Fault Prediction Approach Research Articles

Neuro-Fuzzy Framework for Fault Prediction in Electrical Machines via Vibration Analysis

The advent of Industry 4.0 has ushered in a new era of technological advancements, particularly in integrating information technology with physical devices. This convergence has given rise to smart devices and the Internet of Things (IoT), revolutionizing industrial processes. However, despite the push towards predictive maintenance, there still is a significant gap in fault prediction algorithms for electrical machines. This paper proposes a signal spectrum-based machine learning approach for fault prediction, specifically focusing on bearing faults. This study compares the effectiveness of traditional neural network algorithms with a novel approach integrating fuzzy logic. Through extensive experimentation and analysis of vibration spectra from various mechanical faults in bearings, it is demonstrated that the fuzzy-neuro network model outperforms traditional neural networks, achieving a validation accuracy of 99.40% compared to 94.34%. Incorporating fuzzy logic within the neural network framework offers advantages in handling complex fault combinations, showing promise for applications requiring higher accuracy in fault detection. While initial results are encouraging, further validation with more complex fault scenarios and additional fuzzy layers is recommended to fully explore the potential of fuzzy-neuro networks in fault prediction for electrical machines.

Ensemble feature ranking approach for software fault prediction

Software fault prediction, which aims to find and fix probable flaws before they appear in real-world settings, is an essential component of software quality assurance. This article provides a thorough analysis of the use of feature ranking algorithms for successful software failure prediction. In order to choose and prioritise the software metrics or qualities most important to fault prediction models, feature ranking approaches are essential. The proposed focus on applying an ensemble feature ranking algorithm to a specific software fault dataset, addressing the challenge posed by the dataset’s high dimensionality. In this extensive study, we examined the effectiveness of multiple machine learning classifiers on six different software projects: jedit, ivy, prop, xerces, tomcat, and poi, utilising feature selection strategies. In order to evaluate classifier performance under two scenarios—one with the top 10 features and another with the top 15 features—our study sought to determine the most relevant features for each project. SVM consistently performed well across the six datasets, achieving noteworthy results like 98.74% accuracy on “jedit” (top 10 features) and 91.88% on “tomcat” (top 10 features). Random Forest achieving 89.20% accuracy on the top 15 features, on “ivy.” In contrast, NB repeatedly recording the lowest accuracy rates, such as 51.58% on “poi” and 50.45% on “xerces” (the top 15 features). These findings highlight SVM and RF as the top performers, whereas NB was consistently the least successful classifier. The findings suggest that the choice of feature ranking algorithm has a substantial impact on the fault prediction models’ predictive accuracy and effectiveness. When using various ranking systems, the research also analyses the trade-offs between computing complexity and forecast accuracy.

Dynamic Incipient Fault Forecasting for Power Transformers Using an LSTM Model

Dissolved gas analysis (DGA) is a traditional approach for power transformer fault diagnostics based on measurement of gas contamination. Hydrocarbon gases generated and dissolved in transformer oil during operation can increase in density as fault conditions predominate. Critical determination of gas concentration changes and assessment trending of dissolved gases for fault prediction and prevention of transformer damage is essential. In this paper, a dynamic fault prediction approach is proposed using a long short-term memory (LSTM) model with intelligent classification to determine the running state of a transformer for prediction and avoidance of potential transformer damage. In the paper, the LSTM model processed DGA data collected from real on-site transformer field measurements and predicts future dissolved gas concentrations in time sequence. Four artificial intelligence diagnostic models (support vector machine (SVM), K-nearest neighbors (KNN), decision tree, and artificial neural network (ANN)) were rendered and used for comparative fault prediction assessment. By comparing experimental results from the different LSTM-based models, this paper asserts that the LSTM-KNN model provides the highest and most reliable prediction accuracy for power transformers.

Service-oriented model-based fault prediction and localization for service compositions testing using deep learning techniques

