Model-Based Condition Monitoring of the Sensors and Actuators of an Electric and Automated Vehicle

Constant monitoring of driving conditions and observation of the surrounding area are essential for achieving reliable, high-quality autonomous driving. This requires more reliable sensors and actuators, as there is always the potential that sensors and actuators will fail under real-world conditions. The sensitive condition-monitoring methods of sensors and actuators should be used to improve the reliability of the sensors and actuators. They should be able to detect and isolate the abnormal situations of faulty sensors and actuators. In this paper, a developed model-based method for condition monitoring of the sensors and actuators in an electric vehicle is presented that can determine whether a sensor has a fault and further reconfigure the sensor signal, as well as detect the abnormal behavior of the actuators with the reconfigured sensor signals. Through the simulation data obtained by the vehicle model in complex road conditions, it is proved that the method is effective for the state detection of sensors and actuators

Intelligent fault detection and classification based on hybrid deep learning methods for Hardware-in-the-Loop test of automotive software systems

Hardware-in-the-Loop (HIL) has been recommended by ISO 26262 as an essential test bench for determining the safety and reliability characteristics of automotive software systems (ASSs). However, due to the complexity and the huge amount of data recorded by the HIL platform during the testing process, the conventional data analysis methods used for detecting and classifying faults based on the human expert are not realizable. Therefore, the development of effective means based on the historical data set is required to analyze the records of the testing process in an efficient manner. Even though data-driven fault diagnosis is superior to other approaches, selecting the appropriate technique from the wide range of Deep Learning (DL) techniques is challenging. Moreover, the training data containing the automotive faults are rare and considered highly confidential by the automotive industry. Using hybrid DL techniques, this study proposes a novel intelligent fault detection and classification (FDC) model to be utilized during the V-cycle development process, i.e., the system integration testing phase. To this end, an HIL-based real-time fault injection framework is used to generate faulty data without altering the original system model. In addition, a combination of the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is employed to build the model structure. In this study, eight types of sensor faults are considered to cover the most common potential faults in the signals of ASSs. As a case study, a gasoline engine system model is used to demonstrate the capabilities and advantages of the proposed method and to verify the performance of the model. The results prove that the proposed method shows better detection and classification performance compared to other standalone DL methods. Specifically, the overall detection accuracies of the proposed structure in terms of precision, recall and F1-score are 98.86%, 98.90% and 98.88%, respectively. For classification, the experimental results also demonstrate the superiority under unseen test data with an average accuracy of 98.8%

A framework and methods for on-board network level fault diagnostics in automobiles

A significant number of electronic control units (ECUs) are nowadays networked in automotive vehicles to help achieve advanced vehicle control and eliminate bulky electrical wiring. This, however, inevitably leads to increased complexity in vehicle fault diagnostics. Traditional off-board fault diagnostics and repair at service centres, by using only diagnostic trouble codes logged by conventional onboard diagnostics, can become unwieldy especially when dealing with intermittent faults in complex networked electronic systems. This can result in inaccurate and time consuming diagnostics due to lack of real-time fault information of the interaction among ECUs in the network-wide perspective. This thesis proposes a new framework for on-board knowledge-based diagnostics focusing on network level faults, and presents an implementation of a real-time in-vehicle network diagnostic system, using case-based reasoning. A newly developed fault detection technique and the results from several practical experiments with the diagnostic system using a network simulation tool, a hardware- in-the- loop simulator, a disturbance simulator, simulated ECUs and real ECUs networked on a test rig are also presented. The results show that the new vehicle diagnostics scheme, based on the proposed new framework, can provide more real-time network level diagnostic data, and more detailed and self-explanatory diagnostic outcomes. This new system can provide increased diagnostic capability when compared with conventional diagnostic methods in terms of detecting message communication faults. In particular, the underlying incipient network problems that are ignored by the conventional on-board diagnostics are picked up for thorough fault diagnostics and prognostics which can be carried out by a whole-vehicle fault management system, contributing to the further development of intelligent and fault-tolerant vehicles

