The research presented is motivated by the demand for process improvement for companies active within software development. High demands on software quality are a reality. At the same time, short development time and low effort consumption are required. This stresses the necessity for process improvement. Empirical research methods and close cooperation with the industry partner addressed the research challenge. The research presented in this thesis shows how the analysis of faults through fault classification can be used to determine suitable and required process improvements. Two alternatives are investigated. First, a lightweight approach, and second a fault classification approach targeting all faults. The suitability of the fault classification is stressed as well as the importance of assigning the correct fault class. The latter is determined by classifier agreement calculations. Additionally, the research proposes that the appropriate occasion for a correct fault classification is alleged to be when the fault is corrected. The research also introduces an approach to tailor the verification and validation process. The tailoring process suggested considers the functionality characteristics and the software entity complexity in terms of couplings. This is used to select the appropriate and efficient process for verification and validation.

Fault detection and location algorithms have allowed for the power industry to alter the power grid from the traditional model to becoming a smart grid. This thesis implements an already established algorithm for detecting faults, as well as an impedance-based algorithm for detecting where on the line the fault has occurred and develops a smart algorithm for future HDL conversion using Simulink. Using the algorithms, the ways in which this implementation can be used to create a smarter grid are the fundamental basis for this research. Simulink was used to create a two-bus power system, create environment variables, and then Matlab was used to program the algorithm such that it could be FPGA-implementable, where the ways in which one can retrieve the data from a power line has been theorized. This novel approach to creating a smarter grid was theorized and created such that real-world applications may be further implemented in the future.

The increase of electronics in road vehicles comes along with a broad variety of possibilitiesin terms of safety, handling and comfort for the users. A rising complexityof the vehicle subsystems and components accompanies this development and has tobe managed by increased electronic control. More potential elements, such as sensors,actuators or software codes, can cause a failure independently or by mutually influencingeach other. There is a need of a structured approach to sort the faults from avehicle dynamics stability perspective.This thesis tries to solve this issue by suggesting a fault classification method and faulttolerantcontrol strategies. Focus is on typical faults of the electric driveline and thecontrol system, however mechanical and hydraulic faults are also considered. Duringthe work, a broad failure mode and effect analysis has been performed and the faultshave been modeled and grouped based on the effect on the vehicle dynamic behaviour.A method is proposed and evaluated, where faults are categorized into different levelsof controllability, i. e. levels on how easy or difficult it is to control a fault for the driver,but also for a control system.Further, fault-tolerant control strategies are suggested that can handle a fault with acritical controllability level. Two strategies are proposed and evaluated based on thecontrol allocation method and an electric vehicle with typical faults. It is shown thatthe control allocation approaches give less critical trajectory deviation compared to noactive control and a regular Electronic Stability Control algorithm.To conclude, this thesis work contributes with a methodology to analyse and developfault-tolerant solutions for electric vehicles with improved traffic safety. / <p>QC 20131010</p>

As the automotive industry constantly makes technological progress, higher demands are placed on safety, environmentally friendly and durability. Modern vehicles are headed towards increasingly complex system, in terms of both hardware and software making it important to detect faults in any of the components. Monitoring the engine’s health has traditionally been done using expert knowledge and model-based techniques, where derived models of the system’s nominal state are used to detect any deviations. However, due to increased complexity of the system this approach faces limitations regarding time and knowledge to describe the engine’s states. An alternative approach is therefore data-driven methods which instead are based on historical data measured from different operating points that are used to draw conclusion about engine’s present state. In this thesis a proposed diagnostic framework is presented, consisting of a systematically approach for fault classification of known and unknown faults along with a fault size estimation. The basis for this lies in using principal component analysis to find the fault vector for each fault class and decouple one fault at the time, thus creating different subspaces. Importantly, this work investigates the efficiency of taking multiple classifiers into account in the decision making from a performance perspective. Aggregating multiple classifiers is done solving a quadratic optimization problem. To evaluate the performance, a comparison with a random forest classifier has been made. Evaluation with challenging test data show promising results where the algorithm relates well to the performance of random forest classifier.

Uninterrupted power supply is an important requirement for electric ship since it has to confront frequent travel and hostilities. However, the occurrence of faults in the shipboard power systems interrupts the power service continuity and leads to the severe damage on the electrical equipments. Faults need to be quickly detected and isolated in order to restore the power supply and prevent the massive cascading outage effect on the electrical equipments. This thesis presents an Artificial Neural Network (ANN) based method for the fault classification and location in MVDC shipboard power systems using the transient information in the fault voltage and current waveforms. The proposed approach is applied to the cable of an equivalent MVDC system which is simulated using PSCAD. The proposed method is efficient in detecting the type and location of DC cable faults and is not influenced by changes in electrical parameters like fault resistance and load.

