A Survey on On-Board Diagnostic in Vehicles

D, Saibannavar; Math, M. M.; Kulkarni, Umakant P.

doi:10.1007/978-3-030-49795-8_5

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…For developing AI model certain parameters have to be continuously monitored and thus there is a requirement of fitting large number of sensors for supply of continuous stream of data from engine which can be analysed to predict Remaining Useful Life (RUL) of an engine [4]. The entire sys would consist of various sensors like Oil Pressure Sensor, Engine vibration Sensor, Torque sensor, Coolant temp sensor, Fuel consumption sensor, Exhaust sensor, Acoustic Sensor and data logger which would be employed on the AFV as part of remote diagnostics mechanism [5][6][7][8][9][10]. The sensors would be of compact size, so that it can be mounted easily on various assembly/sub system of the AFV, without hampering the design and overall functioning of the tank.…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence for Predictive Maintenance of Armoured Fighting Vehicles Engine

Narayanan,

Padhy

2023

IJAINN

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Armoured Fighting Vehicles (AFVs) also called as Tanks play a critical role in modern warfare, providing mobility, protection and firepower on the battlefield. However, maintaining these complex machines and ensuring their operational readiness is a significant challenge for military organizations. Traditional maintenance practices are often reactive, resulting in unexpected failures, increased downtime, and operational inefficiencies. This paper focuses on the application of Artificial Intelligence (AI) for predictive maintenance of Armoured Fighting Vehicles. By harnessing the power of AI algorithms and advanced data analytics, predictive maintenance aims to anticipate and address potential equipment failures before they occur. This proactive approach enables military organizations to optimize resource allocation, improve operational planning and extend the lifespan of AFVs. The integration of AI in predictive maintenance involves collecting and analysing data from various sensors installed on the AFV engine. These sensors monitor key parameters, such as engine performance, temperature, vibration and fluid levels to detect anomalies and deviations from normal operating conditions. AI algorithms process this data, utilizing machine learning techniques to identify patterns, correlations, and potential failure indicators. The benefits of AI-based predictive maintenance for AFVs are multifaceted. Firstly, it enhances equipment readiness by reducing unexpected failures and maximizing operational availability. Secondly, it enables optimized resource allocation, ensuring that maintenance activities are scheduled efficiently, minimizing downtime, and improving overall operational efficiency. Thirdly, the predictive capabilities of AI help military planners in better decision-making allowing for improved mission planning and execution. However, the successful implementation of AI for predictive maintenance of AFV engine requires overcoming several challenges. These include data collection and integration from diverse sensors, ensuring data accuracy and quality, establishing robust communication infrastructure, and addressing cyber security concerns to protect sensitive vehicle data. This paper underscores the growing importance of AI in revolutionizing maintenance practices for Armoured Fighting Vehicles. By shifting from reactive maintenance to predictive strategies, military organizations can enhance their operational capabilities, reduce costs, and ensure the optimal performance and longevity of their AFV fleet.

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Section: Methodsmentioning

confidence: 99%

Artificial Intelligence for Predictive Maintenance of Armoured Fighting Vehicles Engine

Narayanan,

Padhy

2023

IJAINN

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“…A vehicle might have more than 100 ECUs for different tasks, from lights to motor control. Some ECUs in a vehicle include [33]- [35]:…”

Section: Faults System Definitionmentioning

confidence: 99%

Crash-Prone Fault Combination Identification for Over-Actuated Vehicles During Evasive Maneuvers

