Fault Detection Ratio Research Articles

CausalViT: Domain generalization for chemical engineering process fault detection and diagnosis

Fault detection and diagnosis (FDD) is a promising technology for safe operation, quality control, and profitability improvement in chemical process systems. In practice, chemical process systems usually switch among operating conditions in response to market demands, fatigue, malfunctions of components and environments. However, FDD models often deteriorate when transferred from one operating condition to another. Current widely used FDD models are trained and tested in identical domains (or operating conditions) without emphasis on the capability of domain generalization. Furthermore, they ignore the causality that remains invariant among domains but excessively rely on statistical correlation that varies from domain to domain. This paper presents an FDD model named CausalViT. Causality and a gradient reversal operation are designed to enhance domain generalization. In CausalViT, a Causal Layer is constructed to discover the causal mechanism in features to purify superfluous domain-specific statistical correlation. Then, a domain classifier cooperating with gradient reversal layers is developed to further enhance the domain invariance. To evaluate the CausalViT, comparisons have been made between it and other advanced FDD models in six domains/modes in Tennessee Eastman Process. CausalViT shows superiority from the perspectives of both fault detection ratio and domain generalization, obtaining an average 16% improvement on domain generalization. Dimension reduction visualization shows the significance of domain generalization and gives a deeper insight into CausalViT. Furthermore, the convergence of the model and the effectiveness of components in the model have been analyzed and demonstrated.

Novel Metrics for Mutation Analysis

A measure of the “goodness” or efficiency of the test suite is used to determine the proficiency of a test suite. The appropriateness of the test suite is determined through mutation analysis. Several Finite State Machine (FSM) mutants are produced in mutation analysis by injecting errors against hypotheses. These mutants serve as test subjects for the test suite (TS). The effectiveness of the test suite is proportional to the number of eliminated mutants. The most effective test suite is the one that removes the most significant number of mutants at the optimal time. It is difficult to determine the fault detection ratio of the system. Because it is difficult to identify the system’s potential flaws precisely. In mutation testing, the Fault Detection Ratio (FDR) metric is currently used to express the adequacy of a test suite. However, there are some issues with this metric. If both test suites have the same defect detection rate, the smaller of the two tests is preferred. The test case (TC) is affected by the same issue. The smaller two test cases with identical performance are assumed to have superior performance. Another difficulty involves time. The performance of numerous vehicles claiming to have a perfect mutant capture time is problematic. Our study developed three metrics to address these issues: , , and In this context, most used test generation tools were examined and tested using the developed metrics. Thanks to the metrics we have developed, the research contributes to eliminating the problems related to performance measurement by integrating the missing parameters into the system.

An enhanced fault detection method for centrifugal chillers using kernel density estimation based kernel entropy component analysis

In building automation system, timely detecting the operational faults in water chillers is crucial to system operation management. As a linear data transformation technique, the performance of principal component analysis (PCA) based chiller fault detection method is limited, especially for the incipient ones, due to the nonlinearities in chillers. In addition, the control limit for the monitoring statistic, such as squared prediction error (SPE), is determined under the Gaussian assumption of the score variables, which can hardly be satisfied in water chillers. Therefore, an enhanced fault detection method with the application of kernel density estimation (KDE) and kernel entropy component analysis (KECA) algorithm is reported in this paper. Cauchy-Schwarz (CS) divergence was evaluated as monitoring statistic to measure the cosine of the angle between two data sets after being projected onto a dominated subspace, and then adopted as an index for dissimilarity. KDE with its bandwidth being optimized was also applied to estimate the distribution of the CS divergence, so that the control limit for fault monitoring could be determined. The proposed KDE based KECA-CS method was validated using the experimental data from ASHRAE RP-1043, and further compared to the PCA -SPE, kernel principal component analysis (KPCA)-SPE, and KECA-SPE methods. Results showed that the best performance could be realized when using the KDE based KECA-CS method. The reported fault detection ratio was over 68% for the seven typical chiller faults even at their corresponding least severity level. The average fault detection accuracy was over 90%.

Test Suite Reduction via Evolutionary Clustering

Test suite reduction is an effective way to reduce the cost of regression testing by identifying and removing redundant test cases from the original test suite. In this paper, we propose a novel cluster test suite reduction using an evolutionary multi-objective optimization algorithm. Specifically, we use a K-means algorithm to group similar test cases to the same cluster. Then the evolutionary algorithm is used to remove redundant test cases based on the clustering results, and optimization objects are represented as the coverage-related criteria, fault-related criteria and cost-related criteria. The experimental results involving eight subject programs show that the proposed method can outperform the other three state-of-the-arts with respect to both fault detection (4.61% −9.44%) and reduction ratio (4.10% −10.64%). Meanwhile, the experiments also prove that our method has a better performance of missing failure rate (0.049% −0.132%) and code coverage rate (3.34% −6.10%). Besides, the proposed method costs are found to be comparable to the other techniques.

