Fault Detection Probability Research Articles

Reflection behaviour of SH0 from small defects in thin sheets, with application to EMAT inspection of titanium

ABSTRACT Defects can arise within the weld during the laser welding process of thin titanium sheets, and it is critical that these defects are detected. Shear horizontal (SH) waves offer good potential for defect detection, but require an understanding of how the ultrasonic waves interact with defects, in order to develop an optimised inspection setup, increasing the probability of defect detection. The geometry of a defect strongly affects the reflected signal magnitude, with finite element simulations showing that defect width and length determine the maximum SH0 wave reflection. Experiments confirm this behaviour. The defect width is shown to affect the reflection behaviour due to wave interference. Phase differences between the front face reflection and back face reflection cause a shift in the peak position. In addition, the experimental work presented in this paper also shows the potential for electromagnetic acoustic transducers to be used for the inspection of titanium components.

Ultrasonic Features for Evaluation of Adhesive Joints: A Comparative Study of Interface Defects.

Ultrasonic non-destructive evaluation in pulse-echo mode is used for the inspection of single-lap aluminum adhesive joints, which contain interface defects in bonding area. The aim of the research is to increase the probability of defect detection in addition to ensuring that the defect sizes are accurately estimated. To achieve this, this study explores additional ultrasonic features (not only amplitude) that could provide more accurate information about the quality of the structure and the presence of interface defects. In this work, two types of interface defects, namely inclusions and delaminations, were studied based on the extracted ultrasonic features in order to evaluate the expected feasibility of defect detection and the evaluation of its performance. In addition, an analysis of multiple interface reflections, which have been proved to improve detection in our previous works, was applied along with the extraction of various ultrasonic features, since it can increase the probability of defect detection. The ultrasonic features with the best performance for each defect type were identified and a comparative analysis was carried out, showing that it is more challenging to size inclusion-type defects compared to delaminations. The best performance is observed for the features such as peak-to-peak amplitude, ratio coefficients, absolute energy, absolute time of flight, mean value of the amplitude, standard deviation value, and variation coefficient for both types of defects. The maximum relative error of the defect size compared to the real one for these features is 16.9% for inclusions and 3.6% for delaminations, with minimum errors of 11.4% and 2.2%, respectively. In addition, it was determined that analysis of the data from repetitive reflections from the sample interface, namely, the aluminum-adhesive second and third reflections, that these contribute to an increase in the probability of defect detection.

A probabilistic early fault detection model for a feedback machining system with multiple types of spares

This paper studies corrective and preventive maintenance to provide a quality control policy. The corrective maintenance, depending on the time, of a feedback machining system model with a finite source and standbys is presented. Moreover, the system has a known number of servers to repair the damaged units, and it contains an inspector to ensure the maintenance quality of the repaired units. The exact value of the probability of n units in the system will be obtained by using an efficient algorithm that depends on the Laplace transformation. To promote the concept of preventive maintenance, we use this probability to get the probability of early fault detection as a function of time and in the steady state. The applicability of this model is discussed for different system capacities.

Do we need high-order mutation in fault-based Boolean-specification testing?

Fault-based Boolean-specification testing is an essential weak mutation testing technique since the executing paths of the programs are usually dependent on those Boolean expressions in predicate statements. In mutation testing, the coupling effect hypothesis assumes that a test set that detects all the simple faults in the program can detect a high percentage of complex faults. Such a hypothesis suggests that we do not need to perform high-order mutation testing in practice. Many empirical results have shown the existence of the coupling effect. However, as fault-based Boolean-specification testing is mainly used in safety-critical scenarios, the question of whether high-order mutation testing is necessary for Boolean-specifications needs to be answered more strictly. To answer whether we need high-order mutation testing in fault-based Boolean-specification testing, we conduct an empirical study on general-form Boolean expressions. Experimental results show us that: (1) Generally, it is slightly easier to kill high-order mutants (HOMs) than first-order mutants (FOMs) since fault detection probabilities for complex faults (HOMs) are marginally higher than that for simple faults (FOMs) for almost all the cases. (2) However, for any fault types in Boolean-specifications, the coupling effect hypothesis is not guaranteed to hold with absolute certainty. (3) When HOMs involve the expression negation fault (ENF) or the operator reference fault (ORF), the percent of killed HOMs is lower. Such results suggest that, if the testing resource is sufficient, high-order mutation testing could be carried out on fault types ENF and ORF in fault-based Boolean-specification testing.

