Browse Topic: Diagnostics

Items (563)
The trend for the future mobility concepts in the automotive industry is clearly moving towards autonomous driving and IoT applications in general. Today, the first vehicle manufacturers offer semi-autonomous driving up to SAE level 4. The technical capabilities and the legal requirements are under development. The introduction of data- and computation-intensive functions is changing vehicle architectures towards zonal architectures based on high-performance computers (HPC). Availability of data-connection to the backend and the above explained topics have a major impact on how to test and update such ‘software-defined’ vehicles and entire fleets. Vehicle diagnostics will become a key element for onboard test and update operations running on HPCs, as well as for providing vehicle data to the offboard backend infrastructure via Wi-Fi and 5G at the right time. The standard for Service Oriented Vehicle Diagnostics (SOVD) supports this development. It describes a programming interface for
Mayer, JulianBschor, StefanFieth, Oliver
The problem of monitoring the parametric failures of a traction electric drive unit consisting of an inverter, a traction machine and a gearbox when interacting with a battery management system has been solved. The strategy for solving the problem is considered for an electric drive with three-phase synchronous and induction machines. The drive power elements perform electromechanical energy conversion with additional losses. The losses are caused by deviations of the element parameters from the nominal values during operation. Monitoring gradual failures by additional losses is adopted as a key concept of on-board diagnostics. Deviation monitoring places increased demands on the information support and accuracy of mathematical models of power elements. We take into account that the first harmonics of currents and voltages of a three-phase circuit are the dominant energy source, higher harmonics of PWM appear as harmonic losses, and mechanical losses in the rotor and gearbox can be
Smolin, VictorGladyshev, SergeyTopolskaya, Irina
SAE J1939 is a CAN-based standard used for connecting various ECUs together within a vehicle. There are also some related protocols sharing many of the features of SAE J1939 across other industries including ISO11783, RVC and NMEA 2000. The standard has enabled the easy integration of electronic devices into a vehicle. However, as with all CAN-based protocols, several vulnerabilities to cyberattacks have been identified and are discussed in this paper. Many are at the CAN-level, whilst others are in common with those protocols from the SAE J1939 family of protocols. This paper reviews the known vulnerabilities that have been identified with the SAE J1939 protocol at CAN and J1939-levels, along with proposed mitigation strategies that can be implemented in software. At the CAN-level, the weaknesses include ways to spoof the network by exploiting parts of the protocol. Denial of Service is also possible at the CAN-level. At the SAE J1939-level, weaknesses include Denial of Service type
Quigley, Christopher
This paper presents Matchit, a novel method for expediting issue investigation and generating actionable insights from textual data. Recognizing the challenges of extracting relevant information from large, unstructured datasets, we propose a domain-adaptable approach by integrating expert domain knowledge to guide Large Language models (LLMs) to automatically identify and categorize key information into distinct topics. This process offers two key functionalities: fully automatic topic extraction based solely on input data, providing a concise overview of the problem and potential solutions, and user-guided extraction, where domain experts can specify the type of information or pre-defined categories to target specific insights. This flexibility allows for both broad exploration and focused analysis of the data. Matchit's efficacy is demonstrated through its application in the automotive industry, where it successfully extracts repair diagnostics from diverse textual sources like
Wang, LijunArora, Karunesh
This paper describes a novel invention which is an Intrusion Detection System based on fingerprints of the CAN bus analogue features. Clusters of CAN message analogue signatures can be associated with each ECU on the network. During a learning mode of operation, fingerprints can be learnt with the prior knowledge of which CAN identifier should be transmitted by each ECU. During normal operation, if the fingerprint of analogue features of a particular CAN identifier does not match the one that was learnt then there is a strong possibility that this particular CAN identifier’s message is symptomatic of a problem. It could be that the message has been sent by either an intruder ECU or an existing ECU has been hacked to send the message. In this case an intruder can be defined as a device that has been added to the CAN bus OR a device that has been hacked/manipulated to send CAN messages that it was not designed to (i.e. could be originally transmitted by another device). It could also be
Quigley, ChristopherCharles, David
The generation of data plays a vital role in machine learning (ML) techniques by providing the foundation for training and improvement of forecast models. As one application area for these models, in-vehicle systems, like vehicle diagnostics, have the potential to enhance the reliability and durability of vehicles by utilizing ML models in the testing phases. However, acquiring a high volume of quality onboard diagnostics (OBD) data is time-consuming and poses challenges like the risk of exposing sensitive information. To address this issue, synthetic data generation offers a promising alternative that is already in use in other domains. Thereby, synthetic data allows the exploitation of knowledge found in original data, ensuring the privacy of sensitive data, with less time costs of data acquisition. The application of such synthetically generated data could be found in predictive maintenance, predictive diagnostics, anomaly detection, and others. For this purpose, the research
Vučinić, VeljkoHantschel, FrankKotschenreuther, Thomas
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.