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Modernized and advanced electricity transmission and distribution infrastructure ensures reliable, efficient, and affordable delivery of electric power. The complexity of fault location problem increases with the proliferation of unusual topologies and with the advent of renewable energy-based power generation in the smart grid environment. The proliferation of new Intelligent Electronic Devices (IEDs) provides a venue for the implementation of more accurate and intelligent fault location methods. This dissertation focuses on intelligent fault location methods for smart power grids and it aims at improving fault location accuracies and decreasing the cost and the mean time to repair damaged equipment in major power outages subsequently increasing the reliability of the grid. The developed methods utilize wavelet transformation to extract the traveling wave information in the very fast voltage and current transients which are initiated immediately after a fault occurs, support vector machines to classify the fault type and identify the faulted branches and finally Bewley diagrams to precisely locate the fault. The approach utilizes discrete wavelet transformation (DWT) for analysis of transient voltage and current measurements. The transient wavelet energies are calculated and utilized as the input for support vector machine (SVM) classifiers. SVM learns the mapping between inputs (i.e. transient voltages and/or currents wavelet energies) and desired outputs (i.e. faulty phase and/or faulty section) through processing a set of training cases. This dissertation presents the proposed methodologies applied to three complex power transmission systems. The first transmission system is a three-terminal (teed) three-phase AC transmission network, a common topology in high- and extra high-voltage networks. It is used to connect three substations that are wide apart from each other through long transmission lines with a tee-point, which is not supported by a substation nor equipped with a measuring device. The developed method overcomes the difficulties introduced by the discontinuity: the tee point. The second topology is a hybrid high voltage alternative current (HVAC) transmission line composed of an overhead line combined with an underground cable. The proposed fault location method is utilized to overcome the difficulties introduced by the discontinuity at the transition point from the overhead line to the underground cable and the different traveling wave velocities along the line and the cable. The third topology is a segmented high voltage direct current (HVDC) transmission line including an overhead line combined with an underground cable. This topology is widely utilized to transmit renewable energy-based electrical power from remote locations to the load centers such as from off-shore wind farms to on-shore grids. This dissertation introduces several enhancements to the existing fault type and fault location algorithms: improvement in the concept of fault type classification and faulty section identification by using SVMs with smaller inputs and improvements in the fault location in the complex configurations by utilizing less measurements from the terminals. / Ph. D.

As software becomes more important to society, the number, age, and complexity of systems grow. Software organizations require continuous process improvement to maintain the reliability, security, and quality of these software systems. Software organizations can utilize data from manual fault classification to meet their process improvement needs, but organizations lack the expertise or resources to implement them correctly. This dissertation addresses the need for the automation of software fault classification. Validation results show that automated fault classification, as implemented in the MiSFIT tool, can group faults of similar nature. The resulting classifications result in good agreement for common software faults with no manual effort. To evaluate the method and tool, I develop and apply an extended change taxonomy to classify the source code changes that repaired software faults from an open source project. MiSFIT clusters the faults based on the changes. I manually inspect a random sample of faults from each cluster to validate the results. The automatically classified faults are used to analyze the evolution of a software application over seven major releases. The contributions of this dissertation are an extended change taxonomy for software fault analysis, a method to cluster faults by the syntax of the repair, empirical evidence that fault distribution varies according to the purpose of the module, and the identification of project-specific trends from the analysis of the changes.

This project, conducted at Ericsson AB, investigates the feasibility of implementing machine learning techniques in order to classify dump files for more effi cient trouble report routing. The project focuses on supervised machine learning methods and in particular Bayesian statistics. It shows that a program utilizing Bayesian methods can achieve well above random prediction accuracy. It is therefore concluded that machine learning methods may indeed become a viable alternative to human classification of trouble reports in the near future. / Detta examensarbete, utfört på Ericsson AB, ämnar att undersöka huruvida maskininlärningstekniker kan användas för att klassificera dumpfiler för mer effektiv problemidentifiering. Projektet fokuserar på övervakad inlärning och då speciellt Bayesiansk klassificering. Arbetet visar att ett program som utnyttjar Bayesiansk klassificering kan uppnå en noggrannhet väl över slumpen. Arbetet indikerar att maskininlärningstekniker mycket väl kan komma att bli användbara alternativ till mänsklig klassificering av dumpfiler i en nära framtid.

Yes / This paper presents a fault detection method in three-phase induction motors using Wavelet Packet Transform (WPT). The proposed algorithm takes a frame of samples from the three-phase supply current of an induction motor. The three phase current samples are then combined to generate a single current signal by computing the Root Mean Square (RMS) value of the three phase current samples at each time stamp. The resulting current samples are then divided into windows of 64 samples. Each resulting window of samples is then processed separately. The proposed algorithm uses two methods to create window samples, which are called non-overlapping window samples and moving/overlapping window samples. Non-overlapping window samples are created by simply dividing the current samples into windows of 64 sam-ples, while the moving window samples are generated by taking the first 64 current samples, and then the consequent moving window samples are generated by moving the window across the current samples by one sample each time. The new window of samples consists of the last 63 samples of the previous window and one new sample. The overlapping method reduces the fault detection time to a single sample accuracy. However, it is computationally more expensive than the non-overlapping method and requires more computer memory. The resulting window sam-ples are separately processed as follows: The proposed algorithm performs two level WPT on each resulting window samples, dividing its coefficients into its four wavelet subbands. Infor-mation in wavelet high frequency subbands is then used for fault detection and activating the trip signal to disconnect the motor from the power supply. The proposed algorithm was first implemented in the MATLAB platform, and the Entropy power Energy (EE) of the high frequen-cy WPT subbands’ coefficients was used to determine the condition of the motor. If the induction motor is faulty, the algorithm proceeds to identify the type of the fault. An empirical setup of the proposed system was then implemented, and the proposed algorithm condition was tested under real, where different faults were practically induced to the induction motor. Experimental results confirmed the effectiveness of the proposed technique. To generalize the proposed meth-od, the experiment was repeated on different types of induction motors with different working ages and with different power ratings. Experimental results show that the capability of the pro-posed method is independent of the types of motors used and their ages.