Semi-Supervised Learning for Diagnosing Faults in Electromechanical Systems

Safe and reliable operation of the systems relies on the use of online condition monitoring and diagnostic systems that aim to take immediate actions upon the occurrence of a fault. Machine learning techniques are widely used for designing data-driven diagnostic models. The training procedure of a data-driven model usually requires a large amount of labeled data, which may not be always practical. This problem can be untangled by resorting to semi-supervised learning approaches, which enables the decision making procedure using only a few numbers of labeled samples coupled with a large number of unlabeled samples. Thus, it is crucial to conduct a critical study on the use of semi-supervised learning for the purpose of fault diagnosis. Another issue of concern is fault diagnosis in non-stationary environments, where data streams evolve over time, and as a result, model-based and most of the data-driven models are impractical. In this work, this has been addressed by means of an adaptive data-driven diagnostic model

Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms

The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the incorporation of transfer learning into federated learning, which overcomes fundamental constraints of primary federated learning, particularly in terms of security. This chapter performs a comprehensive survey on the intersection of federated and transfer learning from a security point of view. The main goal of this study is to uncover potential vulnerabilities and defense mechanisms that might compromise the privacy and performance of systems that use federated and transfer learning.Comment: Accepted for publication in edited book titled "Federated and Transfer Learning", Springer, Cha

Adversarial Learning on Incomplete and Imbalanced Medical Data for Robust Survival Prediction of Liver Transplant Patients

The scarcity of liver transplants necessitates prioritizing patients based on their health condition to minimize deaths on the waiting list. Recently, machine learning methods have gained popularity for automatizing liver transplant allocation systems, which enables prompt and suitable selection of recipients. Nevertheless, raw medical data often contain complexities such as missing values and class imbalance that reduce the reliability of the constructed model. This paper aims at eliminating the respective challenges to ensure the reliability of the decision-making process. To this aim, we first propose a novel deep learning method to simultaneously eliminate these challenges and predict the patients\u27 survival chance. Secondly, a hybrid framework is designed that contains three main modules for missing data imputation, class imbalance learning, and classification, each of which employing multiple advanced techniques for the given task. Furthermore, these two approaches are compared and evaluated using a real clinical case study. The experimental results indicate the robust and superior performance of the proposed deep learning method in terms of F-measure and area under the receiver operating characteristic curve (AUC)

Adversarial Learning on Incomplete and Imbalanced Medical Data for Robust Survival Prediction of Liver Transplant Patients

The scarcity of liver transplants necessitates prioritizing patients based on their health condition to minimize deaths on the waiting list. Recently, machine learning methods have gained popularity for automatizing liver transplant allocation systems, which enables prompt and suitable selection of recipients. Nevertheless, raw medical data often contain complexities such as missing values and class imbalance that reduce the reliability of the constructed model. This paper aims at eliminating the respective challenges to ensure the reliability of the decision-making process. To this aim, we first propose a novel deep learning method to simultaneously eliminate these challenges and predict the patients\u27 survival chance. Secondly, a hybrid framework is designed that contains three main modules for missing data imputation, class imbalance learning, and classification, each of which employing multiple advanced techniques for the given task. Furthermore, these two approaches are compared and evaluated using a real clinical case study. The experimental results indicate the robust and superior performance of the proposed deep learning method in terms of F-measure and area under the receiver operating characteristic curve (AUC)

Embedding Time-Series Features into Generative Adversarial Networks for Intrusion Detection in Internet of Things Networks

In recent years, Generative Adversarial Networks (GAN) have become powerful industrial tools to facilitate various learning tasks, including anomaly detection. This chapter studies a number of GAN architectures used for anomaly detection in the data stream. Moreover, a novel approach is proposed for embedding the dynamic characteristics of the data stream into the GAN-based detector structures. In this process, a GAN model is also proposed for efficient estimation of a confidence measure during the operation that reflects how well samples can be assigned to benign data. Furthermore, this chapter designs an intrusion detection system by developing a GAN-based anomaly detector. To do this, we study the effect of the proposed approach and the selected GAN-based approaches in detecting malicious intrusions in an Internet of Things (IoT) network. Experiments are evaluated in terms of false alarm and missed alarm detection rates. The obtained results indicate the effectiveness of the proposed GAN-based detection approach for the respective task

