Comparing transition trees test suites effectiveness for different mutation operators

Current signature analysis of induction motor becomes very much popular for its Condition monitoring purpose. Most of these studies in recent years are focused on hardware. Though, to build an experimental set-up for fault emulating machines is not an easy task. It involves too much cost and risk of damage. Moreover, it is not possible to create different types of faults in the same machine. In this paper an accurate but simple model of 3-phase Induction motor is being proposed which describes the machine at low frequency as well as at high frequency for different types of fault situations. The model, based on DM, CM characteristics of the machine, is well suited for power frequency (50 Hz) and high frequency applications like adjustable speed drives (ASDs). A mechanical model of a three-phase induction machine is incorporated to this electrical model to make a universal model of dynamic squirrel cage induction motor for overall simulation. The proposed model has the feature to incorporate different types of electrical fault like, stator inter-turn fault, phase-to-phase and phase-to ground insulation fault etc. The premature conditions of the above faults can also be emulated.

A combined image processing and Nearest Neighbor Algorithm tool for classification of incipient faults in induction motor drives

A general framework and a method for model based diagnosis are suggested. In the framework, faults and modeling of faults are formally described. Then the method is used to construct a diagnosis system based on a structure of hypothesis tests. Using these principles, it is possible to diagnose a large variety of different types of faults. The framework and the method are applied to the design of a diagnosis system for an automotive engine. This application clearly shows the strength of these principles because several different types of sensor faults and leakage need to be diagnosed. The resulting diagnosis system is demonstrated using experiments on a real automotive engine.

Testing digital circuits is crucial for guaranteeing the correct and reliable functioning of electronic devices. Deriving high quality test suites to check the correctness of such devices is an important task. To estimate the quality of a test suite, a common approach is to simulate faults in a given circuit specification and to assess the fault coverage of the test suite. In this paper, we propose to use test suites derived at a high abstraction level, i.e. using Finite State Machines (FSMs), and to assess its fault coverage for three different types of faults. Those are single stuck-at faults, ‘bridge’ faults, and hardly detectable faults, which slightly modify the behavior of a single circuit gate. A set of tools was developed for this reason, and experimental results were obtained for a set of ITC'99 benchmarks (Second Release). The fault coverage for the proposed approach is over 90% in most of the cases.

Occurrence of unwanted resistance at terminal of IGBTs is a type of Incipient faults which occurs in 3-phase voltage source Inverter. This type of faults does not trigger the protective circuit and in due course of time leads to severe faults. The present work had made an attempt to identify this type of faults related to two IGBTs in the VSI scheme. Rigorous simulation is done with different cases of faults in a PSIM software platform. The current profiles obtained from the simulation are processed by data processing tools to obtain Mean Current vector (MCV). A two phase reference frame shows the distribution of MCV for different types of faults. The distribution is found to follow a particular pattern. This has encouraged the authors to use SVM classifier for analyzing the distributed pattern and classify different types of faults.

A large number of research works have addressed the importance of models in software engineering. However, the adoption of model-based techniques in software organizations is limited since these models are perceived to be expensive and not necessarily cost-effective. Focusing on model-based testing, this paper reports on a series of controlled experiments. It investigates the impact of state machine testing on fault detection in class clusters and its cost when compared with structural testing. Based on previous work showing this is a good compromise in terms of cost and effectiveness, this paper focuses on a specific state-based technique: the round-trip paths coverage criterion. Round-trip paths testing is compared to structural testing, and it is investigated whether they are complementary. Results show that even when a state machine models the behavior of the cluster under test as accurately as possible, no significant difference between the fault detection effectiveness of the two test strategies is observed, while the two test strategies are significantly more effective when combined by augmenting state machine testing with structural testing. A qualitative analysis also investigates the reasons why test techniques do not detect certain faults and how the cost of state machine testing can be brought down.

State machine testing has received genuine attention in the literature. Among the studied testing strategies are complete round-trip paths and transition trees that cover round-trip paths in a piecewise manner. Although trees are claimed to be equivalent to complete round-trip paths testing, there is anecdotal evidence that this is not the case. The core contribution of this thesis is therefore an empirical comparison between the effectiveness of the complete round-trip paths test suites and the transition trees test suites on the one hand and the effectiveness of the different techniques used to generate transition trees (breadth first traversal, depth first traversal, and random traversal) on the other hand.

Traditionally, mutation testing is used as test set generation and/or test evaluation criteria once it is considered a good fault model. This paper uses mutation testing for evaluating an automated static analyzer. Since static analyzers, in general, report a substantial number of false positive warnings, the intention of this study is to define a prioritization approach of static warnings based on their correspondence with mutations. On the other hand, knowing that Mutation Test has a high application cost, another possibility is to try to identify mutations of some specific mutation operators, which an automatic static analyzer is not adequate to detect. Therefore, this information can be used to prioritize the order of incrementally applying mutation operators considering, firstly, those with no correspondence with static warnings. In both cases, contributing to the establishment of incremental strategies on using automatic static analysis or mutation testing or even a combination of them. We used mutation operators as a fault model to evaluate the direct correspondence between mutations and static warnings. The main advantage of using mutation operators is that they generate a large number of programs containing faults of different types, which can be used to decide the ones most probable to be detected by static analyzers. We provide evidences on the correspondence between mutations and some types of static warnings. The results obtained for a set of 19 open-source programs indicate that: 1) static warnings may be prioritized based on their correspondence level with mutations; 2) specific set of mutation operators and their mutations may be prioritized based on their correspondence level with warnings. It is possible to provide an incremental testing strategy aiming at reducing the cost of both static analysis and mutation testing using the correspondence information between these activities/artifacts.

