Association Model-Based Intermittent Connection Fault Diagnosis for Controller Area Networks

The controller-area network (CAN) bus has a wide range of applications. It plays a key role in information transfer. In fact, a failure of the CAN bus causes information transfer to fail. Therefore, fault diagnosis of the CAN bus is necessary. In addition, CAN bus failures generally result from sample imbalance problems. Therefore, this study proposes a CAN bus fault diagnosis (CANFD) method that integrates a Laplace network (LaplaceCNN) and uses a class-imbalance dataset. The method first utilises the adaptive synthetic (ADASYN) oversampling algorithm to process the class-imbalance CANFD dataset. LaplaceCNN is trained on the processed balanced dataset to diagnose CAN bus faults. To collect the frequency-domain characteristics and feed them into the deep network, LaplaceCNN uses Laplace wavelet convolution as the initial layer of the network. Multiscale wide convolutional and dense residual blocks are proposed to enhance the feature extraction capability of LaplaceCNN and stabilise the model performance. Introducing focal loss further improves the performance of LaplaceCNN on a class-imbalanced dataset. Experiments demonstrate that the proposed method performs better in terms of fault diagnosis.

Abstract- Modern vehicles use the Electronics Control Unit (ECU) to control and monitor all the activities within the vehicle. The number of ECUs are increasing as the complexity of vehicles increases. All the ECUs present in the vehicles are communicated with each other via CAN protocol. Any malfunction in the ECU or abnormal behaviour of ECU is detected or understood by diagnostic services. CAN Protocol does not have advanced features like Diagnostic. The CAN protocol covers only the Physical and Data link layer of the OSI model. There is a need for a standardised diagnostic protocol which can use CAN as underlying technology. Standardised diagnostic protocols used in the automotive domain are On Board Diagnostics (OBD) and Unified Diagnostic services (UDS). UDS protocol is defined under the ISO 14229 standard and provides a standardized framework for in-vehicle communication and fault diagnosis. This project focuses on the design and implementation of the UDS protocol on an embedded system using STM32F407 microcontrollers. The project involves developing a diagnostic communication system between Electronic Control Units (ECUs) Light Control Module (LCM) and Tester tool connected over a CAN bus. Keywords- Unified Diagnostic Services (UDS), ISO 14229, Electronic Control Unit (ECU), Body Control Module (BCM), Light Control Module (LCM), Controller Area Network (CAN), Diagnostic Trouble Code (DTC), Routine Control Identifier (RID), Data Identifier (DID), ISO-TP (Transport Protocol)

Given the lack of sufficient historical data for aircraft landing gear retractor systems, a model-based fault diagnosis approach is needed to overcome this data deficiency. Meanwhile, inherent uncertainties are inevitable in engineering practice, and it is a great challenge to construct a model that accurately reflects the complexity of the actual system under uncertain conditions. Due to the urgent need for reliable model-based diagnostic methods and the need to cope with inherent uncertainties, this paper proposes an improved fault diagnostic method aimed at increasing the diagnostic efficiency of the landing gear retractor system, a critical component in aircraft take-off and landing operations. Due to a lack of historical data, the model-based fault diagnosis method can solve the problem of lack of data. The proposed uncertainty method addresses the challenge of multiple sources of uncertainty by using subsystems to reduce complexity. Fault diagnosis is achieved by comparing residuals with thresholds derived from a diagnostic bond graph (DBG) model. To address the problem of limited fault data, we modeled and simulated the landing gear retractor system using AMESim®. In addition, the linear fractional transform (LFT) approach has been used to resolve parametric uncertainties, but is unable to resolve system structural uncertainties. Therefore, we also analyzed the comparative fault diagnosis results derived from the linear fractional transformation-DBG (LFT-DBG) and the subsystem-DBG approaches. The experimental results support the effectiveness of the subsystem approach in improving fault diagnosis accuracy and reliability, highlighting its potential as a viable diagnostic strategy in aerospace engineering applications.

Faulty switches, broken interswitch connections and certain bridging faults in a Benes switching network can be identified using a sequence of faulty paths across the network. The method described may use on-line faulty-path data and is suitable for use in the diagnosis of intermittent faults. It is shown that a single faulty switch can be identified in less than four random faulty paths on average, after presentation of the initial faulty path. The method applies to other types of switching network and provides a similar performance.

