Bagged ensemble of Fuzzy C-Means classifiers for nuclear transient identification

This paper presents an ensemble-based scheme for nuclear transient identification. The approach adopted to construct the ensemble of classifiers is bagging; the novelty consists in using supervised fuzzy C-means (FCM) classifiers as base classifiers of the ensemble. The performance of the proposed classification scheme has been verified by comparison with a single supervised, evolutionary-optimized FCM classifier with respect of the task of classifying artificial datasets. The results obtained indicate that in the cases of datasets of large or very small sizes and/or complex decision boundaries, the bagging ensembles can improve classification accuracy. Then, the approach has been applied to the identification of simulated transients in the feedwater system of a boiling water reactor (BWR)

Bagged ensemble of Fuzzy C-Means classifiers for nuclear transient identification

This paper presents an ensemble-based scheme for nuclear transient identification. The approach adopted to construct the ensemble of classifiers is bagging; the novelty consists in using supervised fuzzy C-means (FCM) classifiers as base classifiers of the ensemble. The performance of the proposed classification scheme has been verified by comparison with a single supervised, evolutionary-optimized FCM classifier with respect of the task of classifying artificial datasets. The results obtained indicate that in the cases of datasets of large or very small sizes and/or complex decision boundaries, the bagging ensembles can improve classification accuracy. Then, the approach has been applied to the identification of simulated transients in the feedwater system of a boiling water reactor (BWR)

Fault Detection and Diagnosis of Electric Drives Using Intelligent Machine Learning Approaches

Electric motor condition monitoring can detect anomalies in the motor performance which have the potential to result in unexpected failure and financial loss. This study examines different fault detection and diagnosis approaches in induction motors and is presented in six chapters. First, an anomaly technique or outlier detection is applied to increase the accuracy of detecting broken rotor bars. It is shown how the proposed method can significantly improve network reliability by using one-class classification technique. Then, ensemble-based anomaly detection is utilized to compare different methods in ensemble learning in detection of broken rotor bars. Finally, a deep neural network is developed to extract significant features to be used as input parameters of the network. Deep autoencoder is then employed to build an advanced model to make predictions of broken rotor bars and bearing faults occurring in induction motors with a high accuracy

Similarity-based methods for machine diagnosis

This work presents a data-driven condition-based maintenance system based on similarity-based modeling (SBM) for automatic machinery fault diagnosis. The proposed system provides information about the equipment current state (degree of anomaly), and returns a set of exemplars that can be employed to describe the current state in a sparse fashion, which can be examined by the operator to assess a decision to be made. The system is modular and data-agnostic, enabling its use in different equipment and data sources with small modifications. The main contributions of this work are: the extensive study of the proposition and use of multiclass SBM on different databases, either as a stand-alone classification method or in combination with an off-the-shelf classifier; novel methods for selecting prototypes for the SBM models; the use of new similarity functions; and a new production-ready fault detection service. These contributions achieved the goal of increasing the SBM models performance in a fault classification scenario while reducing its computational complexity. The proposed system was evaluated in three different databases, achieving higher or similar performance when compared with previous works on the same database. Comparisons with other methods are shown for the recently developed Machinery Fault Database (MaFaulDa) and for the Case Western Reserve University (CWRU) bearing database. The proposed techniques increase the generalization power of the similarity model and of the associated classifier, having accuracies of 98.5% on MaFaulDa and 98.9% on CWRU database. These results indicate that the proposed approach based on SBM is worth further investigation.Este trabalho apresenta um sistema de manutenção preditiva para diagnóstico automático de falhas em máquinas. O sistema proposto, baseado em uma técnica denominada similarity-based modeling (SBM), provê informações sobre o estado atual do equipamento (grau de anomalia), e retorna um conjunto de amostras representativas que pode ser utilizado para descrever o estado atual de forma esparsa, permitindo a um operador avaliar a melhor decisão a ser tomada. O sistema é modular e agnóstico aos dados, permitindo que seja utilizado em variados equipamentos e dados com pequenas modificações. As principais contribuições deste trabalho são: o estudo abrangente da proposta do classificador SBM multi-classe e o seu uso em diferentes bases de dados, seja como um classificador ou auxiliando outros classificadores comumente usados; novos métodos para a seleção de amostras representativas para os modelos SBM; o uso de novas funções de similaridade; e um serviço de detecção de falhas pronto para ser utilizado em produção. Essas contribuições atingiram o objetivo de melhorar o desempenho dos modelos SBM em cenários de classificação de falhas e reduziram sua complexidade computacional. O sistema proposto foi avaliado em três bases de dados, atingindo desempenho igual ou superior ao desempenho de trabalhos anteriores nas mesmas bases. Comparações com outros métodos são apresentadas para a recém-desenvolvida Machinery Fault Database (MaFaulDa) e para a base de dados da Case Western Reserve University (CWRU). As técnicas propostas melhoraram a capacidade de generalização dos modelos de similaridade e do classificador final, atingindo acurácias de 98.5% na MaFaulDa e 98.9% na base de dados CWRU. Esses resultados apontam que a abordagem proposta baseada na técnica SBM tem potencial para ser investigada em mais profundidade

