI system generates comprehensive diagnostics, identifies potential causes, suggests step-by-step repair solutions, and offers maintenance tips. The proposed system aims to enhance diagnostic accuracy and efficiency, ultimately benefiting mechanics and vehicle owners. The system’s effectiveness is evaluated through various experiments and case studies, showcasing its potential to revolutionize vehicle diagnostics
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 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 Modern data loggers of industrial bus networks provide a useful tool to record the bus traffic associated critical vehicle systems, but provide little insight into the impact of maintenance patches on the associated system binary codes and system behaviors. This paper describes an emerging DARPA technology, the Tactical Smart Network Interface Card (TSNIC), that provides a secure base from which to deploy, monitor, and interact with patched binaries. Our TSNIC appliance can take either a passive or active presence on the vehicle bus, obviating the need for a vulnerable JTAG interface, and processes diagnostic messages arriving from the patched binary. These messages can provide a wide range of insights into the behavior of the system. The Tactical Smart NIC represents the next-generation of secure and reliable patching technology for military and heavy industrial systems. It provides a unique way for developers, maintainers, and field engineers to gain a new appreciation for
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 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 Supporting Open Architecture is a key to most major automation and control suppliers. In every industry, there is a desire to make a unified control system architecture that can easily integrate control system equipment from multiple suppliers. Whether it is a Navy military application or an industrial application, the needs are almost identical. Some of the keys to providing this transparency among control systems are utilizing an open standard that can pull together communications from multiple suppliers. In this paper, SIEMENS will demonstrate the capabilities of utilizing an open standard, which is PROFINET. By adhering to the PROFINET standards, Open Architecture is achieved at many levels in a naval application. Open Architecture is intended to yield modular, interoperable systems that adhere to open standards with published interfaces. As will be demonstrated by this paper, PROFINET provides these capabilities and more. By implementing PROFINET as the infrastructure for
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 This presentation will review the ongoing lessons learned from a joint Industry/DoD collaborative program to explore this area over the past 5 years. The discussion will review the effectiveness of integrating multiple new technologies (combined with select COTS elements) to provide a complete solution designed to reduce spares stockpiles, maximize available manpower, reduce maintenance downtime and reduce vehicle lifecycle costs. A number of new and emerging technology case studies involving diagnostic sensors (such as battery health monitors), knowledge management data accessibility, remote support-based Telematics, secure communication, condition-based software algorithms, browser-based user interfaces and web portal data delivery will be presented
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
In the realm of low-altitude flight power systems, such as electric vertical take-off and landing (eVTOL), ensuring the safety and optimal performance of batteries is of utmost importance. Lithium (Li) plating, a phenomenon that affects battery performance and safety, has garnered significant attention in recent years. This study investigates the intricate relationship between Li plating and the growth profile of cell thickness in Li-ion batteries. Previous research often overlooked this critical aspect, but our investigation reveals compelling insights. Notably, even during early stage of capacity fade (~ 5%), Li plating persists, leading to a remarkable final cell thickness growth exceeding 20% at an alarming 80% capacity fade. These findings suggest the potential of utilizing cell thickness growth as a novel criterion for qualifying and selecting cells, in addition to the conventional measure of capacity degradation. Monitoring the growth profile of cell thickness can enhance the
This SAE Recommended Practice supersedes SAE J1930 MAR2017 and is technically equivalent to ISO 15031-2. This document is applicable to all light-duty gasoline and diesel passenger vehicles and trucks, and to heavy-duty gasoline vehicles. Specific applications of this document include diagnostic, service and repair manuals, bulletins and updates, training manuals, repair databases, underhood emission labels, and emission certification applications. This document should be used in conjunction with SAE J1930DA Digital Annexes, which contain all of the information previously contained within the SAE J1930 tables. These documents focus on diagnostic terms applicable to electrical/electronic systems, and therefore also contain related mechanical terms, definitions, abbreviations, and acronyms. Even though the use and appropriate updating of these documents is strongly encouraged, nothing in these documents should be construed as prohibiting the introduction of a term, abbreviation, or
A new report from Clarivate Plc, London, UK, offers a predictive analysis of high-growth medical technology markets poised to generate over $1 billion in value or achieve double-digit growth within the next five years. The report, “Medical Technologies to Watch in 2024” underscores critical areas of significant investment. Medtech analysts pinpoint five technologies driving substantial clinical and commercial value in devices and diagnostics this year. These innovations hold immense promise for patients, potentially complementing or even supplanting traditional medications and biochemical solutions. Analysts are optimistic that 2024 will bring a more favorable economic climate for medtech competitors, noting that the macro trends remain positive
