Designing a printed circuit board capable of connecting an uNicom 2 and uNicom 3 to an ICT Medalist 3050

This document describes the PCB design of a board that will be used in a ICT

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%

Detecting anomalies in multivariate time series from automotive systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In the automotive industry test drives are conducted during the development of new vehicle models or as a part of quality assurance for series vehicles. During the test drives, data is recorded for the use of fault analysis resulting in millions of data points. Since multiple vehicles are tested in parallel, the amount of data that is to be analysed is tremendous. Hence, manually analysing each recording is not feasible. Furthermore the complexity of vehicles is ever-increasing leading to an increase of the data volume and complexity of the recordings. Only by effective means of analysing the recordings, one can make sure that the effort put in the conducting of test drives pays off. Consequently, effective means of test drive analysis can become a competitive advantage. This Thesis researches ways to detect unknown or unmodelled faults in recordings from test drives with the following two aims: (1) in a data base of recordings, the expert shall be pointed to potential errors by reporting anomalies, and (2) the time required for the manual analysis of one recording shall be shortened. The idea to achieve the first aim is to learn the normal behaviour from a training set of recordings and then to autonomously detect anomalies. The one-class classifier “support vector data description” (SVDD) is identified to be most suitable, though it suffers from the need to specify parameters beforehand. One main contribution of this Thesis is a new autonomous parameter tuning approach, making SVDD applicable to the problem at hand. Another vital contribution is a novel approach enhancing SVDD to work with multivariate time series. The outcome is the classifier “SVDDsubseq” that is directly applicable to test drive data, without the need for expert knowledge to configure or tune the classifier. The second aim is achieved by adapting visual data mining techniques to make the manual analysis of test drives more efficient. The methods of “parallel coordinates” and “scatter plot matrices” are enhanced by sophisticated filter and query operations, combined with a query tool that allows to graphically formulate search patterns. As a combination of the autonomous classifier “SVDDsubseq” and user-driven visual data mining techniques, a novel, data-driven, semi-autonomous approach to detect unmodelled faults in recordings from test drives is proposed and successfully validated on recordings from test drives. The methodologies in this Thesis can be used as a guideline when setting up an anomaly detection system for own vehicle data

A novel hydromechatronics system towards: micro-independent metering.

This thesis presents the outcome of an investigation into the development of an existing hydraulic control system known as Independent Metering towards Micro-Independent Metering (MIM). The Independent Metering system uses a different configuration of the connection between the main elements of the hydraulic systems when compared to a traditional hydraulic circuit arrangement. These elements are pump, tank, and actuator. In a conventional control valve, meter-in connects pump flow to one side of the actuator, while meter-out connects the other side of the actuator back to the tank, these metering features are physically linked. With Independent Metering, these metering features are separated such that they can be independently controlled with a potential resultant reduction of energy losses, improved controllability, but with the increased complexity of the control system. In a conventional Independent Metering system, a spool, poppet or cartridge valve is generally utilised. However, in this research, a new stepped rotary flow control valve is used for the development of a novel configuration that also meets the rules of Independent Metering. The use of this valve alongside the electronic driving technique micro-stepping, commonly used in electronically controlled electrical drives, improved the system controllability by introducing a smoothing operation in the hydraulic system. This resulted in the new Micro-Independent Metering algorithm which is one of the main contributions to knowledge in this research. To develop the MIM system, the Model-Based Design technique including the system analysis, modelling and simulation, software-in-the-Loop (SIL) simulation, and the hardware-in-the- Loop (HIL) test, are used. Mathematical model and performance analysis of the valve were conducted in this research. The multi-step response analysis was used to evaluate the dynamical performance of the valve. This indicated that the micro-step driving technique is more suitable for driving the valve as it reduces the effect of the transient response due to friction, while increasing the resolution. Root Locus Analysis (RLA) was used to study valve stability and the performance limitations. The RLA demonstrated the effect of key parameters on the valve operation. For example, the study show that the valve starts losing stability when the applied pressure drop exceeds 35 MPa. A new algorithm was developed to formulate and apply the rules of the MIM system. The algorithm includes an operational modes selection procedure, valve conductance calculation procedure, anti-cavitation procedure, and close value detection (CVD) procedure. The proposed CVD determines the stepper motor position based on a predetermined vector selection

Modelling, real-time simulation and control of automotive windscreen wiper systems for electronic control unit development

In recent years there has been a growth in the automotive industry, coupled with a growth in the amount of electronic components and systems in a modern vehicle. The higher amount of electronics has led to an increased amount of Electronic Control Units (ECU) in a vehicle which require advanced simulation based testing procedures throughout their development process. One such method is Hardware in the Loop (HIL) simulation in which a real ECU is connected to simulation models of its environment via a real-time simulator. This project is concerned with developing a plant model of a windscreen wiper system for use in the development of Jaguar Land Rover’s (JLR) body electronics ECU. The system is divided into four parts which are modelled separately: Wiper motor, linkages, arm and blades, and the windscreen environment. The wiper motor and mechanical elements models are derived and implemented using the physical modelling tools SimScape and SimMechanics. A dynamic friction model describing the interaction between the wiper blades and the windscreen is developed, based on results presented in the literature. A simple aerodynamic model describing the forces on the wiper blades is also established. The parameters of the models are derived using three sequential optimisation methods: Transfer function parameter identification, Genetic Algorithms (GA) and a nonlinear least squares local optimiser. A transfer function relating the motor current to the voltage was derived for step one, and a bespoke GA has been developed for step two. The parameters were successfully identified. Following this, Artificial Neural Networks (ANN) were used to convert the physical models into real-time capable models suitable for HIL simulation. Finally, adaptive control systems are designed in order to maintain the motor at a constant velocity. The models are presented in a Simulink library and graphical user interface modelling tool for ease of use

Model-Based Testing of Automotive Electronics 1. Role of Testing throughout the Development of Automotive Electronics

The increasing importance of electronics in the automotive industry is illustrated by the growing proportion of manufacturing costs taken up by electrical and electronic systems – this has now reached approx. 30%. At the same time, electrical and electronic systems are the main cause of vehicle failures in the field, accounting for approx. 30 % of these. Manufacturers and also suppliers are well aware of the problems caused by the increasing number of electronic control units (ECUs). Thus, quality assurance is becoming increasingly important, as problems in quality are a liability risk, with the danger of image problems and the cost of recall campaigns and rectification. The realization is that “good quality is expensive, bad quality even more so”. Quality must not be left behind by the immense speed at which new technologies and functions are being developed. Quality is becoming a decisive factor in competition, and quality assurance is becoming a key task and a core competence; and testing is a key component of quality assurance. To allow testing throughout the entire development process, powerful and efficient means of developing and describing tests are necessary. These also have to take into account the various requirements of the test tasks and the different development phases. This contribution gives an overview of modern test development in various phases of development and of test management throughout the overall process, using a modelbased test process. 2. The Model-Based Testing Process The model-based test process (see Figure 1) represents various tasks within function and ECU development. It essentially consists of the following test steps: • The Function Model Test comprises the systematic and automated testing of an executable model of the function or controller under development, representing the unit under test (UUT). The test can be run in open loop or against a model of the control plant (model-inthe-loop, MIL). • In the Implementation Model Test, the UUT is th