Sasikala, T.Swathi, B.Raj, J. Joshua DanielShetty, G. ShreyasDidagur, Darshan
As a journey to green initiatives, one of the focus areas for automotive industry is reducing environmental impact especially in case of internal combustion engines. Latest digital twin technology enable modelling complicated, fast and unsteady phenomena including the changes of emission gases concentration and output torque observed during diesel emission and combustion process. This paper presents research on the emission and combustion characteristics of a heavy vehicle diesel engine, elaborating an engineered architecture for prognostics/diagnostics, state monitoring, and performance trending of heavy-duty vehicle engine (HDVE) and after treatment system (ATS). The proposed architecture leverages advanced modeling methodologies to ensure precise predictions and diagnostics, using data-driven techniques, the architecture accurately model’s engine and exhaust system behaviors under various operating conditions. For exhaust system, architecture demonstrates encouraging predictive
Singh, PrabhsharnThakare, UjvalHivarkar, Umesh
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
Zhang, JianZheng, Yiting
This study explores the effectiveness of two machine learning models, namely multilayer perceptron neural networks (MLP-NN) and adaptive neuro-fuzzy inference systems (ANFIS), in advancing maintenance management based on engine oil analysis. Data obtained from a Mercedes Benz 2628 diesel engine were utilized to both train and assess the MLP-NN and ANFIS models. Six indices—Fe, Pb, Al, Cr, Si, and PQ—were employed as inputs to predict and classify engine conditions. Remarkably, both models exhibited high accuracy, achieving an average precision of 94%. While the radial basis function (RBF) model, as presented in a referenced article, surpassed ANFIS, this comparison underscored the transformative potential of artificial intelligence (AI) tools in the realm of maintenance management. Serving as a proof-of-concept for AI applications in maintenance management, this study encourages industry stakeholders to explore analogous methodologies. Highlights Two machine learning models, multilayer
Pourramezan, Mohammad-RezaRohani, Abbas
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
Vehicle E E System Diagnostic Standards Committee
Software will lead the development and life cycle of vehicles in the future. Nowadays, more and more software is being integrated into a vehicle, evolving it into a Software-Defined Vehicle (SDV). Automotive High Performance Computers (HPCs) serve as enablers by providing more computing infrastructure which can be flexibly used inside a vehicle. However, this leads to a complex vehicle system that needs to function today and in the future. Detecting and rectifying failures as quickly as possible is essential, but existing diagnostic approaches based on Diagnostic Trouble Codes (DTCs) are not designed for such complex systems and lack of flexibility. DTCs are predefined during vehicle development and changes to vehicle diagnostics require a large amount of modification work. Moreover, diagnostics are not intended to handle dynamically changing software systems and have shortcomings when applied to in-vehicle software systems. In the Cloud, there are already established approaches to
Bickelhaupt, SandraHahn, MichaelMorozov, AndreyWeyrich, Michael
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
Zachos, MarkSatam, PratikNaama, Rami
In the ever-evolving landscape of automotive technology, the need for robust security measures and dependable vehicle performance has become paramount with connected vehicles and autonomous driving. The Unified Diagnostic Services (UDS) protocol is the diagnostic communication layer between various vehicle components which serves as a critical interface for vehicle servicing and for software updates. Fuzz testing is a dynamic software testing technique that involves the barrage of unexpected and invalid inputs to uncover vulnerabilities and erratic behavior. This paper presents the implementation of fuzz testing methodologies on the UDS layer, revealing the potential vulnerabilities that could be exploited by malicious entities. By employing both open-source and commercial fuzzing tools and techniques, this paper simulates real-world scenarios to assess the UDS layer’s resilience against anomalous data inputs. Specifically, we deploy several open-source UDS implementations on a
Çelik, LeventMcShane, JohnScott, ChristianAideyan, IwinosaBrooks, RichardPese, Mert D.