The University of Detroit Mercy Vehicle Cyber Engineering (VCE) Laboratory together with The University of Arizona is supporting Secure Vehicle Embedded Systems research work and course projects. The University of Detroit Mercy VCE Laboratory has established several testbeds to cover experimental techniques to ensure the security of an embedded design that includes: data isolation, memory protection, virtual memory, secure scheduling, access control and capabilities, hypervisors and system virtualization, input/output virtualization, embedded cryptography implementation, authentication and access control, hacking techniques, malware, trusted computing, intrusion detection systems, cryptography, programming security and secure software/firmware updates. The VCE Laboratory testbeds are connected with an Amazon Web Services (AWS) cloud-based Cyber-security Labs as a Service (CLaaS) system, which allows students and researchers to access the testbeds from any place that has a secure
With the widespread adoption of fuel cell electric vehicles, electrical insulation resistance is required for driver safety. However, there are two ways in which resistance decreases: the first is electrical shorts because of failure of high-voltage components, and the second is increased conductivity of fuel cell coolant because of depletion of ion exchange filter. In the conventional solution, since these two decreases could not be distinguished due to noise in the resistance value, a vehicle alerted customers without determining the cause and severity when the resistance value falls below a certain threshold. As a corrective maintenance, when an alert occurs, the vehicle is forced to be immediately delivered to the service center. However, in most cases where the alert came on, the cause was low-risk ion filter depletion. This resulted in customers complaining that they were startled and considering the alert to be non-threatening. As a result, the provider recommended customers to
This document is intended to satisfy the data reporting requirements of standardization regulations in the United States and Europe, and any other market that may adopt similar requirements in the future. This document specifies: a Message formats for request and response messages. b Timing requirements between request messages from external test equipment and response messages from vehicles, and between those messages and subsequent request messages. c Behavior of both the vehicle and external test equipment if data is not available. d A set of diagnostic services, with corresponding content of request and response messages. e Standardized source and target addresses for clients and vehicle. This document includes capabilities required to satisfy OBD requirements for multiple regions, model years, engine types, and vehicle types. At the time of publication many regional regulations are not yet final and are expected to change in the future. This document makes no attempt to interpret
Mass spectrometry (MS), which is used to identify molecules within a sample by measuring the mass-to-charge ratio of ions, is employed across many fields of study, including biology, chemistry, physics, and clinical medicine. As the technology continues to evolve, so will the applications that can benefit from this important tool
Next-generation vehicle electrical architectures will be based on highly sophisticated domain controllers called HPCs (high-performance computers). These HPCs are more alike gaming PCs than as the traditional ECUs (electronic control units). Today’s diagnostic communication protocol, e.g., UDS (Unified Diagnostic Services, ISO 14229-1) was developed for ECUs and is not fit to be used for HPCs. There is a new protocol being developed within ASAM, SOVD (service-oriented vehicle diagnostics), which is based on a RESTful API (REpresentational State Transfer Application Programming Interface) sent over http (hypertext transfer protocol). But OBD (OnBoard Diagnostic) under the emissions regulation is not yet updated for this shift of protocols and therefore vehicle manufacturers must support older OBD protocols (e.g., SAE J1979-2) during the transition phase. Another problem is that some of the software packages may fall under the DEC-ECU (diagnostic or emission critical electronic control
On-board diagnostics (OBD) systems support the protection of the environment against harmful pollutants such as carbon monoxide (CO), nitrogen oxide (NOx), hydrocarbons (HC) and particulate matters (PM) emitted by combustion engines. OBD regulations require passenger cars and light-, medium- and heavy-duty trucks to support a minimum set of diagnostic information to external (off-board) “generic” test equipment. For the purpose of communication, both the test equipment and the vehicle must support the same communication protocol stack. The communication protocol SAE J1979, also known as ISO 15031, that has been in use for decades will be replaced by SAE J1979-2 for vehicles with combustion engines and by SAE J1979-3 for zero-emission-vehicle (ZEV) propulsion systems
Thanks to artificial intelligence (AI), augmented reality (AR) has long shaped product development across a variety of areas, including the medtech industry. Use of these trends can significantly improve diagnostics and, therefore, treatment. This applies, for example, to surgery and to the adjustment of medication regimens to reflect the patient’s needs. To do this, medical practitioners use recommendations provided by AI, which in turn draws on a broad digital database
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