Vehicle Technical Inspection Results in Relation to EU Directives and Selected EU Countries

Although road accidents caused by defective vehicles are relatively rare, the roadworthiness of vehicles is important for road safety. In the European Union, the technical conditions that need to be fulfilled by the vehicles using public roads are regulated by EU directives. According to these directives, technical inspections are organized in every EU country. Our study analyzed the vehicle technical inspections in Slovenia over four consecutive years. The statistics for passenger cars and commercial vehicles are compared with two other EU countries: Germany and Finland. From this comparison, it follows that certain differences exist between the countries, although vehicle technical inspections are regulated by the same EU directives. Finally, multivariate regression models were built to predict probabilities of various vehicle defects detected during regular technical supervision as a function of the vehicle’s age. It was found that the probability of fault detection can be effectively modelled as a function of vehicle age.

Statistical model for assessing the reliability of non-destructive testing systems by solving inverse problems

Objectives. The wear monitoring of metal structural elements of power plants—in particular, pipelines of nuclear power plants—is an essential means of ensuring safety during their operation. Monitoring the state of the pipeline by direct inspection requires a considerable amount of labor, as well as, in some cases, the suspension of power plant operation. In order to reduce costs during monitoring measures, it is proposed to use mathematical modeling. This work aimes to develop a mathematical model of a diagnostic system for assessing the probability of detection of defects by solving inverse problems.Methods. A binomial model for assessing the reliability of monitoring, comprising the Berens-Hovey parametric model of the probability of detection of defects and a parametric model based on studying test samples, was analyzed. As an alternative to this binomial model, a computational method for assessing the reliability of non-destructive testing systems by solving an inverse problem was proposed. To determine the parameters of the defect detection probability curve, the model uses data obtained by various monitoring teams over a long period of power plant operation. To serve as initial data, the defect distribution density over one or more of the following characteristics can be used: depth, length, and/or cross-sectional area of the defect. Using the proposed mathematical model, a series of test calculations was performed based on nine combinations of initial data. The combinations differed in the confidence coefficient of the initial monitoring system, the parameters of the distribution of defects, and the sensitivity of the monitoring system.Results. The calculation data were used to construct curves of the probability density of detected defects as a function of the defect size, recover the values of the defect distribution parameters under various test conditions, and estimate the error of recovering the parameters. The degree of imperfection of the system was estimated using the curve of the detection probability of a defect by a certain monitoring system.Conclusions. Under constraints on the data sample size, the proposed methodology allows the metal monitoring results to be applied with greater confidence than currently used methods at the same time as evaluating the efficiency of monitoring carried out by individual test teams or laboratories. In future, this can be used to form the basis of a recommendation of the involvement of a particular team to perform diagnostic work.

Improvement of cell internal weak defects detection under process variation by optimizing test path and test pattern

Advances in integrated circuit process technology have led to new defect mechanisms, and weak resistive defects in standard cells have received attention in addition to traditional defect types. Such defects are categorized as small delay defects that affect the reliability of a circuit. The delay introduced by such defects overlaps with the delay distribution resulting from the random process variation. Combining the results of the theoretical analysis, we propose a test method to improve the probability of detecting cell internal weak defects in the presence of process variation. The path with the minimum logic depth is selected as the test path, and the test pattern that enhanced the small delay of weak defects is paired with the test path. The improvements in defect detection probability over traditional tests are demonstrated on the ISCAS89 benchmark circuits. The results show that the proposed method provides an order of magnitude improvement in the probability of detecting cell internal weak defects.

Defect detectability analysis via Probability of defect detection between traditional and deep learning methods in numerical simulations

X-ray computed tomography (XCT) is one of the most powerful imaging techniques in non-destructive testing (NDT) for detecting, analysing and visualising defects such as pores, fibres, cracks etc. in industrial specimens. Detecting defects in X-ray images, however, is still a challenging problem, as it strongly depends on the quality of the XCT images. Numerical XCT simulation proved to be valuable in order to increase both image quality and detection performance. In this work, we thus analyse the differences between traditional segmentation techniques (i.e., k-means, watershed, Otsu thresholding) and deep learning-based methods (i.e., U-Net, V-Net, modified 3D U-Net) in terms of their defect detection capacity using virtual XCT images. For this purpose, we apply the probability of defect detection (POD) approach on simulated X-ray computed tomography data from aluminium cylinder heads. The XCT simulation tool SimCT was used to generate X-ray radiographs and respective reconstructions from a specimen series which features different well-defined defects with varying sizes, shapes and locations. To generate POD curves and to specify detection limits, the segmentation algorithms are used in predefined regions for defect detection via a hit/miss approach. A comparison and visualisation of six different types of defects is illustrated in 2D and 3D images, together with their POD curves and detection limits.

