Fault Detection and Isolation of a Pressurized Water Reactor Based on Neural Network and K-Nearest Neighbor

Naimi, Amine; Deng, Jiamei; Shimjith, S. R.; Arul, A. John

doi:10.1109/access.2022.3149772

Cited by 25 publications

(14 citation statements)

References 33 publications

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“…In a nuclear power plant scenario, Naimi et al [ 10 ] propose fault detection and diagnosis based on neural networks and a KNN algorithm applied to a pressurized water reactor. First, the neural network performs detection.…”

Section: Related Workmentioning

confidence: 99%

“…However, an industrial scenario requires a sensitive time application to deal with faults, but the SVM demands higher computation time. Naimi et al [ 10 ] also do not consider the necessary time to apply a two-phase solution. Additionally, how to react after the fault has been detected is an important aspect, but countermeasures are often considered only from the security perspective.…”

Section: Related Workmentioning

confidence: 99%

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Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things

Santo

Immich

Dalmazo

et al. 2023

Sensors

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Industrial production and manufacturing systems require automation, reliability, as well as low-latency intelligent control. Industrial Internet of Things (IIoT) is an emerging paradigm that enables precise, low latency, intelligent computing, supported by cutting-edge technology such as edge computing and machine learning. IIoT provides some of the essential building blocks to drive manufacturing systems to the next level of productivity, efficiency, and safety. Hardware failures and faults in IIoT are critical challenges to be faced. These anomalies can cause accidents and financial loss, affect productivity, and mobilize staff by producing false alarms. In this context, this article proposes a framework called Detection and Alert State for Industrial Internet of Things Faults (DASIF). The DASIF framework applies edge computing to execute highly precise and low latency machine learning models to detect industrial IoT faults and autonomously enforce an adaptive communication policy, triggering a state of alert in case of fault detection. The state of alert is a pre-stage countermeasure where the network increases communication reliability by using data replication combined with multiple-path communication. When the system is under alert, it can process a fine-grained inspection of the data for efficient decison-making. DASIF performance was obtained considering a simulation of the IIoT network and a real petrochemical dataset.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things

Santo

Immich

Dalmazo

et al. 2023

Sensors

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“…Artificial neural networks (ANNs) have also been used for FDD in NPPs because they provide outstanding performance in estimating nonlinear systems [11], [12]. An ANN-based FDD method using a K-nearest neighbor (KNN) for the sensor and actuator FDD of an NPP was proposed in [13]. Shallow neural networks were used to detect the faults, and VOLUME 4, 2016 a KNN algorithm was employed to classify the faults.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of the authors' knowledge, only a few FDD approaches can detect and isolate faults in sensors and actuators in NPPs. The FDD approach proposed in [13] can detect and diagnose the presence of sensor and actuator faults; however, faults only concentrate on the reactor subsystem. As an extension of [13], this study evaluates actuator faults and sensor faults throughout the entire PWR NPP using a practical machine learning-based approach.…”

Section: Introductionmentioning

confidence: 99%

“…The FDD approach proposed in [13] can detect and diagnose the presence of sensor and actuator faults; however, faults only concentrate on the reactor subsystem. As an extension of [13], this study evaluates actuator faults and sensor faults throughout the entire PWR NPP using a practical machine learning-based approach. The proposed FDD approach was tested on fauts that affect the sensors and actuators of the reactor core, steam generator, pressurizer pressure and level, and turbine-speed system.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Fault Diagnosis for a PWR Nuclear Power Plant

et al. 2022

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Fault detection and diagnosis (FDD) systems can reduce high costs and energy consumption. This paper presents a machine learning-based fault detection and diagnosis (FDD) technique for actuators and sensors in a pressurized water reactor (PWR). In the proposed FDD framework, faults are first detected using a shallow neural network. Second, fault diagnosis is performed using 15 different classifiers provided in the MATLAB Classification Learner toolbox, including support vector machine (SVM), Knearest neighbor (KNN), and ensemble. Several classifiers were found to provide superior classification performance, including medium KNN, cubic KNN, cosine KNN, weighted KNN, fine Gaussian SVM, quadratic SVM, medium Gaussian SVM, coarse Gaussian, bagged trees, and subspace KNN. The accuracy of the FDD approach was demonstrated using a set of simulation results. INDEX TERMS Fault detection and diagnosis (FDD), nuclear reactor, machine learning, neural networkThis article has been accepted for publication in IEEE Access.

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Noise‐robust gas path fault detection and isolation for a power generation gas turbine based on deep residual compensation extreme learning machine

Nekoonam,

Montazeri‐Gh

2023

Energy Science & Engineering

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One of the major challenges facing fault diagnosis tools is their exposure to noise. The presence of noise may cause false alarms or the inability to detect a progressive fault in the early stages of its occurrence. Continuing previous efforts to address such a problem, in this paper, a noise‐robust diagnosis system for an industrial gas turbine is presented. The proposed structure employs a set of deep residual compensation extreme learning machines (DRCELMs). In this model, an optimal number of compensating blocks are trained to recover some of the lost useful information in the face of noise. Training and testing data required to develop the fault diagnosis model are generated by a performance model of the studied gas turbine. The t‐distributed stochastic neighbor embedding algorithm is employed for visualizing the gas path faults. Furthermore, the performance of the DRCELM is evaluated by comparing it with six other diagnosis models. The results indicate higher robustness of the DRCELM compared to other fault diagnosis systems. The proposed model presents a classification accuracy of >97% in noisy data and an accuracy of >98% in noise‐free data and combined data, while the average of fault positive rate and fault negative rate in noisy data is less than 2.5%.

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Fault Detection and Isolation of a Pressurized Water Reactor Based on Neural Network and K-Nearest Neighbor

Cited by 25 publications

References 33 publications

Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things

Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things

Machine Learning-Based Fault Diagnosis for a PWR Nuclear Power Plant

Noise‐robust gas path fault detection and isolation for a power generation gas turbine based on deep residual compensation extreme learning machine

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