This paper presents the distributed control system for elevators (DECS). Based on the characteristics of the today installed elevator systems, the electric traction elevator has been chosen for this paper. Conventional elevator control systems (ECS) have several disadvantages compared to modern control systems (large number of cables, safety issues, etc.). An alternative to conventional ECS is the DECS. A DECS based on the CAN (Controller Area Network) protocol is presented in this paper. The presented ECS consists of three modules: CU Elevator (CU - Control Unit), CU Cabin, CU Floor. The mentioned modules, control principles and implementation methods based on the PIC 18F458 microcontroller are presented in detail. A description of the CAN protocol design with message scheduling on the bus is given. Messages with an 11 bit identifier without a data field have been used for the distributed control system synthesis. The time analysis of bus availability/traffic demonstrates the adequacy of the distributed control system Based on the defined messages and 5 presented modes of operation, an algorithm of operation has been formed for every control system module. Finally experimental results are presented that have been obtained from a developed CAN based elevator control system. The conducted tests have confirmed the functionality of the elevator control system.

This paper proposes a method based on chaotic spread spectrum sequence for synchronous online diagnosis of intermittent faults for complex and cable. networks, such as the aircraft power system. The novelties of the proposed method include: 1) since chaotic spread spectrum signals are sensitive to initial values, the number of chaotic sequences are not limited and the proposed method is not limited by the number of cables under monitoring; 2) with the self-correlation characteristic and cross-correlation characteristic of chaotic sequence, the proposed method can be extended to online diagnosis of multiple cable intermittent faults; and 3) the signal source impedance amplification method is proposed for intermittent arc faults of multiple cables, which improves the reflection coefficient and increases the accuracy of online detection and location of intermittent arc faults. A series of simulations and experiments on multiple cables with different intermittent fault modes are conducted to verify the effectiveness of the proposed approaches.

In this paper, a new model-based fault detection and diagnosis method for broken rotor bars in squirrel-cage induction motor is proposed. The proposed method relies on innovation sequence generated by the conventional extended Kalman filter. The innovations would follow a Gaussian distribution under normal operation; however a fault, i.e., broken rotor bar, would change this underlying distribution. It has been shown that this change in the distribution is indicative of a fault. The proposed method uses readily available current measurements and no additional sensors are required. Further, the proposed method is robust to unbalanced supply voltage and load changes. Computer simulations are carried out for 4-hp squirrel-cage induction motor using MATLAB software. The results demonstrate the advantage of the proposed technique as it provides accurate estimates for broken rotor bar fault detection.

The in‐vehicular networked control system is among the most critical embedded processes. The controller area network (CAN) has prevailed intra‐vehicle communication for decades. Meanwhile, requirements of future transportation systems are expected to emphasise the in‐vehicle communication complexity, which endangers the reliability/safety of the intelligent navigation. At first, this study reviews the recent solutions proposed to overcome the CAN expanding complexity. Challenges that tomorrow's intelligent vehicles may raise for CAN reliability are investigated. The comprehensive coverage of current research efforts to remove the impact of these challenges is presented. Further, the in‐vehicle system reliability of future automated vehicles is also related to the fault diagnosis performances. Hence, different classes of system‐level diagnosis strategies are compared relatively to the requirements of automotive embedded networks. Furthermore, to thoroughly cover CAN reliability engineering issues, focus is given to the automotive validation techniques. The hardware in the loop, real‐time analysis and computer‐aided‐design tools intervene in various phases along the in‐vehicular network life cycle. Parameters that stand behind the efficiency and accuracy of these techniques in validating the new generation of vehicles are analysed. The authors finally draw some deductive predictions about the future directions related to the reliability of the intelligent transportation system in‐vehicular communication.

Electric vehicles (EVs) are becoming increasingly important in the shift toward sustainable mobility. While their adoption is accelerating, ensuring the health and reliability of EV powertrains remains a critical challenge. Failures in subsystems such as batteries, motors, and controllers may cause unexpected breakdowns, reduced efficiency, and safety issues. Predictive Health Monitoring (PHM) systems aim to prevent such failures by identifying anomalies before they escalate. Traditional PHM solutions often depend on cloud platforms, but these face drawbacks such as latency, high bandwidth requirements, and privacy risks. To address these challenges, edge-based PHM employs embedded devices to process Controller Area Network (CAN) bus data locally, enabling real-time diagnostics, enhanced privacy, and cost efficiency. This paper presents a structured survey of PHM techniques for EV powertrains using Edge Artificial Intelligence (Edge AI) and CAN bus data. The contributions include: (i) classification of PHM approaches into model-based, data-driven, security- focused, and edge-deployed methods, (ii) comparative analysis of recent works, (iii) a summary table of surveyed papers, (iv) discussion of hardware implementations reported in literature, and (v) identification of future research directions.