Non-Destructive Evaluation of the Quality of Adhesive Joints Using Ultrasound, X-ray, and Feature-Based Data Fusion

The aim of this work is to achieve reliable nondestructive evaluation (NDE) of adhesively bonded aerospace components by developing novel multidimensional data fusion techniques, which would combine the information obtained by ultrasonic and X-ray NDE methods. Separately, both NDE techniques have their advantages and limitations. The integration of data obtained from pulse echo immersion ultrasound testing and radiography holds immense potential to help improve the reliability of non-destructive evaluation. In this study, distinctive features obtained from single techniques, traditional ultrasonic pulse echo testing, and radiography, as well as fused images, were investigated and the suitability of these distinctive features and fusion techniques for improving the probability of defect detection was evaluated. For this purpose, aluminum single lap joints with brass inclusions were analyzed using ultrasound pulse echo and radiography techniques. The distinctive features were extracted from the data obtained, and images of features obtained by both techniques were fused together. Different combinations of features and fusion algorithms were investigated, considering the desire to automate data evaluation in the future.

A robust model selection framework for fault detection and system health monitoring with limited failure examples: Heterogeneous data fusion and formal sensitivity bounds

Fault detection models play a fundamental role in monitoring the health state of engineering systems subject to degradation processes. Data-driven fault detection models, albeit very effective when trained on large databases of failures, fail to perform well under a lack of failure examples. Because reliable engineering systems seldom fail, data shortage is often inevitable. To overcomes failure data scarcity, this work proposes a new model selection framework for the robust selection of fault detection and system health monitoring models. We combine heterogeneous sources of information (time-to-failure and sensors data), a model of the system structure, and mathematical bounds on the sensitivity and specificity of components fault classifiers. We use Support Vector Machines (SVMs) to detect components faults and Scenario theory to derive formal sensitivity and specificity bounds. The component predictions are combined within a system structure-function for system health states estimation. A novel model selection strategy optimizes the hyper-parameters of the SVM ensemble by minimizing system-level prediction errors and generalization error bounds of the individual fault classifiers. One of the main advantages of the proposed framework is a set of formal epistemic bounds on fault detection and false alarm probabilities quantifying the lack of data uncertainty affecting the fault detection rate. We test the method on three representative case studies: (1) On randomized fault detection experiments with synthetic data, (2) on ten SVM models for predictive maintenance of industrial health care imaging systems, and (3) on a real-world PHM challenge problem. The results prove the efficacy of the proposed approach and its usefulness for solving fault detection.

Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems

Higher order spectra exhibit a powerful detection capability of low-energy fault-related signal components, buried in background random noise. This paper investigates the powerful nonlinear non-stationary instantaneous wavelet bicoherence for local gear fault detection. The new methodology of selecting frequency bands that are relevant for wavelet bicoherence fault detection is proposed and investigated. The capabilities of wavelet bicoherence are proven for early-stage fault detection in a gear pinion, in which natural pitting has developed in multiple pinion teeth in the course of endurance gearbox tests. The results of the WB-based fault detection are compared with a stereo optical fault evaluation. The reliability of WB-based fault detection is quantified based on the complete probability of correct identification. This paper is the first attempt to investigate instantaneous wavelet bicoherence technology for the detection of multiple natural early-stage local gear faults, based on comprehensive statistical evaluation of the industrially relevant detection effectiveness estimate—the complete probability of correct fault detection.

Empirical Approach to Defect Detection Probability by Acoustic Emission Testing

Estimation of probability of defect detection (POD) is one of the most important problems in acoustic emission (AE) testing. It is caused by the influence of the material microstructure parameters on the diagnostic data, variability of noises, the ambiguous assessment of the materials emissivity, and other factors, which hamper modeling the AE data, as well as the a priori determination of the diagnostic parameters necessary for calculating POD. In this study, we propose an empirical approach based on the generalization of the experimental AE data acquired under mechanical testing of samples to a priori estimation of the AE signals emitted by the defect. We have studied the samples of common industrial steels 09G2S (similar to steel ANSI A 516-55) and 45 (similar to steel 1045) with fatigue cracks grown in laboratory conditions during cyclic testing. Empirical generalization of data using probabilistic models enables estimating the conditional probability of record emissivity and amplitudes of AE signals. This approach allows to eliminate the existing methodological gap and to build a comprehensive method for assessing the probability of fatigue cracks detection by the AE testing.

