Detectable Faults Research Articles

AGA: An Accelerated Greedy Additional Algorithm for Test Case Prioritization

In recent years, many test case prioritization (TCP) techniques have been proposed to speed up the process of fault detection. However, little work has taken the efficiency problem of these techniques into account. In this paper, we target the Greedy Additional (GA) algorithm, which has been widely recognized to be effective but less efficient, and try to improve its efficiency while preserving effectiveness. In our Accelerated GA (AGA) algorithm, we use some extra data structures to reduce redundant data accesses in the GA algorithm and thus the time complexity is reduced from $\mathcal{O}(m^2n)$ to $\mathcal{O}(kmn)$ when $n > m$, where $m$ is the number of test cases, $n$ is the number of program elements, and $k$ is the iteration number. Moreover, we observe the impact of iteration numbers on prioritization efficiency on our dataset and propose to use a specific iteration number in the AGA algorithm to further improve the efficiency. We conducted experiments on 55 open-source subjects. In particular, we implemented each TCP algorithm with two kinds of widely-used input formats, adjacency matrix and adjacency list. Since a TCP algorithm with adjacency matrix is less efficient than the algorithm with adjacency list, the result analysis is mainly conducted based on TCP algorithms with adjacency list. The results show that AGA achieves 5.95X speedup ratio over GA on average, while it achieves the same average effectiveness as GA in terms of Average Percentage of Fault Detected (APFD). Moreover, we conducted an industrial case study on 22 subjects, collected from Baidu, and find that the average speedup ratio of AGA over GA is 44.27X, which indicates the practical usage of AGA in real-world scenarios.

STATIC ECCENTRICITY FAULT DETECTION METHOD FOR ELECTRICAL ROTATING MACHINES BASED ON THE MAGNETIC FIELD ANALYSIS IN THE AIR GAP BY MEASURING COILS

Electrical rotating machines have a great economic significance as they enable conversion of energy between mechanical and electrical state. Reliability and operation safety of these machines can be greatly improved by implementation of continuous condition monitoring and supervisory systems. Especially important feature of such systems is the ability of early fault detection. For this reason, several methods for detection and diagnosis of the machine faults have been developed and designed. As fault detection methods can largely differ in the types of detectable faults, machine adoptability and price of the system, a novel method was developed that can be used for cost-effective detection of various faults of electrical machine. Machine fault detection technique presented in this paper is based on the measurement of magnetic field in the air gap. Numerous studies have proven that crucial information about the machine condition can be determined based on measurement and analysis of the magnetic field in the air gap. It has also been confirmed that analysis of the air gap magnetic field can be used to detect, diagnose and recognize various electrical faults in their very early stage. Proposed method of positioning and installation of the measuring coils on ferromagnetic core parts within the air gap region of the machine enables differentiation of various faults. Furthermore, different faults can be detected if measuring coils are placed on the stator teeth then when placed on the rotor side. The paper presents method on how to analyse and process the measured voltages acquired from measuring coils placed within the machine, especially in the case of rotor static eccentricity detection. The methodology is explained by means of finite element method (FEM) calculations and verified by measurements that were performed on the induction machine. FEM calculation model was used to predict measurement coil output of the induction motor for healthy and various faulty states (at different amounts of static eccentricity). These results were then confirmed by measurements performed in the laboratory on the induction traction motor that was specially modified to enable measurements of faulty operation states of the machine. Measurements comprised of several machine fault conditions broken one rotor bar, broken multiple rotor bars, broken rotor end ring and various levels of rotor static eccentricity.
 Other methods used for faults detection are primarily based on the monitoring of quantities such as current and vibration and their harmonic analysis. This new system is based on the tracing the changes of induced voltage of the measuring coils installed on the stator teeth. Faults can be detected and differentiated based on RMS value of these voltages and the number of voltage spikes of voltage waveform i.e. without the need of harmonic analyses. If these coils are installed on the rotor it is possible to detect the stator winding faults in a similar manner.

