A distributed sensor-fault detection and diagnosis framework using machine learning

Jan, Sana Ullah; Lee, Young Doo; Koo, Insoo

doi:10.1016/j.ins.2020.08.068

Cited by 127 publications

(55 citation statements)

References 20 publications

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“…We present a time-series prediction model for anomaly detection, and this kind of problem can be found in many industrial scenarios. [30][31][32] Since the model proposed in this article performs well in WT blades icing detection, it is conceivable that our proposed model should also be applicable to those problems, which needs to be further verified in the future.…”

Section: Discussionmentioning

confidence: 98%

A PCC-Ensemble-TCN model for wind turbine icing detection using class-imbalanced and label-missing SCADA data

Ding

Wang

Zhang

et al. 2021

International Journal of Distributed Sensor Networks

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Blade icing problems are ubiquitous for wind turbines located in cold climate zones. Data-driven indirect icing detection methods based on supervisory control and data acquisition system have shown strong potential recently. However, the supervisory control and data acquisition data is annotated through manual observation, which will cause the data between normal condition and icing condition to be unlabeled. In addition, the amount of normal data is far more than icing data. The above two issues restrict the performance of most current data-driven models. In order to solve the label missing problem, this article proposes a Pearson correlation coefficient–based algorithm for measuring the degree of blade icing, which calculates the similarity between the unlabeled data and the icing data as its label. Aiming at the class-imbalance problem, this article constructs multiple class-balanced subsets from the original dataset by under-sampling the normal data. Temporal convolutional networks are trained to extract features and make predictions on each subset. The final prediction result is obtained by ensembling the prediction results of all temporal convolutional network models. The proposed model is validated using the actual supervisory control and data acquisition data collected from a wind farm in northern China, and the results indicate that ensuring the consecutiveness and class-balance of the data are quite advantageous for improving the detection accuracy.

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Section: Discussionmentioning

confidence: 98%

A PCC-Ensemble-TCN model for wind turbine icing detection using class-imbalanced and label-missing SCADA data

Ding

Wang

Zhang

et al. 2021

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…The SVM algorithm works based on the concept of statistical learning theory, and in multi-class classification problems, an SVM is one of the best performing machine learning algorithms. Many researchers have utilized and suggested SVMs for distinct real-life applications such as a system fault diagnosis or abnormality detection and the monitoring of patients [ [160] , [161] , [162] , [163] ]. The SVM algorithm generates a hyperplane or linear line as a decision boundary to separate different types of data points for classification tasks.…”

Section: Machine Learning For Detection Of Covid-19 Symptomsmentioning

confidence: 99%

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

Saeed

Shah

Ahmad

et al. 2022

Journal of Pharmaceutical Analysis

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The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), which caused the COVID-19 pandemic, has affected more than 250 million people worldwide. With the recent rise of a new Delta variant, the efficacy of the vaccines has become an important question. The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions, particularly for healthcare workers. In this review paper, we discuss the current literature on invasive/contact and non-invasive/non-contact technologies (including Wi-Fi, RADAR, and software-defined radio) that have been effectively used to detect, diagnose, and monitor human activities and COVID-19 related symptoms, such as irregular respiration. In addition, we focused on cutting-edge machine learning algorithms (such as generative adversarial networks, random forest, multilayer perceptron, support vector machine, extremely randomized trees, and k-nearest neighbors) and their essential role in intelligent healthcare systems. Furthermore, this study highlights the limitations related to non-invasive techniques and prospective research directions.

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“…Furthermore, the paper [27] presents a fuzzy logic-based intrusion detection model specifically designed for protecting IoT nodes with the MQTT protocol against DoS attacks. Although fuzzy logic has shown its efficiency for different applications, including sensor fault detection in IoT [28]; however, its high complexity with an increase in the input dimension limits its potential in intrusion detection for IoT where huge data are transferred continuously. In addition, more advanced and complex attacks have been left untouched in paper [27] that raises questions over the efficiency of the proposed model for detecting other types of attacks.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT

Khan

Jan

et al. 2021

Sensors

Self Cite

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A large number of smart devices in Internet of Things (IoT) environments communicate via different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely used publish–subscribe-based protocol for the communication of sensor or event data. The publish–subscribe strategy makes it more attractive for intruders and thus increases the number of possible attacks over MQTT. In this paper, we proposed a Deep Neural Network (DNN) for intrusion detection in the MQTT-based protocol and also compared its performance with other traditional machine learning (ML) algorithms, such as a Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbour (kNN), Decision Tree (DT), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). The performance is proved using two different publicly available datasets, including (1) MQTT-IoT-IDS2020 and (2) a dataset with three different types of attacks, such as Man in the Middle (MitM), Intrusion in the network, and Denial of Services (DoS). The MQTT-IoT-IDS2020 contains three abstract-level features, including Uni-Flow, Bi-Flow, and Packet-Flow. The results for the first dataset and binary classification show that the DNN-based model achieved 99.92%, 99.75%, and 94.94% accuracies for Uni-flow, Bi-flow, and Packet-flow, respectively. However, in the case of multi-label classification, these accuracies reduced to 97.08%, 98.12%, and 90.79%, respectively. On the other hand, the proposed DNN model attains the highest accuracy of 97.13% against LSTM and GRUs for the second dataset.

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A distributed sensor-fault detection and diagnosis framework using machine learning

Cited by 127 publications

References 20 publications

A PCC-Ensemble-TCN model for wind turbine icing detection using class-imbalanced and label-missing SCADA data

A PCC-Ensemble-TCN model for wind turbine icing detection using class-imbalanced and label-missing SCADA data

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT

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