As service-oriented computing systems become more buoyant and complex, the occurrence of faults dramatically increases. Fault prediction plays a crucial role in the service-oriented computing paradigm, aiming to reduce testing cost while maximizing testing quality to utilize testing resources effectively and increase the reliability of service compositions. Although various fault prediction techniques were considered in software testing, service-oriented systems were less fortunate, in which most of the studies have focused on single web services testing rather than service compositions. Moreover, mainly the detection of faulty/non-faulty services was addressed, ignoring the estimate of faults count, their severity, as well as predicting when and where such faults would occur. In this paper, a multilateral model-based fault prediction and localization approach is proposed using deep learning techniques for web service compositions testing rather than single web service testing, which uniquely predicts not only faulty services, but also their count and severity level, location of faults, and time at which faults would occur. Three deep learning models are investigated: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and a proposed hybrid model based on both CNN and RNN. The proposed approach is language-independent, as it adopts process metrics rather than code metrics to overcome the code unavailability concern of services. The experimental analysis adopted main performance metrics on multiple public datasets to evaluate its efficiency and effectiveness. The results indicated that the hybrid CNN_RNN model achieves an average accuracy range of 84%–95.7%, where the RNN and CNN models individually achieve 75%–90% and 70%-79.3% respectively. Thus, the hybrid model increases the accuracy level by 5%–10% and 15%–20%, while achieving the least mean square error of 30% and 60% compared to the RNN and CNN models respectively. In terms of time, the RNN model consumes less average time as of 30–50 ms for the different datasets of variant sizes compared to the CNN and hybrid CNN_RNN models that consume 79–102 and 177–224 ms respectively. Thus, RNN model consumes around 50%–80% less time than those of the CNN and hybrid models respectively.

GIS Fault Prediction Approach Based on IPSO-LSSVM Algorithm

With the improvement of industrialization, the importance of equipment failure prediction is increasing day by day. Accurate failure prediction of gas-insulated switchgear (GIS) in advance can reduce the economic loss caused by the failure of the power system to operate normally. Therefore, a GIS fault prediction approach based on Improved Particle Swarm Optimization Algorithm (IPSO)-least squares support vector machine (LSSVM) is proposed in this paper. Firstly, the future gas conditions of the GIS to determine the characteristic data of SF6 decomposition gas are analyzed; Secondly, a GIS fault prediction model based on LSSVM is established, and the IPSO algorithm is used to normalize the parameters LSSVM. The parameters of c and radial basis kernel function σ2 are optimized, which can meet the needs of later search accuracy while ensuring the global search capability in the early stage. Finally, the effectiveness of the proposed method is verified by the fault data of gas-insulated switch. Simulation results shows that, compared with the prediction methods based on IGA-LSSVM and PSO-LSSVM, the prediction accuracy rate of the proposed method reached 92.1%, which has the smallest prediction absolute error, higher accuracy and stronger prediction ability.

Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines

Industrial revolution 4.0 has enabled the advent of new technological advancements, including the introduction of information technology with physical devices. The implementation of information technology in industrial applications has helped streamline industrial processes and make them more cost-efficient. This combination of information technology and physical devices gave birth to smart devices, which opened up a new research area known as the Internet of Things (IoT). This has enabled researchers to help reduce downtime and maintenance costs by applying condition monitoring on electrical machines utilizing machine learning algorithms. Although the industry is trying to move from scheduled maintenance towards predictive maintenance, there is a significant lack of algorithms related to fault prediction of electrical machines. There is quite a lot of research going on in this area, but it is still underdeveloped and needs a lot more work. This paper presents a signal spectrum-based machine learning approach toward the fault prediction of electrical machines. The proposed method is a new approach to the predictive maintenance of electrical machines. This paper presents the details regarding the algorithm and then validates the accuracy against data collected from working electrical machines for both cases. A comparison is also presented at the end of multiple machine learning algorithms used for training based on this approach.

Recursive approach for multiple step-ahead software fault prediction through long short-term memory (LSTM)

The advancement of technologies demands a sustainable solution. To ensure the software system’s sustainability, diminishing the software faults before the implementation requires utmost attention, along with an effective procedure to predict the faults. A software system’s maximum number of faults can be neutralized if it can be predicted at the earliest possible time. Therefore, we applied Long short-term memory (LSTM) to predict the faults of multi-time stamps ahead using a recursive approach. The Min-Max scaler and one of the power transformation methods, Box-Cox are used to normalize the software fault data. The traditional software reliability growth models (SRGMs) are also used to predict faults. The performance of the LSTM and SRGMs models are compared based on their prediction accuracy evaluation. The observed prediction error of LSTM models is much lower than the SRGMs.

An Unsupervised Software Fault Prediction Approach Using Threshold Derivation

Software fault prediction models help the software quality assurance team to manage the resources optimally during software maintenance. Most of the recently proposed fault prediction approaches are helpful on labeled datasets only. Recently, several threshold-based software fault prediction approaches have been proposed. However, these approaches do not incorporate the distribution of software metrics for metric threshold derivation; hence, they demonstrate poor performance. To fill this gap, we develop an automated fault prediction approach, namely threshold clustering labeling/threshold clustering labeling plus (TCLP), which does not need a labeled dataset. It can identify the faulty and nonfaulty artifacts on unlabeled datasets by self-learning. Our proposed approach is an extension of the state-of-the-art technique known as CLAMI. Unlike CLAMI, we derive the metrics threshold using logarithmic transformation. Thereafter, we label the instances into binary classes (faulty/nonfaulty) using the metric threshold values. TCLP extends this approach one step further by performing fault prediction using a random forest algorithm. The empirical evaluation of the proposed approach on 28 datasets (with the different number of metrics and granularity) collected from five software groups shows that the proposed unsupervised method obtains significantly better results than those of the state-of-the-art methods. The proposed approach impressively enhances the performance of CLAMI in terms of accuracy, F-measure, and Mathew’s correlation coefficient.