Vibration based gear fault detection

Any transmission system is having gear is of important requirement. Early fault detection is a primary necessity. For this purpose a nonlinear mathematical model has been considered of gear contact .The entire system is represented by discretized ordinary differential equation taking backlash error and transmission error into consideration. The overall contact modeling is done in MATLAB simulink. A linear vibration analysis of normal gear and faulty gear is done using ANSYS Software. Two different types of conditions were considered, one with perfect condition and other with small crack. So for the gear contact analysis a nonlinear mathematical model has been considered. It is found that whenever b < δ, the frequency component is becoming strong with increasing in ω but becomes constant when δ < b, i.e. a very low relative angular displacement with high backlash. The ANSYS results concluded that fault detected near the mode shape frequency. And with comparative harmonic analysis it is being observed that the faulty gear has more fluctuating frequency response than the healthy one.

Data-driven Residual Generation for Early Fault Detection with Limited Data

&#x0D; &#x0D; &#x0D; Traditionally, fault detection and isolation community have used system dynamic equations to generate diagnosers and to analyze detectability and isolability of the dynamic systems. Model-based fault detection and isolation methods use system model to generate a set of residuals as the bases for fault detection and isolation. However, in many complex systems it is not feasible to develop highly accurate models for the systems and to keep the models updated during the system lifetime. Recently, data- driven solutions have received an immense attention in the industrial applications for several practical reasons. First, these methods do not require the initial investment and expertise for developing accurate models. Moreover, it is possible to update and retrain the diagnosers as the system or the environment change over time automatically. Finally, unlike the model-based methods it is straightforward to combine time series measurements such as pressure and voltage with other sources of information such as system operating hours to achieve a higher accuracy. In this paper, we extend the traditional model- based fault detection and isolation concepts such as residuals, and detectable and isolable faults to the data-driven domain. We then propose an algorithm to automatically generate residuals from the normal operating data. We compare the performance of our proposed approach with traditional model-based methods through a case study.&#x0D; &#x0D; &#x0D;

An improved sideband energy ratio for fault diagnosis of planetary gearboxes

Frequency spectrum analysis is an effective and direct way to understand the vibration behavior of a planetary gearbox (PG). Sideband structures, as useful and detectable fault information, have drawn widespread research attention to track and indicate the health conditions of the internal gears. However, for planetary gearboxes, different characteristic fault sidebands may mislead the diagnostic decisions, therefore, expert knowledge must be heavily relied on. The sideband related indicators, such as Sideband Energy Ratio (SER) and Sideband Index (SI), which synthesized the amplitudes of characteristic frequencies, have demonstrated their effectiveness in fault diagnosis of a PG. Whereas, the diagnostic mechanisms behind these sideband related indicators were not adequately studied. Moreover, how to properly select the numbers of sidebands to form a reliable indicator so far still has not yet been seriously considered. In this article, the diagnostic mechanisms of SER and SI are reexamined and analyzed via planetary gear vibration signal models with faulty gear. The drawbacks of these indicators are revealed over the theoretical analysis. According to the theoretical derivations, a selection of fewer sideband numbers is applied. A novel indicator, namely an Improved Sideband Energy ratio (ISER), is then proposed for the diagnostic task. The ISER and other sidebands indicators are testified through experimental data under different gear fault scenarios. Three typical intelligent classification algorithms, namely support vector machine (SVM), XGBoost, and deep neural network (DNN) are employed to demonstrate their diagnostic abilities. The results show that the ISER outperforms than SER, SI, and other statistical sideband indicators.

The instrument fault detection and identification based on kernel principal component analysis and coupling analysis in process industry

This paper investigates the fault detection problem of instruments in process industry. Considering the difficulty of fault identification and the problems of multivariable and large computation complexity based on traditional kernel principal component analysis (KPCA), this paper presents a new method for fault detection and identification, which combines the coupling analysis with kernel principal component for multivariable fault detection and employed the local outlier factor (LOF) for multivariable fault identification. The new method consists of three parts. Firstly, according to nonlinear correlation of multivariable, coupling analysis and module division of variables based on detrended cross-correlation analysis (DCCA) are considered to reduce false alarm rate (FAR) and missed detection rate (MDR) in fault detection and identification. Secondly, KPCA is employed to detect fault in each sub-module of variables. Finally, for the sub-module which has the fault detected in second step, the LOF is adopted to calculate abnormal contribution of each variable in sub-modules to realize fault identification. To prove that the new method has the better capability of processing multivariable fault detection and the more accuracy rate on fault detection and identification than the conventional methods of KPCA, a case study on Tennessee process is carried out at the end.

