Browse Topic: Diagnostics
This SAE Surface Vehicle Technical Information Report, SAE J2836/4, establishes diagnostic use cases between plug-in electric vehicles (PEV) and the electric vehicle supply equipment (EVSE). As PEVs are deployed and include both plug-in hybrid electric (PHEV) and battery electric (BEV) vehicle variations, failures of the charging session between the EVSE and PEV may include diagnostics particular to the vehicle variations. This document describes the general information required for diagnostics and SAE J2847/4 will include the detail messages to provide accurate information to the customer and/or service personnel to identify the source of the issue and assist in resolution. Existing vehicle diagnostics can also be added and included during this charging session regarding issues that have occurred or are imminent to the EVSE or PEV, to assist in resolution of these items.
Helicopter maintenance troubleshooting faces significant challenges due to fragmented documentation, outdated procedural manuals, and reliance on human expertise, all of which threaten flight safety and operational efficiency. While Knowledge Graphs (KGs) effectively model hierarchical system relationships and causal dependencies, they struggle with dynamic unstructured data. Conversely, Retrieval-Augmented Generation (RAG) systems access technical manuals but risk hallucinating unsafe procedures without structural grounding. This paper introduces KG-RAG, a novel hybrid troubleshooting framework specifically engineered for helicopter systems, addressing a critical gap as existing work focuses predominantly on fixed-wing aircraft. The framework merges knowledge graphs modeling fault causality and maintenance history with multi-dimensional retrieval combining graph-based reasoning, vector embeddings, and keyword-based search. This integration enables contextual interpretation of ambiguous fault descriptions, generation of precise diagnostics aligned with operational constraints, and dynamic adaptation to new fault patterns without retraining. By transforming fragmented maintenance knowledge into a verifiable, context-aware troubleshooting guide, the framework directly addresses aviation's persistent obstacles: data incompleteness, knowledge erosion, and slow safety-critical decisions. This work positions KG-RAG not merely as a tool but as a foundational shift toward cognitively augmented maintenance, elevating human expertise through AI that reasons like an engineer and contextualizes like a veteran technician for enhanced safety-critical decision-making in complex helicopter operations.
With the rise of software-defined vehicles and the emergence of cyber threats to vehicular systems, developing teams are compelled to conduct extensive testing on both virtual and physical prototypes at an accelerated pace. This new development landscape necessitates diagnostic tools that are both precise and adaptable. However, proprietary systems dominate this field, often hindering accessibility for students and researchers due to high costs and restrictive licensing. This paper presents the design and implementation of an open-source, low-cost remote testing system tailored for automotive development and diagnostics. The proposed system utilizes Arduino and Raspberry Pi processing units, along with relay-based switching modules, to provide secure remote control of vehicle components through a web-based dashboard equipped with authentication, scheduling, and real-time synchronization capabilities. The tested prototype showcased robust scalability, secure session handling, and seamless integration with the open-source Woodpecker EV platform at the University of Detroit Mercy. The affordability and open-source nature of the framework offer a practical alternative to proprietary tools, while also enabling future adaptation to diverse automotive contexts.
This study introduces a novel Large Language Model (LLM)-driven approach for comprehensive diagnosis and prognostics of vehicle faults, leveraging Diagnostic Trouble Codes (DTCs) in line with industry-standard automation protocols. The proposed model asks for significant advancement in automotive diagnostics by reasoning through the root causes behind the fault codes given by DTC document to enhance fault interpretability and maintenance efficiency, primarily for the technician and in few cases, the vehicle owner. Here LLM is trained on vehicle specific service manuals, sensor datasets, historical fault logs, and Original Equipment Manufacturer (OEM)-specific DTC definitions, which leads to context-aware understanding of the vehicle situation and correlation of incoming faults. Approach validation has been done using field level real-world vehicle dataset for different running scenarios, demonstrating model’s ability to detect complex fault chains and successfully predicting the associated root cause. By utilizing time series based future projection of the vehicle pattern, this approach could also predict the probable future faults as well as the requisite steps needed to prevent them. Overall, key contributions of this work include: (1) a modular diagnostic framework that seamlessly integrates different electronic control unit (ECU) architectures for sequential root cause analysis of vehicle faults, (2) cross-platform compatibility allowing utility across varied vehicle models and platforms, and (3) a user-friendly interface that eliminates the need for technical expertise by generating output data into simple, actionable insights. This work was benchmarked against traditional rule-based diagnostic tools and showed 50-70% reduction in the troubleshooting time for Root cause analysis (RCA). In the prognosis front, model could predict upcoming possible faults in the Battery behavior with significant accuracy. The framework also supports continuous learning by integrating new fault patterns, ensuring adaptability over time. This paper establishes the potential of integrating advanced language models into the automotive diagnostics pipeline and provides a scalable, intelligent, and intuitive solution for next-generation vehicle fault management.