A Dependable Actuator and Sensor Health Monitoring System

Impressive progress in vehicle control technologies has equipped modern vehicles with advanced driver assistance and stability control systems to help the driver handle unfavorable driving conditions. These control systems rely on sensor measurements and/or information estimated based on these measurements to generate control commands for vehicle actuators. Therefore, any failure in sensors and actuators can degrade the performance of these control systems and cause instability in vehicle operation. Sensor failures make the control systems generate undesired control commands and actuator failures prevent desired control commands from being applied. To achieve safe and satisfactory vehicle operation, a real-time health monitoring system is crucial for vehicle sensors and actuators. The reliability of health monitoring is determined by its sensitivity to faults and robustness to disturbances. A reliable health monitoring system is responsible for timely detecting the occurrence of a fault in the target vehicle, accurately identifying the source of the fault, and properly determining the type and magnitude of the fault. This information is crucial for reconstructing sensor data or scaling actuator output, which ultimately could result in a fault-tolerant vehicle system. This thesis proposes a hybrid model/data fault detection and diagnosis system to monitor the health status of any sensor or actuator in a vehicle. The proposed approach works based on residuals generated by comparing sensor measurements or control inputs with their estimations. The estimations are obtained by a hybrid estimator that is developed based on the integration of model-based and data-driven estimators to leverage their strengths. Due to the poor performance of data-driven estimators in unknown conditions, a self-updating dataset is proposed to learn new cases. Estimation based on updated datasets necessitates the use of data-driven estimators that do not require any pre-training. As case studies, the proposed hybrid fault detection and diagnosis system is applied to a vehicle’s lateral acceleration sensor and traction motor. The experimental results show that the hybrid estimator outperforms the model-based and data-driven estimators used individually. These results also confirm that the proposed hybrid fault detection and diagnosis system can detect the faults in the target sensor or actuator, and then reconstruct the healthy value of the faulty sensor or find the failure level of the faulty actuator. For cases where a set of sensors and actuators should be monitored to evaluate their health status, this thesis develops a general data-driven health monitoring system to detect, isolate, and quantify faults in these components. This method checks the coherency among the target vehicle's variables by using the vehicle's data. The coherency among the vehicle variables means that the variables reflect the physical principles governing the target vehicle's motion and the causality between its states. Each variable corresponds to a component in the vehicle. When a fault occurs in one of the vehicle's components, the coherency among the variables is no longer valid. This idea is incorporated to detect faults. After fault detection, to isolate the faulty component, the developed system explores the coherency in the subsets of the vehicle's variables to find which variable is not coherent with others. Once the faulty component is determined, the health monitoring system uses the remaining healthy components to reconstruct the true value of the faulty sensor or find the failure level of the faulty actuator. The experimental results show that the developed health monitoring system appropriately detects, isolates, and quantifies faults in the test vehicle's sensors and actuators. The developed data-driven health monitoring system requires a pre-collected dataset for each vehicle to monitor the health status of its sensors and actuators. To relax this requirement, this thesis proposes a universal health monitoring system for the vehicle IMU sensor (measuring the longitudinal/lateral accelerations and yaw rate) by involving vehicle parameters in the health monitoring process. The proposed universal health monitoring system is able to monitor the health status of the target vehicle's IMU sensor using the other vehicles' data. The performance of the universal health monitoring system is evaluated by simulations. The simulation results are in line with what is expected