Detection of Malicious SCADA Communications via Multi-Subspace Feature Selection

Security maintenance of Supervisory Control and Data Acquisition (SCADA) systems has been a point of interest during recent years. Numerous research works have been dedicated to the design of intrusion detection systems for securing SCADA communications. Nevertheless, these data-driven techniques are usually dependant on the quality of the monitored data. In this work, we propose a novel feature selection approach, called MSFS, to tackle undesirable quality of data caused by feature redundancy. In contrast to most feature selection techniques, the proposed method models each class in a different subspace, where it is optimally discriminated. This has been accomplished by resorting to ensemble learning, which enables the usage of multiple feature sets in the same feature space. The proposed method is then utilized to perform intrusion detection in smaller subspaces, which brings about efficiency and accuracy. Moreover, a comparative study is performed on a number of advanced feature selection algorithms. Furthermore, a dataset obtained from the SCADA system of a gas pipeline is employed to enable a realistic simulation. The results indicate the proposed approach extensively improves the detection performance in terms of classification accuracy and standard deviation

Enhancing detection accuracy of cyber attacks through dimensionality reduction

The importance of cyber-security has led to long-standing endeavors dedicated to the design of intrusion detection systems (IDS). Nevertheless, the performance of these data-driven techniques is highly dependent on data quality. Incorporating dimensionality reduction techniques into a hybrid intrusion detection system, we aim to study the effect of dimensionality reduction on the performance of intrusion detection. By this mean, the efficiency of the intrusion detection systems is increased by processing a smaller feature space. Moreover, the reduced feature space also increases the detection accuracy, as redundant and meaningless features are removed in the new feature space. Furthermore, the intrinsic structure of the data is improved, that is different states of the system become more discriminant after dimensionality reduction. For this mean, various state-of-the-art dimensionality reduction techniques are selected. Then, a simulation is performed on a Supervisory Control and Data Acquisition (SCADA) system, which resembles a gas pipeline control system introduced by Morris et al. (2011). A comparative study is then performed to suggest the best dimensionality reduction algorithm in these experiments. The experiments indicate the general improvement of detection accuracy when dimensionality reduction techniques are combined with the IDS in terms of accuracy and standard deviation

A Novelty Detector and Extreme Verification Latency Model for Nonstationary Environments

Safe and reliable operation of systems relies on the use of online condition monitoring and diagnostic systems that aim to take immediate actions upon the occurrence of a fault. Model-based solutions are often not practical in nonstationary environments. Thus, the evolving data stream requires the data-driven model to be adaptive. In this paper, we propose a framework for the fault detection and classification that is accomplished on the data stream with both the gradual and abrupt drifts. The framework is only provided with prior information about the possible faults at the initial step; however, despite this, the framework can still detect the novel faults without receiving any update. Furthermore, an efficient fault classification algorithm is presented to maximize the efficiency of the proposed framework. Finally, the proposed framework is applied for diagnosing bearing defects in the induction motors to demonstrate its feasibility for industrial applications

Unsupervised concrete feature selection based on mutual information for diagnosing faults and cyber-attacks in power systems

Removing the redundant features from massive data collected from power systems is of paramount importance in improving the efficiency of data-driven diagnostic systems. This work proposes a novel concrete feature selection based on mutual information, called CFMI, for selecting proper features to enhance diagnosing faults and cyber-attacks in power systems. The proposed technique is then compared with various state-of-the-art techniques and a comprehensive study has been performed on the selected features. All techniques are evaluated with respect to simulated scenarios on IEEE 39-bus system and a Three-Bus Two-Line experimental setup. The attained results, on one hand, verify the superiority of the proposed CFMI technique over other techniques. On the other hand, the selected features from both setups denote that current and voltage features are more informative than other features for diagnostic systems. Furthermore, the results of the comprehensive study shows that those features collected from generation buses are of higher priority for diagnostic systems