Analysis of Developers’ Network and Change Burst Metrics as Predictors of Software Faults

Introduction: Many software quality metrics that can be used as proxies of measuring software quality by predicting software faults have previously been proposed. However determining a superior predictor of software faults given a set of metrics is difficult since prediction performances of the proposed metrics have been evaluated in non–uniform experimental contexts. There is need for software metrics that can guarantee consistent superior fault prediction performances across different contexts. Such software metrics would enable software developers and users to establish software quality. Objectives: This research sought to determine a predictor for software faults that requires least effort to detect software faults and has least cost of misclassifying software components as faulty or not given developers’ network metrics and change burst metrics. Methods: Experimental data for this study was derived from Jmeter, Gedit, POI and Gimp open source software projects. Logistic regression was used to predict faultiness of a file while linear regression was used to predict number of faults per file. Results: Change burst metrics model exhibited the highest fault detection probabilities with least cost of mis-classification of components as compared to the developers’ network model. Conclusion: The study found that change burst metrics could effectively predict software faults.

Reliability analysis of station autonomous computer system based on fuzzy dynamic fault tree and Markov model

AbstractStation autonomous computer system is the core of the centralized traffic control (CTC) system. The working mode is dual computer hot standby, and its reliability is very important. Due to the dynamic and fuzzy issue with the reliability analysis of station autonomous computer system, a reliability analysis method is formulated based on fuzzy dynamic fault tree (DFT) and Markov model. First, the structure and working principle of the dual computer hot standby autonomous computer system and the double 2‐vote‐2 autonomous computer system are analyzed, and two autonomous computer systems based on DFT are formulated. Then, the failure rates and the fault detection probabilities are considered as fuzzy variables and represented as triangular fuzzy variables. The fuzzy state transition matrix of the system is obtained by conversion of DFT to the Markov model. Finally, the fuzzy reliability of the system is calculated by Laplace transform. Simulation results show that the reliability of the dual computer hot standby autonomous computer system is higher than that of the double 2‐vote‐2 autonomous computer system. Besides, the reliability of the hardware system is not only related to the structure of the system, but also affected by the uncertain unit failure rate, fault detection probabilities, and so on.

Evaluation of detection capability of eddy current probes with stochastic decision threshold for inspecting pits on austenitic stainless steel welding

It is still a big challenge to calculate the probability of defect detection for inspecting pits on austenitic stainless steel welding using traditional POD models. Because we determine the decision threshold, the POD curve has a lot of changes as the decision threshold changes, there is no clear reason to insist which threshold is reasonable. This study proposes a new probability of detection (POD) model to quantitatively assess the detection capability of eddy current probes for inspecting pits on austenitic stainless steel welding. The experimental results show that the proposed model is more reasonable than traditional ones. The novel POD model was employed to analyze three eddy current probes, uniform, TR, plus-point probes on austenitic stainless steel welding. The results reveal that the uniform eddy current probe has the best detection capability for inspecting pits on among the three probes.

Probability of defect detection in glass fibre reinforced polymers using pulse compression favourable frequency modulated thermal wave imaging

Infrared Non-Destructive Evaluation (IRNDE) is an emerging approach among the Non-Destructive Testing (NDT) techniques to evaluate sub-surface defects, due to its non-contact, whole field, fast and quantitative defect detection abilities. Among various recently proposed aperiodic Thermal Non-destructive Testing and Evaluation (TNDT&E) methods, pulse compression favourable thermal wave imaging approaches gained significant importance due to their improved defect detection capability in terms of sensitivity and resolution. The present work attempts to explore the applicability of pulse-compression favourable Frequency Modulated Thermal Wave Imaging (FMTWI) approach for testing and evaluation of Glass Fibre Reinforced Polymer (GFRP) sample by considering the Peak to Side Lobe Ratio (PSLR) as a figure of merit. Results clearly depict that in pulse compression favourable FMTWI exhibits higher probability for detection of sub-surface defects of higher aspect ratio, with the subtraction of background temporal temperature distribution of the test sample in comparison with the presence of background.