MACHINERY ANOMALY DETECTION UNDER INDETERMINATE OPERATING CONDITIONS

Anomaly detection is a critical task in system health monitoring. Current practice of anomaly detection in machinery systems is still unsatisfactory. One issue is with the use of features. Some features are insensitive to the change of health, and some are redundant with each other. These insensitive and redundant features in the data mislead the detection. Another issue is from the influence of operating conditions, where a change in operating conditions can be mistakenly detected as an anomalous state of the system. Operating conditions are usually changing, and they may not be readily identified. They contribute to false positive detection either from non-predictive features driven by operating conditions, or from influencing predictive features. This dissertation contributes to the reduction of false detection by developing methods to select predictive features and use them to span a space for anomaly detection under indeterminate operating conditions. Available feature selection methods fail to provide consistent results when some features are correlated. A method was developed in this dissertation to explore the correlation structure of features and group correlated features into the same clusters. A representative feature from each cluster is selected to form a non-correlated set of features, where an optimized subset of predictive features is selected. After feature selection, the influence of operating conditions through non-predictive variables are removed. To remove the influence on predictive features, a clustering-based anomaly detection method is developed. Observations are collected when the system is healthy, and these observations are grouped into clusters corresponding to the states of operating conditions with automatic estimation of clustering parameters. Anomalies are detected if the test data are not members of the clusters. Correct partitioning of clusters is an open challenge due to the lack of research on the clustering of the machinery health monitoring data. This dissertation uses unimodality of the data as a criterion for clustering validation, and a unimodality-based clustering method is developed. Methods of this dissertation were evaluated by simulated data, benchmark data, experimental study and field data. These methods provide consistent results and outperform representatives of available methods. Although the focus of this dissertation is on the application of machinery systems, the methods developed in this dissertation can be adapted for other application scenarios for anomaly detection, feature selection, and clustering

Maximizing Model Generalization for Machine Condition Monitoring with Self-Supervised Learning and Federated Learning

Deep Learning (DL) can diagnose faults and assess machine health from raw condition monitoring data without manually designed statistical features. However, practical manufacturing applications remain extremely difficult for existing DL methods. Machine data is often unlabeled and from very few health conditions (e.g., only normal operating data). Furthermore, models often encounter shifts in domain as process parameters change and new categories of faults emerge. Traditional supervised learning may struggle to learn compact, discriminative representations that generalize to these unseen target domains since it depends on having plentiful classes to partition the feature space with decision boundaries. Transfer Learning (TL) with domain adaptation attempts to adapt these models to unlabeled target domains but assumes similar underlying structure that may not be present if new faults emerge. This study proposes focusing on maximizing the feature generality on the source domain and applying TL via weight transfer to copy the model to the target domain. Specifically, Self-Supervised Learning (SSL) with Barlow Twins may produce more discriminative features for monitoring health condition than supervised learning by focusing on semantic properties of the data. Furthermore, Federated Learning (FL) for distributed training may also improve generalization by efficiently expanding the effective size and diversity of training data by sharing information across multiple client machines. Results show that Barlow Twins outperforms supervised learning in an unlabeled target domain with emerging motor faults when the source training data contains very few distinct categories. Incorporating FL may also provide a slight advantage by diffusing knowledge of health conditions between machines