Structural health monitoring of railway tracks using IoT-based multi-robot system

A multi-robot-based fault detection system for railway tracks is proposed to eliminate manual human visual inspection. A hardware prototype is designed to implement a master–slave robot mechanism capable of detecting rail surface defects, which include cracks, squats, corrugations, and rust. The system incorporates ultrasonic sensor inputs coupled with image processing using OpenCV and deep learning algorithms to classify the surface faults detected. The proposed Convolutional Neural Network (CNN) model fared better compared to the Artificial Neural Network (ANN), random forest, and Support Vector Machine (SVM) algorithms based on accuracy, R-squared value, F1 score, and Mean-Squared Error (MSE). To eliminate manual inspection, the location and status of the fault can be conveyed to a central location enabling immediate attention by utilizing GSM, GPS, and cloud storage-based technologies. The system is extended to a multi-robot framework designed to optimize energy utilization, increase the lifetime of individual robots, and improve the overall network throughput. Thus, the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol is simulated using 100 robot nodes, and the corresponding performance metrics are obtained.

Multiple actuator-fault detectability analysis using invariant sets for discrete-time LPV systems

This paper characterises and analyses the minimum detectable fault (MDF) (named generalised MDF here) for multiple multiplicative actuator faults based on set-separation constraints of healthy and faulty residual sets for discrete-time linear parameter varying (LPV) systems with bounded inputs and uncertainties. First, multiple multiplicative actuator faults are equivalently transformed into the form of multiple additive actuator faults for the sake of obtaining the simplified faulty residual set explicitly. Then, two different generalised MDF for different multiple actuator faults are, respectively, defined, which can be computed via solving simple linear programming (LP) problems. Moreover, we propose three detectability conditions to guarantee the detection of multiple multiplicative actuator faults by using the invariant set-based method. Finally, we provide effective selection guidance for the detection of different multiple multiplicative actuator faults by systematically analysing the conservatism and characteristics of these three guaranteed detectability conditions. At the end of this paper, an electrical circuit example is used to illustrate the effectiveness of the proposed method.

3D seismic fault detection using the Gaussian process regression, a study on synthetic and real 3D seismic data

Faults are natural tectonic events as planar features along which rock units are moved. The patterns of these planes on seismic sections include linear or curvilinear features that are called edge in image processing, where amplitudes sharply change. In seismic data, these edge features made by fault traces are usually detected by seismic attributes, which need complex mathematical calculation such as a dip-steered cube. In this study, we introduce faults as global anomalies which disturb the normal interaction of seismic reflectors. Here, the normal interaction means the absence of intensive changes in reflectors trend in seismic sections. Fault detection as a global anomaly is done by the Gaussian process regression model (GPR), which is a nonparametric probabilistic model based on Bayesian statistics supporting noisy (Gaussian noise) and sparse features in data. In data mining, anomaly detection identifies items or events that do not match an expected pattern in a dataset. Global anomalies are sparse and affect a wide range of normal trends of data which are also held by fault features in seismic data. In this study, the GPR-based anomaly detection algorithm was implemented on the 3D seismic data of the Gulf of Mexico containing normal growth faults and a salt dome to detect salt boundary and fault traces in seismic data. In this respect, the reflections from rock units and fault features were taken into account as normal interactions and global anomalies, respectively, because faults disrupted the normal trend of reflectors in seismic sections. To detect fault locations, the location of a voxel in voxel grid where it is a part of fault in seismic data, after smoothing the seismic data, a Gaussian process (GP) model was trained on seismic data, attempting to describe the seismic amplitude data as a multivariate Gaussian model. However, GP regression fails to describe the seismic data at fault locations. Thus, the failure of the GP in the regression step was analyzed to separate the probable fault points, highlighted by calculating the variance of the GPR results. Finally, the detected probable fault points were improved and separated from background results by implementing a consistent reconstruction morphological algorithm. The results were validated using a similar structural index method, mean square error, and power signal to noise ratio indices in comparison with interpreted faults, implying the superiority of the proposed method in comparison with seismic attributes. The faults detected by the proposed method have the most structural similarity to faults interpreted. This similarity has improved by 22% compared to used attributes.