Quartile Based Differential Protection of Power Transformer

A new technique for power transformer protection, that depends on the calculation of fault detection ratio (FDR) based on quartile of superimposed differential currents is presented. Internal faults are detected by comparing FDR with the predetermined threshold derived through analytical analysis and verified based on simulation results. The performance of the presented scheme is tested by modeling an existing Indian power transmission network using PSCAD/EMTDC software. Various types of abnormal conditions such as magnetizing inrush (including sympathetic and recovery) and over-excitation along with several types of internal (winding, inter-winding and turn-to-turn) faults have been investigated. Its validity has also been checked on the data of magnetizing inrush and simultaneous inrush with the internal fault in the transformer, which was obtained from the real network. The results reveal the effectiveness of the suggested technique in differentiating internal faults from other abnormal conditions. Further, it remains stable during heavy through fault with current transformer saturation. It is equally applicable for different ratings and winding connections of power transformers. Finally, comparative evaluation of the proposed scheme with the conventional and several existing techniques shows its superiority in terms of higher sensitivity during internal faults and improved stability in case of non-internal faults.

Similarity testing for role-based access control systems

ContextAccess control systems demand rigorous verification and validation approaches, otherwise, they can end up with security breaches. Finite state machines based testing has been successfully applied to RBAC systems and enabled to obtain effective test cases, but very expensive. To deal with the cost of these test suites, test prioritization techniques can be applied to improve fault detection along test execution. Recent studies have shown that similarity functions can be very efficient at prioritizing test cases. This technique is named similarity testing and assumes the hypothesis that resembling test cases tend to have similar fault detection capabilities. Thus, there is no gain from similar test cases, and fault detection ratio can be improved if test diversity increases.ObjectiveIn this paper, we propose a similarity testing approach for RBAC systems named RBAC similarity and compare to simple dissimilarity and random prioritization. RBAC similarity combines the dissimilarity degree of pairs of test cases with their relevance to the RBAC policy under test to maximize test diversity and the coverage of its constraints.MethodFive RBAC policies and fifteen test suites were prioritized using each of the three test prioritization techniques and compared using the Average Percentage Faults Detected metric.ResultsOur results showed that the combination of the dissimilarity degree to the relevance of a test case to RBAC policies in the RBAC similarity can be more effective than random prioritization and simple dissimilarity, by itself, in most of the cases.ConclusionThe RBAC similarity criterion is suitable as a test prioritization criteria for test suites generated from finite state machine models specifying RBAC systems.

Hybrid Test Case Optimization Approach Using Genetic Algorithm With Adaptive Neuro Fuzzy Inference System for Regression Testing

Abstract In an agile environment, regression testing is inevitable because it aims to attain software with better quality. Regression testing identifies whether an accurate result can be obtained for the corresponding input submitted. The test cases, hence, have an important role to play in the error-identification process of an application. An algorithm has been proposed in this paper that prioritizes the test cases based on the rate of fault detection and impact of faults. Artificial intelligence techniques have been incorporated with optimizing algorithms to reduce the overall number of test cases using minimization, selection, and prioritization collectively. Fuzzy K-means (FKM) clustering algorithm is used to cluster the test cases initially. The clustered test cases are further scaled down using the genetic algorithm (GA) combined with the adaptive neuro fuzzy inference system (ANFIS) called “Hybrid G-ANFIS.” The size of the test suite is reduced because only the optimal test cases are selected for further optimization by the ANFIS, from an already clustered test suite. This is done by fuzzy logic principles that select only the test cases that are needed for validating the changes in the software. Along with this, the test cases that have the ability to find faults by covering maximum code in a minimum time frame are also chosen. Optimization achieves a better outcome because it is done repetitively both by clustering and optimization algorithms continuously, which results in reducing the test cases considerably in the regression test suite. The proposed research work is evaluated in terms of performance measures, namely fault detection ratio, fault coverage, statement coverage, and an average percentage of faults detected (APFD) chart from which it is clear that a better optimization of the regression testing estimation process can be done.

Distributed Fault Detection Based on Credibility and Cooperation for WSNs in Smart Grids.