In recent years, the automotive industry has been making efforts to develop vehicles that satisfy customers’ emotions rather than malfunctions by improving the durability of vehicles. The durability and reliability of vehicles sold in the U.S. can be determined through the VDS (Vehicle Dependability Study) published by JD Power. The VDS is index which is the number of complaints per 100 units released by J.D. POWER in every year. It investigates customers who have used it for 3 years after purchasing a new car and consists of 177 specific problems grouped into 8 categories such as PT, ACEN, FCD, Exterior. The VDS-4 has been strengthened since the introduction of the new evaluation system VDS-5 in 2015. In order to improve the VDS index, it is important to gather various customer complaints such as internet data, warranty data, Enprecis data and clarify the problem and cause. Enprecis data is survey of customer complaints by on-line in terms of VDS. In the case of warranty and Enpreics
You, Hanmin
SAE J1979 and its “OBD Modes” served for the protection of our environment against harmful pollutants for decades, but due to regulatory adoption of Unified Diagnostic Services (UDS), SAE J1979 has now become a multiple part document series: SAE J1979 will be replaced by SAE J1979-2 for vehicles with combustion engines (ICEs) and by SAE J1979-3 for Zero Emission Vehicle (ZEV) propulsion systems. For ZEVs, emission-related failures will be replaced by ZEV propulsion-related failures. Both SAE J1979-2 and -3 are variants of ISO 14229 (UDS) but limited to emission-related and ZEV propulsion-related failures, respectively, and associated diagnostic data. These new diagnostic communication protocols are required by California Air Resources Board (CARB) but do not support vehicle-manufacturer-specific diagnostic applications like calibration or flash programming. For performance reasons of the flash process, the deployment of UDS on Internet Protocol (UDSonIP) as it is standardized in ISO
Subke, PeterHeineman, LindseyMayer, Julian
In the diagnosis of membrane flooding and drying faults in a Proton Exchange Membrane Fuel Cell (PEMFC) through Electrochemical Impedance Spectroscopy (EIS), this paper proposes a Genetic Algorithm (GA)-based feature selection method for selecting the required frequency points of failure, to reduce the measurement time taken by EIS while ensuring high diagnostic accuracy. This feature selection method searches the feature space through GA and proposes an encoding method tailored to this problem. During the searching process, three algorithms, i.e., Backpropagation Neural Network (BPNN), K-Nearest Neighbor (KNN), and eXtreme Gradient Boosting (XGBoost), are used to extract various features and select higher diagnostic rates of feature frequencies. Comparisons are made between the feature frequencies selected by the proposed method and those selected by conventional methods based on empirical experience, and it is found that the feature frequencies selected by the proposed method have
Guan, PengShen, YitaoWang, ZheyuBai, YuXinJi, ZhaoQi
To develop safe vehicles, system development must be performed in compliance with functional safety. Functional safety considers situations where failures could make a vehicle unsafe, and it requires the inclusion of mechanisms to detect and mitigate these failures, even though they may not always be detected with 100% certainty — referred as diagnostic coverage (DC). Therefore, some faults, called residual faults, might go undetected. In the realm of functional safety from a communication perspective, industry standards define nine distinct fault modes. The detection of these faults is crucial, especially in the widely used AUTOSAR automotive operating system. AUTOSAR E2E (End-to-End Communication Protection) serves as a communication fault detection mechanism utilizing three mechanisms: counters, timers, and Cyclic Redundancy Check (CRC) to address the nine fault modes. Especially, determining the DC for CRC can be challenging and often requires a conservative evaluation approach. In
Emi, TaichiAung, Han NayYamasaki, YasuhiroOhsaki, Hiroyuki
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
Jang, Wook Il (Woogil)Kim, Seong-Mok