Challenges concerning test case specifications in automotive software testing: assessment of frequency and criticality

Automotive test case specifications document test cases to be performed for a specific test object at a defined test level. They are a fundamental part of a structured automotive testing process, as required by the ISO 26262. The aim of our research is to identify challenges from a practitioner’s point of view that lead to poor quality of test case specifications and thus negatively impact time, cost, and probability of defect detection. We designed an exploratory case study to systematically identify challenges focusing on (C) creation, (P) processing, and (Q) quality assurance related aspects of test case specifications. We conducted 17 semi-structured interviews covering a German OEM as well as three of its automotive suppliers and analyzed them qualitatively. We investigated causes and consequences arising from the challenges. Additionally, we conducted a descriptive survey to assess frequency and criticality. The identified challenges were summarized in a taxonomy consisting of nine main categories: (1) availability and (2) content-related problems with input artifacts, problems related to (3) a lack of knowledge, (4) the test case description, (5) the test case specification content, (6) processes, (7) communication, (8) quality assurance, and (9) tools. The challenges were assessed by 26 internal and 10 external employees. Hence, we identified differences between these groups in terms of access to documents, incomplete requirements, scope of model series, process, and tool-related aspects. Overall, the study results underline the necessity of quality assurance measures for test case specifications. Based on the assessments, our research indicates a broad range of test case description related challenges that are promising candidates for improving test case specification quality.

Probability of defect detection in pulse compression favourable thermal excitation schemes for infra‐red non‐destructive testing

InfraRed non-destructive testing and evaluation (IRNDT&E) has emerged as a promising approach for non-destructive testing and evaluation (NDT&E) of various materials because of its inherent merits such as remote, non-invasive, qualitative as well as quantitative inspection capabilities. Among the various IRNDT&E techniques, recently proposed modulated pulse compression favourable thermal wave imaging (PCTWI) techniques, especially frequency modulated thermal wave imaging (FMTWI) and its digitised version digitised FMTWI (DFMTWI) have gained popularity over the conventional sinusoidal modulated [lock-in thermography (LT)] and pulse-based thermographic techniques [pulse thermography (PT) and pulse phase thermography (PPT)] by providing better depth resolution, in less experimentation time, using low peak power excitation heat sources. The present work highlights a comparative study on highly depth resolved continuous depth scanning PCTWI techniques, with single frequency thermal excited LT, on the basis of defect detection probability. The proposed study has been carried out on a glass fibre reinforced polymer test sample with Teflon inserts as defects by considering peak side lobe ratio as a figure of merit. The experimental results clearly show that the probability of detection of defects for DFMTWI is far superior as compared to FMTWI and LT.

Investigations on probability of defect detection using differential filtering for pulse compression favourable frequency modulated thermal wave imaging for inspection of glass fibre reinforced polymers

Thermal Non-Destructive Testing and Evaluation (TNDT&E) plays a crucial role in industrial quality control and structural health monitoring of a variety of materials. Among various TNDT&E modalities, active Infrared Thermography (IRT) has emerged as an extremely promising approach and has gained enormous significance due to its quick, whole field, non-contact and quantitative defect detection capabilities. Pulse Compression favourable Thermal Wave Imaging (PCTWI) especially Frequency Modulated Thermal Wave Imaging (FMTWI) has become popular among a number of active IRT techniques because of increment in defect detection sensitivity as well as test resolution. The present work attempts to explore the applicability of differential filtering post processing scheme for pulse compression favourable FMTWI for enhanced detection contrast, resolution and Probability of Detection (PoD). The proposed scheme has been applied on a Glass Fibre Reinforced Polymer (GFRP) sample with sub-surface flat bottom hole (FBH) defects located inside the sample at different depths. The results presented clearly demonstrate that the differential contrast approach enhances the defect detection probabilities by considering maximum and minimum deviation dip values as a figure of merit. Hence, pulse compression favourable FMTWI employing differential filtering manifests higher Probability of Detection (PoD) for defects located at different depths as compared to taking into account the peak Correlation Coefficient (CC) as a statistical figure of merit. Further Probability of Detection (PoD) of the pulse compression favourable FMTWI technique has been improved by differential filtering post-processing based scheme that reduces the memory requirement, computational cost as well as complexity.

Guest Editorial: Non‐Destructive Testing

Guest Editorial: Non‐Destructive Testing