A Study on Comparison of Classification Algorithms for Pump Failure Prediction

The reliability of pumps can be compromised by faults, impacting their functionality. Detecting these faults is crucial, and many studies have utilized motor current signals for this purpose. However, as pumps are rotational equipped, vibrations also play a vital role in fault identification. Rising pump failures have led to increased maintenance costs and unavailability, emphasizing the need for cost-effective and dependable machinery operation. This study addresses the imperative challenge of defect classification through the lens of predictive modeling. With a problem statement centered on achieving accurate and efficient identification of defects, this study’s objective is to evaluate the performance of five distinct algorithms: Fine Decision Tree, Medium Decision Tree, Bagged Trees (Ensemble), RUS-Boosted Trees, and Boosted Trees. Leveraging a comprehensive dataset, the study meticulously trained and tested each model, analyzing training accuracy, test accuracy, and Area Under the Curve (AUC) metrics. The results showcase the supremacy of the Fine Decision Tree (91.2% training accuracy, 74% test accuracy, AUC 0.80), the robustness of the Ensemble approach (Bagged Trees with 94.9% training accuracy, 99.9% test accuracy, and AUC 1.00), and the competitiveness of Boosted Trees (89.4% training accuracy, 72.2% test accuracy, AUC 0.79) in defect classification. Notably, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and k-Nearest Neighbors (KNN) exhibited comparatively lower performance. Our study contributes valuable insights into the efficacy of these algorithms, guiding practitioners toward optimal model selection for defect classification scenarios. This research lays a foundation for enhanced decision-making in quality control and predictive maintenance, fostering advancements in the realm of defect prediction and classification

Anomaly Detection of Smart Meter Data

Presently, households and buildings use almost one-third of total energy consumption among all the power consumption sources. This trend is continuing to rise as more and more buildings install smart meter sensors and connect to the Smart Grid. Smart Grid uses sensors and ICT technologies to achieve an uninterrupted power supply and minimize power wastage. Abnormalities in sensors and faults lead to power wastage. Along with that studying the consumption pattern of a building can lead to a substantial reduction in power wastage which can save millions of dollars. According to studies, 20\% of energy consumed by buildings are wasted due to the above factors. In this work, we propose an anomaly detection approach for detecting anomalies in the power consumption of smart meter data from an open dataset of 10 houses from Ausgrid Corporation Australia. Since the power consumption may be affected by various factors such as weather conditions during the year, it was necessary to search for a way to discover the anomalies, considering seasonal periods such as weather seasons, day/night and holidays. Consequently, the first part of this thesis is to identify the outliers and obtain data with labels (normal or anomalous). We use Facebook prophet algorithm along with power consumption domain knowledge to detect anomalies for two years of half-hour sampled data. After generating the dataset with anomaly labels, we proposed a method to classify future power consumptions as anomalous or normal. We use four different approaches using machine learning for classifying anomalies. We also measure the run-time of different classification algorithms. We are able to achieve a G-mean score of 97 per cent

Novel deep cross-domain framework for fault diagnosis or rotary machinery in prognostics and health management

Improving the reliability of engineered systems is a crucial problem in many applications in various engineering fields, such as aerospace, nuclear energy, and water declination industries. This requires efficient and effective system health monitoring methods, including processing and analyzing massive machinery data to detect anomalies and performing diagnosis and prognosis. In recent years, deep learning has been a fast-growing field and has shown promising results for Prognostics and Health Management (PHM) in interpreting condition monitoring signals such as vibration, acoustic emission, and pressure due to its capacity to mine complex representations from raw data. This doctoral research provides a systematic review of state-of-the-art deep learning-based PHM frameworks, an empirical analysis on bearing fault diagnosis benchmarks, and a novel multi-source domain adaptation framework. It emphasizes the most recent trends within the field and presents the benefits and potentials of state-of-the-art deep neural networks for system health management. Besides, the limitations and challenges of the existing technologies are discussed, which leads to opportunities for future research. The empirical study of the benchmarks highlights the evaluation results of the existing models on bearing fault diagnosis benchmark datasets in terms of various performance metrics such as accuracy and training time. The result of the study is very important for comparing or testing new models. A novel multi-source domain adaptation framework for fault diagnosis of rotary machinery is also proposed, which aligns the domains in both feature-level and task-level. The proposed framework transfers the knowledge from multiple labeled source domains into a single unlabeled target domain by reducing the feature distribution discrepancy between the target domain and each source domain. Besides, the model can be easily reduced to a single-source domain adaptation problem. Also, the model can be readily updated to unsupervised domain adaptation problems in other fields such as image classification and image segmentation. Further, the proposed model is modified with a novel conditional weighting mechanism that aligns the class-conditional probability of the domains and reduces the effect of irrelevant source domain which is a critical issue in multi-source domain adaptation algorithms. The experimental verification results show the superiority of the proposed framework over state-of-the-art multi-source domain-adaptation models