Can this fault be detected: A study on fault detection via automated test generation

Automated test generation can reduce the manual effort in improving software quality. A test generation method employs code coverage, such as the widely-used branch coverage, to guide the inference of tests. These tests can be used to detect hidden faults. An automatic tool takes a specific type of code coverage as a configurable parameter. Given an automated tool of test generation, a fault may be detected by one type of code coverage, but omitted by another. In frequently released software projects, the time budget of testing is limited. Configuring code coverage for a testing tool can effectively improve the quality of projects. In this paper, we conduct a study on whether a fault can be detected by specific code coverage in automated test generation. We build predictive models with 60 metrics of faulty source code to identify detectable faults under eight types of code coverage. In the experiment, an off-the-shelf tool, EvoSuite is used to generate test data. Experimental results based on four research questions show that different types of code coverage result in the detection of different faults; a code coverage can be used as a supplement to increase the number of detected faults if another coverage is applied first; for each coverage, the number of detected faults increases with its cutoff time in test generation. Our result shows that the choice of code coverage can be learned via multi-objective optimization from sampled faults and directly applied to new faults. This study can be viewed as a preliminary result to support the configuration of code coverage in the application of automated test generation.

A Revisit of Metrics for Test Case Prioritization Problems

For the test case prioritization problems, the average percent of faults detected (APFD) and its variant versions are widely used as metrics to evaluate prioritized test suite’s efficiency of fault detection. By a revisit of metrics for test case prioritization, we observe that APFD is only available for the scenarios where all test suites under evaluation contain the same number of test cases. Such a limitation is often overlooked, and lead to incorrect results when comparing fault detection efficiency of test suites with different sizes. Moreover, APFD cannot precisely illustrate the process of fault detection in the real world. Besides the APFD, most of its variants, including the NAPFD and the APFD[Formula: see text], have similar problems. This paper points out these limitations in detail by analyzing the physical explanation of APFD series metrics formally. In order to eliminate these limitations, we propose a series of improved metrics, including the relative average percent of faults detected (RAPFD) and the relative cost-cognizant weighted average percent of faults detected (RAPFD[Formula: see text]), to evaluate the efficiency of the test suite. Furthermore, for the scenario of parallel testing, a series of metrics including the relative average percent of faults detected in parallel testing ([Formula: see text]-RAPFD) and the relative cost-cognizant weighted average percent of faults detected in parallel testing ([Formula: see text]-RAPFD[Formula: see text]) are proposed too. All the proposed metrics refer to both the speed of fault detection and the constraint of the testing resource. A formal analysis and some examples show that all the proposed metrics provide much more precise illustrations of the fault detection process.

Novel Bidirectional O-Z-Source Circuit Breaker for DC Microgrid Protection

DC microgrids that feature a simple and efficient integration with renewable energy sources and energy storage elements have drawn increasing attention in industrial applications. In this article, a bidirectional Z-source circuit breaker with an O-shaped impedance network (abbreviated as O-Z-source circuit breaker) is proposed to guarantee the reliable operation of dc microgrids. By a simplified structure, the O-Z-source circuit breaker allows bidirectional power flow with fewer components compared with conventional Z-source circuit breakers. Increased operating power efficiency can also be obtained from the novel topology. Based on detailed mathematical models, design equations are derived for sizing the components in the O-Z-source circuit breaker. Moreover, a new concept of equivalent capacitance that is useful for estimating the minimum detectable fault ramp rate for Z-source circuit breakers is presented. A discussion over snubber circuits of the proposed breaker is also included. Finally, a laboratory prototype has been built to verify the analysis and design the O-Z-source circuit breaker.

Robust Incipient Fault Detection of Complex Systems Using Data Fusion

An incipient fault refers to the first change point when a system starts to deteriorate. Early detections of incipient faults are crucial to the safety, reliability, and effective predictive maintenance of complex engineering systems. However, it is very difficult to detect incipient faults at the initial stage of the system-degradation processes. To address this issue, an innovative data-fusion method is introduced to detect the incipient faults by integrating data collected from multiple sources instead of a single data source. The data-fusion problem is formulated as a convex optimization problem, aiming at reducing the false-alarm rate and the time to detect incipient faults. We demonstrate the proposed data-fusion method using a data set generated by the degraded aircraft engines. The numerical results have demonstrated that the proposed method can assist in reducing both the false-alarm rate and the time to detect incipient faults.