As vehicles are becoming more complex, maintaining the effectiveness of safety critical systems like adaptive cruise control, lane keep assist, electronic breaking and airbag deployment extends far beyond the initial design and manufacturing. In the automotive industry these safety systems must perform reliably over the years under varying environmental conditions. This paper examines the critical role of periodic maintenance in sustaining the long-term safety and functional integrity of these systems throughout the lifecycle. As per the latest data from the Ministry of Road Transport and Highways (MoRTH), in 2022, India reported a total of 4.61 lakh road accidents, resulting in 1.68 lakh fatalities and 4.43 lakh injuries. The number of fatalities could have been reduced by the intervention of periodic services and monitoring the health of safety critical systems. While periodic maintenance has contributed to long term safety of the vehicles, there are a lot of vehicles on the road which are not serviced regularly. This paper aims to fill this critical gap by proposing a system where the government agencies actively collect and monitor vehicle maintenance data and diagnostics data ensuring that all vehicles on the road undergo mandatory periodic servicing to uphold the integrity of safety-critical systems. This paper concludes by proposing a centralized framework for data sharing and proactive monitoring to ensure the sustained performance of safety-critical systems—ultimately reducing preventable road fatalities and improving overall vehicular safety across India.
Cars that are more connected, equipped with more sensors than ever before, should make proactive maintenance somewhat easy and reliable. Drivers could have lower repair costs and fewer breakdowns overall if the automotive industry shifts away from a periodic maintenance mindset towards data-driven proactive services. But the automakers themselves would also win with a massive drop in recalls. A connected car requires accurate telemetry sensors to track details such as temperature (in the engine, battery, and cabin), pressure (in tires, fuel, and oil), electrical current/voltage, and vibration patterns to detect problems before they become failures. The vehicle also needs to be able to combine telemetry, diagnostics, metadata, and service records in one platform and then make sense of it all. “Poor data integration kills even the best analytics,” according to Upstream co-founder and CTO Yonatan Appel.
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 many times greater than electrical losses in high-speed modes of traction machines. The paper pays special attention to monitoring the three-phase circuit of a traction machine by a measuring observer consisting of six current and voltage sensors. Algorithms for data processing using the generalized energy flow technology are presented. They allow obtaining comprehensive information on the energy state of a three-phase circuit with a time delay of no more than one millisecond. The three-phase circuit monitoring data are taken as a basis for monitoring electrical and polarization losses of the battery, electrical and switching losses of the voltage inverter, electrical, magnetic, harmonic and mechanical losses of the motor and gearbox.
The SAE J1939 communications network is developed for use in heavy-duty environments and is suitable for horizontally integrated vehicle industries. The SAE J1939 communications network is applicable for light-duty, medium-duty, and heavy-duty vehicles used on-road or off-road, and for appropriate stationary applications which use vehicle-derived components (e.g., generator sets). Vehicles of interest include, but are not limited to, on-highway and off-highway trucks and their trailers, construction equipment, and agricultural equipment and implements. SAE J1939-71 is the SAE J1939 reference document describing SAE J1939 parameter (SP) and message (PG) definitions, SLOT (standard data encoding) definitions, conventions and notations used to specify the parameter (SP) placement in PG data, conventions for text data parameters, and conventions for PG transmission rates. This document previously contained the majority of the SAE J1939 OSI application layer data parameters and messages for information exchange between the ECU applications connected to the SAE J1939 communications network. It also contained reference figures and reference information. The data parameters (SPs), messages (PGs), reference figures, and information previously published within this document are now published in SAE J1939DA. There are several SAE J1939-7X documents that collectively define all of the SAE J1939 application layer data parameters and messages. Diagnostic services and some industry-specific data parameters and messages are documented within other SAE J1939-7X application layer documents. An ECU may simultaneously use and support data parameters and messages from multiple SAE J1939-7X application layer documents.