In the rapidly evolving automotive landscape, integrating cutting-edge off-board diagnostics tools has triggered a paradigm shift in diesel engine applications. Simultaneously, engineers are compelled to transform conventional mechanical engines into advanced common rail direct injection (CRDi) systems amidst India’s changing pollution norms for industries. Aligned with Bharat Stage Emission Standards, non-road vehicles face stringent emission limits, necessitating complex electronic control predominantly managed by the engine control unit (ECU). Government mandates require the ECU to detect NOx control malfunctions and emission-affecting faults, storing data for off-board analysis. A tool that can read engine data and monitor engine health is required to deal with this situation. Network protocols such as CAN enable remote communication with specialized ECUs. This study examines implementing customized off-board tools, which helps easier coordination with protocols such as the unified
Ayachit, Vedashree VikasGandhi, NareshKakade, Suhas
High Fidelity Communication has become a necessity in various sectors. Different wireless data transfer methods play a vital role in various far field and near-field communications. Wireless communication for transferring data through radio spectrum has been a continuous evolving trend, especially in Automotive Sector, with fleet monitoring, platooning and even connected vehicles. Some important parameters considered in selecting a wireless platform would be bandwidth, data transfer, speed and security. Some interesting advantages of communication over the visible spectrum has led to the evolution of Light Fidelity. Implementation of Visible Light Communication (VLC) in the automotive field might enable safer driving conditions through vehicle-to-vehicle (V2V) and vehicle to Infrastructure (V2I) communication with high data transmission rates and efficient-bandwidth usage. The principle of VLC is based on “line of sight” data transmission through modulation of the light source. Highly
Ali, Rifat FahmidaN, PremNatarajan, AkshayKashi, Anitha
For commercial vehicles, reliability is key since the vehicle is typically linked to the daily earnings of the owner. To ensure continuous vehicle operation, early diagnostics of critical issues and proactive maintenance are important. However, an electric vehicle is a complex and dynamic system consisting of numerous components interacting with each other and with external environments such as road conditions, traffic, weather, and driving behavior. Thus, vehicle operation and performance are highly contextual and for identifying an abnormal operation (diagnostics) the solution must consider the conditions under which it is driven. To address this, the paper proposes an AI-based digital twin of an electric three-wheeler vehicle. TabNet a deep-learning based model is used to learn and generate near-ideal vehicle behavior. The focus of the paper is motor subsystem. The model is trained using appx 200 vehicles first 1500 km driven data. To ensure, the digital twin model learns near-ideal
Jain, SiddhantKumar, VedantSoni, NimishSaran, Amitabh
Neutron diffraction is a powerful tool for noninvasive and nondestructive characterization of materials and can be applied even in large devices such as internal combustion engines thanks to neutrons’ exceptional ability to penetrate many materials. While proof-of-concept experiments have shown the ability to measure spatially and temporally resolved lattice strains in a small aluminum engine on a timescale of minutes over a limited spatial region, extending this capability to timescales on the order of a crank angle degree over the full volume of the combustion chamber requires careful design and optimization of the engine structure to minimize attenuation of the incident and diffracted neutrons to maximize count rates. We present the design of a “neutronic engine,” which is analogous to an optical engine in that the materials and external geometry of a typical automotive engine have been optimized to maximize access of the diagnostic while maintaining the internal combustion chamber
Wissink, MartinWray, Christopher L.Lee, P.M.Hoffmeyer, Matthew M.Frost, Matthew J.An, KeChen, Yan
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
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
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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.