Da Silva Junior,

Birkner,

Jazar

et al. 2024

IEEE Access

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Throughout a vehicle's lifecycle, systems may fail during operation, requiring effective fault management by the vehicle controller. Various system faults affect vehicle handling differently. Additionally, vehicle velocity and road friction directly impact handling and stability. Thus, it is essential to investigate relevant factors, such as actuator faults, vehicle velocity, road friction, and their combinations, before developing a fault-tolerant controller to mitigate potential critical situations. Our work thus focuses on identifying faults and fault combinations that might lead to crashes for over-actuated vehicles during evasive maneuvers and those impacting comfort parameters. We employ a state-of-the-art vehicle controller optimized for evasive lane changes for over-actuated vehicles. The driving scenario encompasses critical conditions defined in ISO 26262 with ASIL-D, including velocities up to 130 km/h and requiring steering away from obstacles. Failure Mode and Effects Analysis, Design of Experiments, and statistical tools are used to determine fault combinations most likely to lead to crashes during evasive maneuvers. Our results indicate that the vehicle controller successfully handled the maneuver in over 53% of investigated cases, reaching up to 75.1% on dry surfaces. Road friction emerges as the most critical parameter for collision avoidance and comfort. Brake faults exhibit a higher influence on vehicle handling than other actuator faults, while single motor faults do not significantly impact vehicle parameters. Regarding two-factor interactions, brake actuators dominate, followed by steering and motor. These findings provide valuable insights for developing faulttolerant controllers for over-actuated vehicles, guiding decisions on addressing specific faults to enhance safety and comfort parameters.INDEX TERMS Autonomous vehicle, crash avoidance, evasive maneuvers, over-actuated vehicle, sliding mode control, vehicle coupled controllers.

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“…For any physically measurable signal, the signal must be preprocessed to obtain a better characteristic acoustic signal. According to the research in [7] by M. M. Hasan et al, the background ambient broadband noise is the majority among the measured acoustic signals. Background noise includes human noise or other noises in the environment during measurement.…”

Section: Overview Of Vehicle Transmission Acoustic Signal Processingmentioning

confidence: 99%

“…However, today's transportation-related command, control, communication, and intelligence systems are developing rapidly, and we still face numerous unpredictable risks [6]. In terms of past technology developments, fault diagnosis mainly relies on sensors to collect relevant physical analog signals [7]; then, from a large amount of data, according to field operations or the past, a reference data threshold (also known as the threshold value) or a specific data threshold is set to observe, compare, and determine subsequent adjustments. The trend value reference enables the system to perform diagnostic identification and analysis on the basis of calculation identification.…”

Section: Introductionmentioning

confidence: 99%

How to Implement Automotive Fault Diagnosis Using Artificial Intelligence Scheme

Gong

Chen

et al. 2022

Micromachines

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The necessity of vehicle fault detection and diagnosis (VFDD) is one of the main goals and demands of the Internet of Vehicles (IoV) in autonomous applications. This paper integrates various machine learning algorithms, which are applied to the failure prediction and warning of various types of vehicles, such as the vehicle transmission system, abnormal engine operation, and tire condition prediction. This paper first discusses the three main AI algorithms, such as supervised learning, unsupervised learning, and reinforcement learning, and compares the advantages and disadvantages of each algorithm in the application of system prediction. In the second part, we summarize which artificial intelligence algorithm architectures are suitable for each system failure condition. According to the fault status of different vehicles, it is necessary to carry out the evaluation of the digital filtering process. At the same time, it is necessary to preconstruct its model analysis and adjust the parameter attributes, types, and number of samples of various vehicle prediction models according to the analysis results, followed by optimization to obtain various vehicle models. Finally, through a cross-comparison and sorting, the artificial intelligence failure prediction models can be obtained, which can correspond to the failure status of a certain car model and a certain system, thereby realizing a most appropriate AI model for a specific application.

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A Survey on On-Board Diagnostic in Vehicles

Cited by 7 publications

References 13 publications

Artificial Intelligence for Predictive Maintenance of Armoured Fighting Vehicles Engine

Artificial Intelligence for Predictive Maintenance of Armoured Fighting Vehicles Engine

Crash-Prone Fault Combination Identification for Over-Actuated Vehicles During Evasive Maneuvers

How to Implement Automotive Fault Diagnosis Using Artificial Intelligence Scheme

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