Diversity based imbalance learning approach for software fault prediction using machine learning models

The Software fault prediction (SFP) target is to distinguish between faulty and non-faulty modules. The prediction model’s performance is vulnerable to the class imbalance issue in SFP. The existing oversampling approaches generate relatively identical synthetic data, which results in over-generalization and less diverse data. Moreover, many undesirable noisy modules are introduced while generating synthetic data. In this study, we propose the Weighted Average Centroid based Imbalance Learning Approach (WACIL), an effective synthetic over-sampling technique to mitigate the imbalance issue. The WACIL first finds borderline instances, then generates pseudo-data of them through a weighted average centroid concept and filters out inappropriate noise data through a filtration process. We conducted experiments on 24 PROMISE and NASA projects and compared them with some of the existing sampling approaches using K-Nearest Neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT) and Deep Neural Network (DNN) as classification models. WACIL achieves superior results in terms of Fall Out Rate (FOR), F-measure and Area Under Curve (AUC) and obtains comparable results in terms of Recall and G-mean compared to the competitive approaches. The statistical analysis indicates that WACIL’s ability to outperform the other over-sampling techniques is significant under the statistical Wilcoxon signed rank test and matched pairs rank biserial correlation coefficient effect size. Hence, WACIL is advisable as a competent choice to deal with the imbalance issue in SFP.

An Unsupervised Bayesian OC-SVM Approach for Early Degradation Detection, Thresholding, and Fault Prediction in Machinery Monitoring

In the literature pertaining to condition-based maintenance using machine learning, the class of unsupervised approaches has yet to be fully explored. In particular, the topics of fault prediction and unsupervised degradation modeling, which have significant importance to practice, have not been widely studied. In this article, we propose a novel unsupervised approach for early degradation detection and fault prediction, termed the Bayesian one-class support vector machine (B-OCSVM), which is based on OC-SVM and the hierarchical Bayesian framework. The proposed approach aims to address key gaps in the literature by proposing a new unsupervised OC-SVM hyperparameter estimation method for early degradation detection and new degradation threshold, which is used to predict when a detected degradation process will manifest as a fault. Unlike traditional prognostic approaches, the proposed degradation threshold can be defined without prior knowledge pertaining to the fault. Fault prediction is achieved using a novel prognostic in conjunction with a hierarchical Bayesian degradation framework. We validate and benchmark the proposed approach against the state of the art using run-to-failure data from three datasets to demonstrate the earlier detection point of the proposed approach compared to existing approaches and to showcase how the proposed degradation threshold and novel prognostic can be used effectively to predict when faults will occur. In addition, the results show that B-OCSVM generalizes well to a number of different operating conditions and fault types, which signifies that the early detection and prognostic elements of the proposed approach have strong practical significance in real-world applications.

Improving Cross-Project Defect Prediction with Weighted Software Modules via Transfer Learning

Cross-project defect prediction is investigated to resolve the trouble that software program defect prediction besides historic data. However, there are differences in distribution of software metrics of different software projects, which decreases the overall performance of cross-project defect prediction. This research presents a transfer learning-based cross-project fault prediction approach. The weights of the training software modules are determined by analogy with gravity and compared to those in the test set. The costs associated with different prediction errors are considered. Cost-sensitive C4.5 is employed on weighted training data for cross-project defect prediction. 10 projects from NASA are used in our experiments. Our suggested technique delivers promising cross-project defect prediction results, according to the results, 0.81, 0.41 and 0.8 in average PD value, F-measure value and AUC value, which are best compared to Naïve Bayes based cross-project defect prediction.