How does combinatorial testing perform in the real world: an empirical study

Studies have shown that combinatorial testing (CT) can be effective for detecting faults in software systems. By focusing on the interactions between different factors of a system, CT shows its potential for detecting faults, especially those that can be revealed only by the specific combinations of values of multiple factors (multi-factor faults). However, is CT practical enough to be applied in the industry? Can it be more effective than other industry-favored techniques? Are there any challenges when applying CT in practice? These research questions remain in the context of industrial settings. In this paper, we present an empirical study of CT on five industrial systems with real faults. The details of the input space model (ISM) construction, such as factor identification and value assignment, are included. We compared the faults detected by CT with those detected by the in-house testing teams using other methods, and the results suggest that despite some challenges, CT is an effective technique to detect real faults, especially multi-factor faults, of software systems in industrial settings. Observations and lessons learned are provided to further improve the fault detection effectiveness and overcome various challenges.

Model-based test case prioritization using selective and even-spread count-based methods with scrutinized ordering criterion.

Regression testing is crucial in ensuring that modifications made did not introduce any adverse effect on the software being modified. However, regression testing suffers from execution cost and time consumption problems. Test case prioritization (TCP) is one of the techniques used to overcome these issues by re-ordering test cases based on their priorities. Model-based TCP (MB-TCP) is an approach in TCP where the software models are manipulated to perform prioritization. The issue with MB-TCP is that most of the existing approaches do not provide satisfactory faults detection capability. Besides, their granularity of test selection criteria is not very good and this can affect prioritization effectiveness. This study proposes an MB-TCP approach that can improve the faults detection performance of regression testing. It combines the implementation of two existing approaches from the literature while incorporating an additional ordering criterion to boost prioritization efficacy. A detailed empirical study is conducted with the aim to evaluate and compare the performance of the proposed approach with the selected existing approaches from the literature using the average of the percentage of faults detected (APFD) metric. Three web applications were used as the objects of study to obtain the required test suites that contained the tests to be prioritized. From the result obtained, the proposed approach yields the highest APFD values over other existing approaches which are 91%, 86% and 91% respectively for the three web applications. These higher APFD values signify that the proposed approach is very effective in revealing faults early during testing. They also show that the proposed approach can improve the faults detection performance of regression testing.

Test Case Prioritization in Continuous Integration environments: A systematic mapping study

Abstract Context: Continuous Integration (CI) environments allow frequent integration of software changes, making software evolution more rapid and cost-effective. In such environments, the regression test plays an important role, as well as the use of Test Case Prioritization (TCP) techniques. Such techniques attempt to identify the test case order that maximizes certain goals, such as early fault detection. This research subject has been raising interest because some new challenges are faced in the CI context, as TCP techniques need to consider time constraints of the CI environments. Objective: This work presents the results of a systematic mapping study on Test Case Prioritization in Continuous Integration environments (TCPCI) that reports the main characteristics of TCPCI approaches and their evaluation aspects. Method: The mapping was conducted following a plan that includes the definition of research questions, selection criteria and search string, and the selection of search engines. The search returned 35 primary studies classified based on the goal and kind of used TCP technique, addressed CI particularities and testing problems, and adopted evaluation measures. Results: The results show a growing interest in this research subject. Most studies have been published in the last four years. 80% of the approaches are history-based, that is, are based on the failure and test execution history. The great majority of studies report evaluation results by comparing prioritization techniques. The preferred measures are Time and number/percentage of Faults Detected. Few studies address CI testing problems and characteristics, such as parallel execution and test case volatility. Conclusions: We observed a growing number of studies in the field. Future work should explore other information sources such as models and requirements, as well as CI particularities and testing problems, such as test case volatility, time constraint, and flaky tests, to solve existing challenges and offer cost-effective approaches to the software industry.