Due to the increasingly important role in monitoring and data collection that sensors play, accurate and timely fault detection is a key issue for wireless sensor networks (WSNs) in smart grids. This paper presents a novel distributed fault detection mechanism for WSNs based on credibility and cooperation. Firstly, a reasonable credibility model of a sensor is established to identify any suspicious status of the sensor according to its own temporal data correlation. Based on the credibility model, the suspicious sensor is then chosen to launch fault diagnosis requests. Secondly, the sending time of fault diagnosis request is discussed to avoid the transmission overhead brought about by unnecessary diagnosis requests and improve the efficiency of fault detection based on neighbor cooperation. The diagnosis reply of a neighbor sensor is analyzed according to its own status. Finally, to further improve the accuracy of fault detection, the diagnosis results of neighbors are divided into several classifications to judge the fault status of the sensors which launch the fault diagnosis requests. Simulation results show that this novel mechanism can achieve high fault detection ratio with a small number of fault diagnoses and low data congestion probability.

Effective product-line testing using similarity-based product prioritization

A software product line comprises a family of software products that share a common set of features. Testing an entire product-line product-by-product is infeasible due to the potentially exponential number of products in the number of features. Accordingly, several sampling approaches have been proposed to select a presumably minimal, yet sufficient number of products to be tested. Since the time budget for testing is limited or even a priori unknown, the order in which products are tested is crucial for effective product-line testing. Prioritizing products is required to increase the probability of detecting faults faster. In this article, we propose similarity-based prioritization, which can be efficiently applied on product samples. In our approach, we incrementally select the most diverse product in terms of features to be tested next in order to increase feature interaction coverage as fast as possible during product-by-product testing. We evaluate the gain in the effectiveness of similarity-based prioritization on three product lines with real faults. Furthermore, we compare similarity-based prioritization to random orders, an interaction-based approach, and the default orders produced by existing sampling algorithms considering feature models of various sizes. The results show that our approach potentially increases effectiveness in terms of fault detection ratio concerning faults within real-world product-line implementations as well as synthetically seeded faults. Moreover, we show that the default orders of recent sampling algorithms already show promising results, which, however, can still be improved in many cases using similarity-based prioritization.

Fault Modeling and Testing for Analog Circuits in Complex Space Based on Supply Current and Output Voltage

This paper deals with the modeling of fault for analog circuits. A two-dimensional (2D) fault model is first proposed based on collaborative analysis of supply current and output voltage. This model is a family of circle loci on the complex plane, and it simplifies greatly the algorithms for test point selection and potential fault simulations, which are primary difficulties in fault diagnosis of analog circuits. Furthermore, in order to reduce the difficulty of fault location, an improved fault model in three-dimensional (3D) complex space is proposed, which achieves a far better fault detection ratio (FDR) against measurement error and parametric tolerance. To address the problem of fault masking in both 2D and 3D fault models, this paper proposes an effective design for testability (DFT) method. By adding redundant bypassing-components in the circuit under test (CUT), this method achieves excellent fault isolation ratio (FIR) in ambiguity group isolation. The efficacy of the proposed model and testing method is validated through experimental results provided in this paper.

Pairwise testing for systems with data derived from real-valued variable inputs

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WCET-Aware Partial Control-Flow Checking for Resource-Constrained Real-Time Embedded Systems

Real-time embedded systems in diverse application domains, such as industrial control, automotive, and aerospace, are often safety-critical systems with stringent timing constraints that place strong demands on reliability and fault tolerance. Since fault-tolerance mechanisms inevitably add performance and/or resource overheads, it is important to guarantee a system's real-time constraints despite these overheads. Control-flow checking (CFC) is an effective technique for improving embedded systems' reliability and security by online monitoring and checking of software control flow to detect runtime deviations from the control-flow graph (CFG). Software-based CFC has high runtime overhead, and it is generally not applicable to resource-constrained embedded systems with stringent timing constraints. We present techniques for partial CFC (PCFC), which aims to achieve a tradeoff between runtime overhead, which is measured in terms of increases in worst case execution time (WCET), and fault-detection coverage by selectively instrumenting a subset of basic blocks. Experimental results indicate that PCFC significantly enables reductions of the program WCET compared to full CFC at the cost of reduced fault-detection ratio, thus providing a tunable fault-tolerance technique that can be adapted by the designer to suit the needs of different applications.