Software fault prediction based on the dynamic selection of learning technique: findings from the eclipse project study

An effective software fault prediction (SFP) model could help developers in the quick and prompt detection of faults and thus help enhance the overall reliability and quality of the software project. Variations in the prediction performance of learning techniques for different software systems make it difficult to select a suitable learning technique for fault prediction modeling. The evaluation of previously presented SFP approaches has shown that single machine learning-based models failed to provide the best accuracy in any context, highlighting the need to use multiple techniques to build the SFP model. To solve this problem, we present and discuss a software fault prediction approach based on selecting the most appropriate learning techniques from a set of competitive and accurate learning techniques for building a fault prediction model. In work, we apply the discussed SFP approach for the five Eclipse project datasets and nine Object-oriented (OO) project datasets and report the findings of the experimental study. We have used different performance measures, i.e., AUC, accuracy, sensitivity, and specificity, to assess the discussed approach’s performance. Further, we have performed a cost-benefit analysis to evaluate the economic viability of the approach. Results showed that the presented approach predicted the software’s faults effectively for the used accuracy, AUC, sensitivity, and specificity measures with the highest achieved values of 0.816, 0.835, 0.98, and 0.903 for AUC, accuracy, sensitivity, and specificity, respectively. The cost-benefit analysis of the approach showed that it could help reduce the overall software testing cost.

Mining Software Repository for Cleaning Bugs Using Data Mining Technique

Despite advances in technological complexity and efforts, software repository maintenance requires reusing the data to reduce the effort and complexity. However, increasing ambiguity, irrelevance, and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories. Thus, there is a need for a repository mining technique for relevant and bug-free data prediction. This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software. To predict errors in mining data, the Apriori algorithm was used to discover association rules by fixing confidence at more than 40% and support at least 30%. The pruning strategy was adopted based on evaluation measures. Next, the rules were extracted from three projects of different domains; the extracted rules were then combined to obtain the most popular rules based on the evaluation measure values. To evaluate the proposed approach, we conducted an experimental study to compare the proposed rules with existing ones using four different industrial projects. The evaluation showed that the results of our proposal are promising. Practitioners and developers can utilize these rules for defect prediction during early software development.

A probabilistic sequence classification approach for early fault prediction in distribution grids using long short-term memory neural networks

As the global power grid must undergo a profound transformation in the coming decades to ensure reliable and cost-effective operation in a system with large shares of intermittent renewable energy generation, a critical element will be to leverage advanced data-driven predictive tools to optimise grid management activities. As it is expected that existing grids will be operated more to their limits, it is important to obtain better operational insights and estimations of the time to equipment failure to provide useful operational guidance and maintenance prioritisation support for grid operators. In this regard, this paper proposes a novel and real-time applicable method for fault prediction in 10 kV underground oil-insulated power cables using low-resolution data from a real case study from a Danish distribution system operator. The developed method is based on a sequence classification approach using long short-term memory neural networks where three different operational states are defined (Normal, Early warning, and Critical warning) to allow for prediction flexibility and better indication of the presence of systemic faults. Moreover, to enhance the data foundation, this paper investigates a Virtual Sample Generation method based on an adaptive Gaussian distribution. The capability of the proposed method yields satisfying results with prediction accuracy on the test set reaching as high as ~90%, hence proving the usefulness of the proposed approach and paving the way for smarter maintenance protocols.

Fault prediction of gas-insulated system with hypersensitive optical monitoring and spectral information

Hazardous discharge is the primary threat to power apparatus. Utilizing the fact that discharge is companied by intrinsic light radiation in specific spectral bands, a micro ternary spectral hypersensitive sensor array is developed for condition monitoring and fault prediction. With this new concept, a multispectral diagnosis strategy is proposed and implemented on a gas-insulated system, demonstrating significant potential for fault classification as well as quantitative risk evaluation, circumventing the necessities of phase-resolved statistics and complex denoising algorithms which are the most challenging but necessary aspects for traditional discharge diagnosis. Thus, it is an intrinsic and stable approach for practical fault prediction, especially for HVDC gas-insulated system.

Gearbox Fault Prediction of Wind Turbines Based on a Stacking Model and Change-Point Detection

The fault diagnosis and prediction technology of wind turbines are of great significance for increasing the power generation and reducing the downtime of wind turbines. However, most of the current fault detection approaches are realized by setting a single alarm threshold. Considering the complicated working conditions of wind farms, such methods are prone to ignore the fault, send out a false alarm, or leave insufficient troubleshooting time. In this work, we propose a gearbox fault prediction approach of wind turbines based on the supervisory control and data acquisition (SCADA) data. A stacking model composed of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBOOST) was constructed as the normal behavior model to describe the normal conditions of the wind turbines. We used the Mahalanobis distance (MD) instead of the residual to measure the deviation of the current state from the normal conditions of the turbines. By inputting the MD series into the proposed change-point detection algorithm, we can obtain the change point at which the fault symptom begins to appear, and thus achieving the fault prediction of the gearbox. The proposed approach is validated on the historical data of 5 wind turbines in a wind farm, which proves its effectiveness to detect the fault in advance.