A new handheld, sound-based diagnostic system can deliver precise results in an hour with a mere finger prick of blood. The researchers used tiny particles they call functional negative acoustic contrast particles (fNACPs) and a custom-built, handheld instrument or acoustic pipette that delivers sound waves to the blood samples inside.
The term Software-Defined Vehicle (SDV) describes the vision of software-driven automotive development, where new features, such as improved autonomous driving, are added through software updates. Groups like SOAFEE advocate cloud-native approaches – i.e., service-oriented architectures and distributed workloads – in vehicles. However, monitoring and diagnosing such vehicle architectures remain largely unaddressed. ASAM’s SOVD API (ISO 17978) fills this gap by providing a foundation for diagnosing vehicles with service-oriented architectures and connected vehicles based on high-performance computing units (HPCs). For service-oriented architectures, aspects like the execution environment, service orchestration, functionalities, dependencies, and execution times must be diagnosable. Since SDVs depend on cloud services, diagnostic functionality must extend beyond the vehicle to include the cloud for identifying the root cause of a malfunction. Due to SDVs’ dynamic nature, vehicle systems must be monitored as service degradation is more likely than a complete failure. Established monitoring and error analysis approaches for cloud environments cannot easily be transferred to vehicles. Monitored values must be aggregated and correlated to error events before cloud transmission, or suspects must be created in the vehicle for thorough analysis, reducing the data exchanged with the backend. The SOVD API provides a good foundation to diagnose service-oriented architectures and HPCs. While SOVD offers a wide range of diagnostic and monitoring features, it currently lacks solutions for diagnosing certain aspects and especially monitoring of a service-oriented architecture. This paper addresses these gaps, showcasing approaches and techniques to enhance monitoring and 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.
SAE J1939-73 defines the SAE J1939 messages to accomplish diagnostic services and identifies the diagnostic connector to be used for the vehicle service tool interface. Diagnostic messages (DMs) provide the utility needed when the vehicle is being repaired. Diagnostic messages are also used during vehicle operation by the networked electronic control modules to allow them to report diagnostic information and self-compensate as appropriate, based on information received. Diagnostic messages include services such as periodically broadcasting active diagnostic trouble codes, identifying operator diagnostic lamp status, reading or clearing diagnostic trouble codes, reading or writing control module memory, providing a security function, stopping/starting message broadcasts, reporting diagnostic readiness, monitoring engine parametric data, etc. California-, EPA-, or EU-regulated OBD requirements are satisfied with a subset of the specified connector and the defined messages.
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 safety and operational efficiency of lithium-ion batteries in low-altitude flight systems. Furthermore, this study proposes an innovative approach for onboard Li plating detection by considering signals related to cell thickness data. This method reduces computational demands, enhancing detection efficiency—a vital advancement for real-time monitoring in low-altitude flight power systems. Moreover, our research establishes a strong correlation between the occurrence of Li plating and the loss of active material in the negative electrode, shedding light on the underlying mechanisms and emphasizing the need to mitigate this phenomenon. Overall, this study significantly contributes to the existing research focused on improving the safety and efficiency of lithium-ion batteries in low-altitude flight applications. By emphasizing robust detection techniques for Li plating, we pave the way for safer and more efficient power sources in this rapidly evolving field.
SAE J1939-13 specifies the diagnostic connectors used for off-board connection to a vehicle’s SAE J1939 communication links. The defined diagnostic connectors support connection to the twisted shielded pair media (refer to SAE J1939-11), the unshielded twisted pair (refer to SAE J1939-15), the twisted pair (refer to SAE J1939-14), and the twisted unshielded quad media (refer to ISO 11783-2).
Items per page:
50
1 – 50 of 619