We propose a new batch bioprocess monitoring approach which combines the Gaussian mixture model (GMM) with the multiway analysis in localized discriminant embedding subspace for detecting and classifying different types of faults. Due to the inherent process multimodality within abnormal points, the capability of traditional multimay locality preserving projection (MLPP) is degraded. For the routine bioprocess operation with abnormal events, GMM is used to partition the training set with various types of faults into different clusters. Three localized distance matrices reflecting the relationships of nearby points are then computed. The extracted leading discriminant embedding directions can not only separate the normal and faulty data by maximizing the distance among nearby data points from different clusters, but also preserve the intrinsic geometrical structure within the multiple faulty clusters by minimizing the small distance and penalizing the large distance among neighboring data from the same cluster. The newly developed multiway localized discriminant embedding (MLDE) approach is applied to two test scenarios in the fed-batch penicillin fermentation process and compared with the conventional MLPP method. The results demonstrate that the MLDE approach is superior to the MFDA method in detecting abnormal events and classifying different types of faults in fed-batch process with higher accuracy and sensitivity.

Mutation testing is a well known technique for estimating and improving the quality of test suites. Given a test suite T for a system S, mutation testing systematically creates mutants of S and executes T to measure how many mutants T detects. If T does not detect some (non-equivalent) mutants, T can be improved by adding test cases that detect those mutants. Mutants are created by applying mutation operators. Mutation operators are important because they define the characteristics of the system that are tested as well as the characteristics that are improved in the test suite. While mutation operators are well defined for a number of programming paradigms such as sequential or multi-threaded, to the best of our knowledge, mutation operators have not been defined for the actor programming model. In this paper, we define and classify mutation operators that can be used for mutation testing of actor programs.

Mutation analysis is extensively used for the comparison of state-based testing methods that work from a finite state machine (FSM); In this thesis, we report on results from an experiment during which we compared different mutation operators used to generate FSM mutants. We randomly generated multiple synthetic FSMs and selected real-world FSMs; We created all the possible mutants from a complete set of FSM mutation operators; We generated multiple test suites using a pool of test paths; We executed all those test suites on FSM mutants while using different types of oracles, and compared mutation operators using a metric we define. Results help us identify mutation operators that lead to easy-to-reveal faults, possibly too easy to help discriminate between test suite construction techniques, as well as study the impact of various oracle strategies. We have developed a tool infrastructure called μFSM that automates the whole process of this experiment.

Internal winding faults are among the most common causes of transformer failure. Once a fault occurs, a fast and efficient method for its detection and location is required to repair and reconnect the device, thus avoiding further delays in the network operation. This paper introduces a simple method for the location of internal winding faults. This method is based on time-domain terminal measurements of wave propagation along the winding. By means of low-cost laboratory components (a lowvoltage DC source and an oscilloscope), different types of faults in layer-type windings can be detected and located with high accuracy. A frequency-domain distributed-parameter winding model is used to predict the transient response of the winding subjected to different types of faults. FEM simulations are used to compute the model parameters. A test case is presented to demonstrate the efficacy of the fault location method.

Current methods for processing acoustic signals for fault diagnosis are commonly based on the method of processing the vibration signals. It is difficult to obtain the information only existing in the acoustic signal. Therefore, considering the fault diagnosis based on the acoustic signal, a method based on the Logarithmic Mel (LM) spectrogram and a residual neural network (ResNet) is proposed. Firstly, to correspond with the auditory perception, the acoustic signal is processed and linearly represented in the LM spectrogram. Secondly, the ResNet model is introduced to extract the fault information from the LM spectrogram for the identification of the different types of faults. By training two datasets obtained from the gearbox platform, the different types of faults and the faults at different components of the gearbox are separately classified. The comparison results of different inputs with the LM spectrogram for training the ResNet model show that the LM spectrogram as the input could achieve the best classification accuracy. The comparison results of different classic deep learning models with the ResNet model demonstrate that the proposed methods can effectively extract features from acoustic signals in the LM spectrogram for the fault classification of the gearbox.

Brushless DC (BLDC) motors are used extensively in the industry for a wide variety of applications. In the Aerospace industry, they are mainly used in the control actuation system in launch vehicles. Different types of faults can develop in a BLDC motor. It is essential to detect these faults in the early stage itself before they develop into system failures. In this paper, fault detection in BLDC motors using parameter estimation technique is presented. Detailed fault diagnosis is subsequently carried out by a fuzzy inference system. Discrete Square Root Filtering in Covariance form (DSFC) algorithm is used initially for estimating the physical parameters of the BLDC motor. These parameters are then passed on to the fuzzy inference system which carries out the detailed diagnosis. Simulation studies carried out indicates that the developed fault detection and isolation scheme is capable of isolating different types of faults like open circuit, short circuit faults, performance degradation of magnets etc. in BLDC motors.

This paper presents an interpretable machine learning approach to classify different types of faults in ball bearing systems with high accuracy. These methods are based on ensemble methods (Random Forest, Gradient Boosting, and AdaBoost) which use data from the fan system collected by vibration monitors mounted on three axes. The proposed technique needs statistical feature inputs to classify different types of faults. We train models and explain their output using SHAP (SHapley Additive exPlanations). In the rigorous numerical study, we point out a suitable explanation for black-box predictive models trained on vibration data. Our model achieves 98 percent classification accuracy when classifying all fault types.