An approach for fault prediction in SOA-based systems using machine learning techniques

PurposeSoftware fault prediction is an important concept that can be applied at an early stage of the software life cycle. Effective prediction of faults may improve the reliability and testability of software systems. As service-oriented architecture (SOA)-based systems become more and more complex, the interaction between participating services increases frequently. The component services may generate enormous reports and fault information. Although considerable research has stressed on developing fault-proneness prediction models in service-oriented systems (SOS) using machine learning (ML) techniques, there has been little work on assessing how effective the source code metrics are for fault prediction. The paper aims to discuss this issue.Design/methodology/approachIn this paper, the authors have proposed a fault prediction framework to investigate fault prediction in SOS using metrics of web services. The effectiveness of the model has been explored by applying six ML techniques, namely, Naïve Bayes, Artificial Networks (ANN), Adaptive Boosting (AdaBoost), decision tree, Random Forests and Support Vector Machine (SVM), along with five feature selection techniques to extract the essential metrics. The authors have explored accuracy, precision, recall, f-measure and receiver operating characteristic curves of the area under curve values as performance measures.FindingsThe experimental results show that the proposed system can classify the fault-proneness of web services, whether the service is faulty or non-faulty, as a binary-valued output automatically and effectively.Research limitations/implicationsOne possible threat to internal validity in the study is the unknown effects of undiscovered faults. Specifically, the authors have injected possible faults into the classes using Java C3.0 tool and only fixed faults are injected into the classes. However, considering the Java C3.0 community of development, testing and use, the authors can generalize that the undiscovered faults should be few and have less impact on the results presented in this study, and that the results may be limited to the investigated complexity metrics and the used ML techniques.Originality/valueIn the literature, only few studies have been observed to directly concentrate on metrics-based fault-proneness prediction of SOS using ML techniques. However, most of the contributions are regarding the fault prediction of the general systems rather than SOS. A majority of them have considered reliability, changeability, maintainability using a logging/history-based approach and mathematical modeling rather than fault prediction in SOS using metrics. Thus, the authors have extended the above contributions further by applying supervised ML techniques over web services metrics and measured their capability by employing fault injection methods.

An empirical approach for complexity reduction and fault prediction for software quality attribute

An empirical approach for complexity reduction and fault prediction for software quality attribute

Big data analytics based fault prediction for shop floor scheduling

The current task scheduling mainly concerns the availability of machining resources, rather than the potential errors after scheduling. To minimise such errors in advance, this paper presents a big data analytics based fault prediction approach for shop floor scheduling. Within the context, machining tasks, machining resources, and machining processes are represented by data attributes. Based on the available data on the shop floor, the potential fault/error patterns, referring to machining errors, machine faults and maintenance states, are mined for unsuitable scheduling arrangements before machining as well as upcoming errors during machining. Comparing the data-represented tasks with the mined error patterns, their similarities or differences are calculated. Based on the calculated similarities, the fault probabilities of the scheduled tasks or the current machining tasks can be obtained, and they provide a reference of decision making for scheduling and rescheduling the tasks. By rescheduling high-risk tasks carefully, the potential errors can be avoided. In this paper, the architecture of the approach consisting of three steps in three levels is proposed. Furthermore, big data are considered in three levels, i.e. local data, local network data and cloud data. In order to implement this idea, several key techniques are illustrated in detail, e.g. data attribute, data cleansing, data integration of databases in different levels, and big data analytic algorithms. Finally, a simplified case study is described to show the prediction process of the proposed method.

A Fault Prediction Approach based on the Probabilistic Model for Improvising Software Inspection

Objective: Software development is a multitask activity performed by a team. Each activity involves with different tasks and complexity. To achieve quality of improvement it is important that each activity task should be fault free. But, finding and correcting faults are most expensive and time consuming. Methods: Software inspection is a static analysis technique which does not required program execution, instead it use inspector to make decision during the development. Findings: But it was observed in literature that inspection has bad records in finding accurate defects in software development. In this paper, we present a novel Fault Prediction Approach (FPA) based on the probabilistic model to improvise the software inspection to detect the defect accurately and cost effective for the quality software development. Application/ Improvement: Inspection is an effective activity to find the defects using empirical data in the initial stage of development. The proposed FPA investigate a probabilistic methods using modified Naive Bayes classification to estimate the probable faults in an experiment context to suggest fault controlling development. Further, the analysis investigates how FPA effectively identifying the faults during the inspection and impact in the quality development performance.