Evaluating test suite characteristics, cost, and effectiveness of FSM-based testing methods

ContextTesting from finite state machines has been investigated due to its well-founded and sound theory as well as its practical application. There has been a recurrent interest in developing methods capable of generating test suites that detect all faults in a given fault domain. However, the proposal of new methods motivates the comparison with traditional methods. ObjectiveWe compare the methods that generate complete test suites from finite states machines. The test suites produced by the W, HSI, H, SPY, and P methods are analyzed in different configurations. MethodComplete and partial machines were randomly generated varying numbers of states, inputs, outputs, and transitions. These different configurations were used to compare test suite characteristics (number of resets, test case length) and the test suite length (i.e., the sum of the length of its test cases). The fault detection ratio was evaluated using mutation testing to produce faulty implementations with an extra state. ResultsOn average, the recent methods (H, SPY, and P) produced longer test cases but smaller test suites than the traditional methods (W, HSI). The recent methods generated test suites of similar length, though P produced slightly smaller test suites. The SPY and P methods had the highest fault detection ratios and HSI had the lowest. For all methods, there was a positive correlation between the number of resets and the test suite length and between the test case length and the fault detection ratio. ConclusionThe recent methods rely on fewer and longer test cases to reduce the overall test suite length, while the traditional methods produce more and shorter test cases. Longer test cases are correlated to fault detection ratio which favored SPY, though all methods have a ratio of over 92%.

Study on the Method for Fault Sample Selection Based on Fuzzy Clustering

A new method of fault sample selection is presented. First, the mapping relationship is established between the system components and system functions attributes according to the similarity of the system components and system functions attributes, the components are clustered to the class by fuzzy clustering method; then the fault sample is selected in the class according to relation of the faults propagation, the selected fault sample is validated by using the function adequacy and test adequacy. Finally From experimental results, the conclusion can be drown that the cost of this method is lower and the fault detection ratio is high. Compare with the other method the new method has certain advantages.

Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods

An enhanced sensor fault detection, diagnosis and estimation (FDD&E) strategy is developed for centrifugal chillers using wavelet analysis method and principal component analysis (PCA) method. A number of sensors of concern in chiller system monitoring and control are assigned into a PCA model, which can group these correlated variables and capture the systematic variations of chillers. Raw measurements or simple processing measurements of sensors may deteriorate the performance of sensor FDD&E strategy using PCA because of the embodied noises and dynamics. Wavelet analysis can extract the approximations of sensor measurements by separating noises and dynamics. Using these approximation coefficients for PCA modeling we can improve the capability and reliability of fault detection and diagnosis as well as the accuracy of sensor fault estimation. This wavelet–PCA-based sensor FDD&E strategy was validated using field operation data of an existing centrifugal chiller plant while various sensor faults of different magnitudes were introduced. The results demonstrate that this strategy can produce better performance of sensor FDD&E in terms of fault detection ratio, diagnosis ratio and estimation accuracy by comparing with conventional PCA-based sensor FDD&E strategy using raw or simple processing measurements for PCA modeling.

Metamorphic Testing and Special Case Testing: A Case Study

This paper presents an integrated metamorphic testing environment MTest and reports an experimental analysis of the effectiveness of metamorphic testing, which is carried out using MTest with a real program of sparse matrix multiplication. Quantitative evaluation and comparison of special case testing, metamorphic testing with special and random test cases are illustrated with two measurements: mutation score and fault detection ratio. The case study shows that metamorphic testing and special case testing are complementary to each other, and with

An enhanced fault model for high defect coverage

AbstractIn this paper, we propose an enhanced fault model to increase coverage in detecting physical faults in LSI. First, as an alternative to the single stuck‐at fault model, we propose the “single gate logical fault with Hamming distance 1” (SGLFH1) model, which is capable of generating test pattern sets that detect more defects from the same number of faults. The characteristics of this fault model are discussed and the effectiveness of the test pattern sets generated by it is described. Next we show that the multiple detection test pattern set for single stuck‐at faults that is conventionally used to enhance defect coverage does not detect SGLFH1 faults satisfactorily. In addition, the effectiveness of the proposed model is shown by comparing the fault detection ratio of the SGLFH1 test pattern set for physical surrogate faults [1, 2] with the corresponding fault detection ratio of the multiple detection test pattern set for single stuck‐at faults. © 2001 Scripta Technica, Syst Comp Jpn, 32(6): 36–44, 2001

The performance of the N-fold requirement inspection method

Faults in system requirements can be very harmful. It is therefore often required that the inspection achieves a high fault detection ratio (FDR). To achieve this, a large number of inspectors is required. Large teams are known to be inefficient. Therefore, the N-fold requirements inspection method divides the inspectors intoN small efficient teams. All temas inspect the same requirements document. Experiments with both information and real-time systems demonstrate that the different teams detect different faults such that they achieve together a higher FDR value. The analysis suggests that the FDR is primarily a function of the level of expertise of the inspectors and of the number of teams. A quite simple probabilistic model that matches the experimental results enables the prediction of the FDR as a function of these two parameters. A diagram based on the model enables a fast estimation of the FDR and of the most effective number of inspections teams money-wise. The model may also be employed for measuring the efficiency of requirement inspection methods.