Semi-Tensor Product of Matrices Approach to the Problem of Fault Detection for Discrete Event Systems (DESs)

This brief focuses on presenting a state-based matrix approach to the problem of fault detection for discrete event systems (DESs). Firstly, the given system is modeled as a finite state machine and converted into algebraic structure with the help of semi-tensor product of matrices. Assuming that the faults influence only the state transition and can’t recover, there are two possible constructions for the system: a normative and a faulty one. Considering the evolutionary process for the two systems, labeled correlative matrices between current state and initial state are established, respectively, which have an advantage in computer storage compared with existent methods. And then, a theorem is proposed as a criterion of detectable faults, followed with a corresponding algorithm to verify whether the faults are detectable for a given system. Finally, the approach in this brief is tested effectively for the exhaust gas recirculation system.

A simple procedure for fault detectors design in SISO systems

In this work, we present a novel approach for fault detectors design and implementation in the case of actuator faults. Both the design and the implementation are focused on simplicity. The fault detector is based in an output observer that estimates the fault signal followed by a decision mechanism that detects the presence of a fault from the estimation. The observer consists of two transfer functions fed by the process manipulated variable and the sensor measurement. For the synthesis of the fault detector, we just need an input–output model of the process and two tuning parameters; one used in the observer, and the other in the decision mechanism. We present simple rules for the design considering the trade-off between the detection time, the minimum detectable fault and the false alarm rate. Our implementation method uses standard tools available in industrial control systems and we have applied it to a real two-tank system setup. The main contribution of this work is the simplicity of the design and implementation of the fault detector, making it suitable for process industry and for being managed by not experts in control systems. Another contribution is the a priori design based in intuitive engineering performance indices.

ANALYSIS OF DATA SKIPPING USING LOW TRANSITION SWITCH REGISTERS

The emerging scenario in the fields of VLSI testing has its own demand for fault diagnosis in VLSI circuits. If the area and size of the circuit increases the problem of test generation time becoming very tough. Efficient techniques for test generations are essential in order to reduce the test generation time and size. In Existing methods, Low transition Switch Register (LTSR) applies the corresponding repeated patterns of the generated test data. The complication in the LTSR technique exponentially increases power with respect to the circuit size. In case of circuit which has more stuck-at-fault, the LTSR method fails to provide suitable fault coverage and low power consumption. The proposed test data skipping scheme using Reconfigurable Johnson counter reduces the test data volume from the multiple test pattern and reduces switching transitions by skipping the test sequence mostly between the consecutive test sequences. The RJC based Test data skipping scheme has additional circuit which consists of various counters, Bit skipping circuit and logic gates. Memory unit consists of the whole test sequence then these test sequences are fed to RJC and Switching Transition counter. The consecutive test sequence are eliminated or skipped by comparing those counters and the state analysis of FSM. The proposed test data skipping scheme circuit is developed to achieve minimum test patterns and reduced scan power by skipping long scan chain switching activities. This present work with the above mentioned issues for LTSR and existing in VLSI circuits is to examine all detectable faults with proposed circuit. The Efficiency of such system is tested by Xilinx and ISE tools to generate test patterns to achieve high fault coverage with low power consumption over the conventional systems.

Multiple Multiplicative Actuator Fault Detectability Analysis Based on Invariant Sets for Discrete-time LPV Systems

This paper proposes a generalized minimum detectable fault (MDF) computation method based on the set-separation condition between the healthy and faulty residual sets for discrete-time linear parameter varying (LPV) systems with bounded inputs and uncertainties. First, we equivalently transform the multiple multiplicative actuator faults into the form of multiple additive actuator faults, which is beneficial to simplify the problem. Then, by considering the 1-norm of the fault vector, we define the generalized MDF in the case of multiple additive actuator faults, which can be computed via solving a simple linear programming (LP) problem. Moreover, an analysis of the effect of the input vector on the magnitude of the generalized MDF is made. Since the proposed generalized MDF computation method is robust by considering the bounds of inputs and uncertainties, robust fault detection (FD) can be guaranteed whenever the sum of the magnitudes of all occurred faults is larger than the magnitude of the generalized MDF. At the end of this paper, a numerical example is used to illustrate the effectiveness of the proposed method.