Microgrids are a topic of interest in recent years, largely due to their compatibility with the integration of distributed renewable resources, capability for bidirectional power flow, and ability to reconfigure to mitigate the effects of faults. Fault diagnosis algorithms are a foundational technology for microgrids. These algorithms must have two primary capabilities. First, faults must be detectable; it is known when the fault occurs. Second, faults must be isolable; the type and location of detected faults can be determined. However, most fault handling research considering microgrids has focused on the protection algorithm. Protection algorithms seek to quickly extinguish dangerous faults which can damage components. However, these algorithms may not sufficiently capture less severe faults, or provide comprehensive monitoring for the microgrid. This is particularly relevant when considering applications involving fault tolerant control or dynamic grid reconfiguration. Although
Heyer, GabrielD'Arpino, Matilde
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
Pauli, Joakim
Decades ago, like the 1990s automobile industry, the off-highway industry was purely recognized as a mechanical entity. In the mechanical system, accuracy and troubleshooting of faults were significant concerns. Additionally, the continuous stringent emission norms by the government call for the adaptation of the aftertreatment and DeNOx led to more complexity and challenges. To meet the government emission regulation and product performance, thorough functionality testing of manufactured units was crucial. For this purpose, EOL/diagnostics testers are developed. Diagnostic protocol CAN establish the connection between ECU and tester due to its robustness and data handling capabilities. This paper aims to develop and test the end-of-line (EOL) tester for off-highway diesel engines. The communication between the tester and ECU will be over UDSonCAN, conforming to standard ISO14229. This tester will cover the synchronization of various components used to assemble the engine and maintain
Khond, Nikita AnilGandhi, NareshKakade, Suhas
In order to guarantee the dependability and effectiveness of industrial machinery, real-time gearbox malfunction detection is extremely important. Traditional approaches to condition monitoring systems sometimes rely on time-consuming human inspections or routine maintenance, which can result in unanticipated failures and expensive downtime. The rise of the industrial Internet of things (IIoT) in recent years has paved the way for more sophisticated and automated monitoring methods. An IIoT-based condition monitoring system is suggested in this study for real-time gearbox failure detection. The gearbox health state is continually monitored by the system using sensor data from the gearbox, such as temperature, vibration, and oil analysis. Real-time transmission of the gathered data is made to a central monitoring hub, where sophisticated analytics algorithms are used to look for any flaws. This study’s potential to improve the dependability and operational effectiveness of industrial
Sivaraman, P.Ilakiya, P.Prabhu, M.K.Ajayan, Adarsh
We introduce novel approaches utilizing Physics Informed Machine Learning (PIML) for advanced diagnostics & prognostics of ground combat vehicles (CV). Specifically, we present the development of a PIML model designed to predict the health of engine oil in diesel engines. The condition of engine oil is closely linked to engine wear, thus serving as a crucial indicator of engine health. Our model integrates a physics-based simulation of engine wear in diesel engines, leveraging a time history of engine oil viscosity and engine speed as key input parameters. Furthermore, we conduct uncertainty quantification to assess the impact of varying parameters on engine oil health prediction. Additionally, our model demonstrates the capability to enhance low-fidelity physics models through the integration of a limited set of experimental data. By combining data-driven techniques with physics-based insights, our approach offers enhanced diagnostics and prognostics capabilities for ground combat
Betts, Juan F.Alizadeh, Arash
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.
The development of predictive maintenance has become one of the most important drivers of innovation, not only in the maritime industry. The proliferation of on-board and remote sensing and diagnostic systems is creating many new opportunities to reduce maintenance costs and increase operational stability. By predicting impending system faults and failures, proactive maintenance can be initiated to prevent loss of seaworthiness or operability. The motivation of this study is to optimize predictive maintenance in the maritime industry by determining the minimum useful remaining lead-acid battery capacity measurement frequency required to achieve cost-efficiency and desired prognostic performance in a remaining battery capacity indication system. The research seeks to balance operational stability and cost-effectiveness, providing valuable insight into the practical considerations and potential benefits of predictive maintenance. The methodology employed in this study includes outlining
Golovan, AndriiGritsuk, IgorHoncharuk, Iryna
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.
More and more applications (apps) are entering vehicles. Customers would like to have in-car apps in their infotainment system, which they already use regularly on their smartphones. Other apps with new functionalities also inspire vehicle customers, but only as long as the customer can utilize them. To ensure customer satisfaction, it is important that these apps work and that failures are found and corrected as quickly as possible. Therefore, in-car apps also implicate requirements for future vehicle diagnostics. This is because current vehicle diagnostic methods are not designed for handling dynamic software failures of apps. Consequently, new diagnostic methods are needed to support the diagnosis of in-car apps. Log data are a central building block in software systems for system health management or troubleshooting. However, there are different types of log data and log environment setups depending on the underlying system or software platform. Depending on that, the creation of
Bickelhaupt, SandraHahn, MichaelNuding, NikolaiMorozov, AndreyWeyrich, Michael
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