Model-based fault diagnosis approach has been widely studied and applied because of high accuracy, strong adaptability and other advantages. However, as the complexity of the structure and function of the satellite systems increase, online reasoning of model based fault diagnosis method is greatly challenged by the low time efficiency. Offline compilation technology compiles the system model into a special form in the off-line phase, making it unnecessary to complete complex reasoning during the online diagnosis stage, thus saves much time in online diagnosis stage. Ordered Binary Decision Diagrams (OBDD) is an excellent compilation language. This paper applies it in the fault diagnosis of satellite system, studying the model abstraction, off-line model compilation and on-line fault diagnosis of satellite system. Theoretical analysis and experimental analysis show that the use of ordered binary decision diagrams can significantly improve the efficiency of online diagnostics and has a wide range of application value.

Timely fault diagnosis and localization of transmission lines is crucial for ensuring the reliable operation of increasingly complex power systems. This study proposes an optimized R-Net algorithm based on a feature pyramid network (FPN) and densely connected convolutional network (D-Net) for transmission line fault diagnosis and localization. The R-Net network is enhanced by reshaping the anchor points using an improved K-means algorithm and incorporating an FPN for multi-scale feature extraction. The backbone network is further optimized using D-Net to strengthen inter-layer connections and improve feature reuse. Experimental results demonstrate that the optimized R-Net achieves an overall average accuracy of 0.64, outperforming the original network by 1.30%. The accuracy improvement is particularly significant for ground wire defects (2.40%). The D-Net-based R-Net, despite having fewer parameters, maintains high accuracy (0.6502). Compared to other object detection algorithms, such as YOLO-v3 and Faster R-CNN, the optimized R-Net exhibits superior performance in terms of mean average precision (15.58% and 2.45% higher, respectively) and parameter efficiency (17M vs. 38M and 81M). Considering both performance and speed, the optimized R-Net achieves a processing rate of 10.5 frames per second. This study provides an efficient and accurate tool for transmission line fault diagnosis and localization, with significant practical implications for power system operation and maintenance.

In recent years, complexity of fault diagnosis in the advanced distribution networks, mainly due to the increased use of distributed generations and fault with impedance result in proposing of adaptive fault location technique using neural network. Radial basis function neural networks are used for fault diagnosis and fault location. The proposed approach reduces the complexity of the fault location in case of impedance fault. The predicted results prove the effectiveness of the proposed online automatic procedure for fast and accurate fault diagnosis of the system for a wide range of system conditions.

To enhance the safety and reliability of fuel cell vehicles, a remote monitoring system based on 5th generation (5G) mobile networks and controller area networks (CANs) was designed, and a random forest (RF) algorithm for the fault diagnosis for eight typical malfunctions of its powertrain system was incorporated. Firstly, the information on the powertrain system was obtained through a 5G-based monitoring terminal, and the Alibaba Cloud IoT platform was utilized for data storage and remote monitoring. Secondly, a fault diagnosis model based on the RF algorithm was constructed for fault classification; its parameters were optimized with a genetic algorithm (GA), and it was applied on the Alibaba Cloud PAI platform. Finally, the performance of the proposed RF fault diagnosis model was evaluated by comparing it with three other classification models: random search conditioning, grid search conditioning, and Bayesian optimization. Results show that the model accuracy, F1 score, and kappa value of the optimized RF fault classification model are higher than the other three. The model achieves an F1 value of 97.77% in identifying multiple typical faults of the powertrain system, as validated by vehicle malfunction data. The method demonstrates the feasibility of remote monitoring and fault diagnosis for the powertrain system of fuel cell vehicles.

With the advantages of non-pollution, low noise, and high energy efficiency, electric cars have become an important trend. The vehicle fault diagnostic protocol ISO15765 is introduced, and a CAN-based (Controller Area Network) fault diagnosis system and diagnostic trouble codes (DTCs) for electric cars are designed. The CAN communication networks are tested by CANoe software, and the results show that the networks meet the requirements.

Fault diagnosis of power distribution networks is a significant task for power system operation and maintenance, which can support rapid and accurate identification, localisation and repair of various faults that may occur in power distribution networks. To improve the accuracy and efficiency of fault diagnosis, this paper proposes a deep adaptive learning model for dealing with the data-driven identification problems in power systems. In the deep adaptive learning model, an adaptive shortcut learning scheme is designed to adaptively adjust the aggregation between the convolutional layers and the attention modules. In this manner, the hidden features can be effectively captured by adaptive learning with the proposed scheme. Therefore, the proposed model can effectively make online decisions on the fault types in high-voltage AC and DC test transmission. The experimental case study and comparison study in this paper demonstrate the reliable performance of the proposed model for online fault diagnosis of power distribution networks, which also show its capability and feasibility for online implementation.