An intelligent navigation system for an unmanned surface vehicle

Merged with duplicate record 10026.1/2768 on 27.03.2017 by CS (TIS)A multi-disciplinary research project has been carried out at the University of Plymouth to design and develop an Unmanned Surface Vehicle (USV) named ýpringer. The work presented herein relates to formulation of a robust, reliable, accurate and adaptable navigation system to enable opringei to undertake various environmental monitoring tasks. Synergistically, sensor mathematical modelling, fuzzy logic, Multi-Sensor Data Fusion (MSDF), Multi-Model Adaptive Estimation (MMAE), fault adaptive data acquisition and an user interface system are combined to enhance the robustness and fault tolerance of the onboard navigation system. This thesis not only provides a holistic framework but also a concourse of computational techniques in the design of a fault tolerant navigation system. One of the principle novelties of this research is the use of various fuzzy logic based MSDF algorithms to provide an adaptive heading angle under various fault situations for Springer. This algorithm adapts the process noise covariance matrix ( Q) and measurement noise covariance matrix (R) in order to address one of the disadvantages of Kalman filtering. This algorithm has been implemented in Spi-inger in real time and results demonstrate excellent robustness qualities. In addition to the fuzzy logic based MSDF, a unique MMAE algorithm has been proposed in order to provide an alternative approach to enhance the fault tolerance of the heading angles for Springer. To the author's knowledge, the work presented in this thesis suggests a novel way forward in the development of autonomous navigation system design and, therefore, it is considered that the work constitutes a contribution to knowledge in this area of study. Also, there are a number of ways in which the work presented in this thesis can be extended to many other challenging domains.DEVONPORT MANAGEMENT LTD, J&S MARINE LTD AND SOUTH WEST WATER PL

Real-time sensor data development for smart truck drivetrains

Heavy articulated transport vehicles have a poor reputation associated with dramatic road accidents with frequent fatalities for those in automobiles. The result of this work is a formal data flow structure to enhance real-time decision-making in complex mechanical systems to increase performance capability and responsiveness to human commands. This structure recognizes the multiple layers of highly non-linear mechanical components (actuators, wheel tire & ground surfaces, controllers, power supplies, human/machine interfaces, etc.) that must operate in unison (i.e., reduce conflicts) in real-time (in milli-seconds) to enhance operator (driver) control to maximize human choice. This work contains a discussion on dependable sensor data is vital in complex systems that rely on a suite of sensors for both control as well as condition monitoring purposes as well as discussion on real-time energy distribution analysis in high momentum mechanical systems. The focus will be on tractor trucks of class 7 & 8 that are outfitted with an array of low-cost redundant sensors leveraging advances in intelligent robotic systems. This work details many topics including: Most relevant sensor types and their technologies, Designing, implementing, and maintaining a multi-sensor system using feasible industry standards, Sensor signal integrity and data flow processing for decision making, Asynchronous data flow methods for operating decision making schemes in real-time, Multiple applications to enhance tractor trucks systems with multi-sensor systems for real-time decision making.Mechanical Engineerin

Sensor Fault Detection and Fault-Tolerant Estimation of Vehicle States

Manufacturing smarter and more reliable vehicles is a progressing trend in the automotive industry. Many of today’s vehicles are equipped with driver assistant, automated driving and advanced stability control systems. These systems rely on measured or estimated information to accomplish their tasks. Evidently, reliability of the sensory measurements and the estimate information is essential for desirable operation of advanced vehicle subsystems. This thesis proposes a novel methodology to detect vehicle sensor faults, reconstruct the faulty sensory signals and deliver fault-tolerant estimation of vehicle states. The proposed method can detect failures of the longitudinal, lateral and vertical acceleration sensors, roll rate, yaw rate and pitch rate sensors, steering angle sensor, suspension height sensors, and motor torque sensors. The proposed structure can deliver fault-tolerant estimations of the vehicle states including the longitudinal, lateral and vertical tire forces, longitudinal and lateral velocities, roll angle, and pitch angle. Road grade and bank angles are also estimated in this method even in presence of sensor faults. The unified structure in this thesis is realized by fusion of analytical redundancy relations, fault detection observers and adaptive state estimation algorithms. The proposed method can isolate the faults for vehicle stability and control systems and deliver accurate estimation of vehicle states required by such systems despite sensor failures. The methods developed in this thesis are validated through experiments and can operate reliably in various driving scenarios