Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network

A nuclear power plant (NPP) consists of an enormous number of components with complex interconnections. Various techniques to detect sensor errors have been developed to monitor the state of the sensors during normal NPP operation, but not for emergency situations. In an emergency situation with a reactor trip, all the plant parameters undergo drastic changes following the sudden decrease in core reactivity. In this paper, a machine learning model adopting a consistency index is suggested for sensor error detection during NPP emergency situations. The proposed consistency index refers to the soundness of the sensors based on their measurement accuracy. The application of consistency index labeling makes it possible to detect sensor error immediately and specify the particular sensor where the error occurred. From a compact nuclear simulator, selected plant parameters were extracted during typical emergency situations, and artificial sensor errors were injected into the raw data. The trained system successfully generated output that gave both sensor error states and error-free states

A review on predictive maintenance technique for nuclear reactor cooling system using machine learning and augmented reality

Reactor TRIGA PUSPATI (RTP) is the only research nuclear reactor in Malaysia. Maintenance of RTP is crucial which affects its safety and reliability. Currently, RTP maintenance strategies used corrective and preventative which involved many sensors and equipment conditions. The existing preventive maintenance method takes a longer time to complete the entire system’s maintenance inspection. This study has investigated new predictive maintenance techniques for developing RTP predictive maintenance for primary cooling systems using machine learning (ML) and augmented reality (AR). Fifty papers from recent referred publications in the nuclear areas were reviewed and compared. Detailed comparison of ML techniques, parameters involved in the coolant system and AR design techniques were done. Multiclass support vector machines (SVMs), artificial neural network (ANN), long short-term memory (LSTM), feed forward back propagation (FFBP), graph neural networks-feed forward back propagation (GNN-FFBP) and ANN were used for the machine learning techniques for the nuclear reactor. Temperature, water flow, and water pressure were crucial parameters used in monitoring a nuclear reactor. Image marker-based techniques were mainly used by smart glass view and handheld devices. A switch knob with handle switch, pipe valve and machine feature were used for object detection in AR markerless technique. This study is significant and found seven recent papers closely related to the development of predictive maintenance for a research nuclear reactor in Malaysia

A constraint-based random search algorithm for optimizing neural network architectures and ensemble construction in detecting loss of coolant accidents in nuclear power plants

© 2018 IEEE. One major accident of a nuclear power plant (NPP) is the loss of a coolant accident (LOCA) which is caused by a large break in an inlet header (IH) of a nuclear reactor. This work proposes a constraint-based random search algorithm for optimizing neural network (NN) architectures and ensemble construction in three stages for detecting the break size of an IH of a NPP. In stage one, a number of 2-hidden layer, 3-hidden layer and 4-hiddden layer network architectures are created using a proposed constraint satisfaction algorithm. Then, an optimised 2-hidden layer network, an optimised 3-hidden layer network and an optimised 4-hidden layer network are chosen from these architectures by training and testing them on a transient dataset of IHs and a linear interpolation dataset. In stage two, the optimised 2-hidden layer network, the optimised 3-hidden layer network and the optimised 4-hidden layer network are trained and tested iteratively 200 times on the transient dataset to further improve their performance. In stage three, the optimised 2-hidden layer network, the optimised 3-hidden layer network and the optimised 4-hidden layer network are combined into a neural network ensemble (NNE) using a weighted meaning approach. The results show that the NNE outperformed the individual optimised neural networks in detecting the break size of an IH

Representation Learning based and Interpretable Reactor System Diagnosis Using Denoising Padded Autoencoder

With the mass construction of Gen III nuclear reactors, it is a popular trend to use deep learning (DL) techniques for fast and effective diagnosis of possible accidents. To overcome the common problems of previous work in diagnosing reactor accidents using deep learning theory, this paper proposes a diagnostic process that ensures robustness to noisy and crippled data and is interpretable. First, a novel Denoising Padded Autoencoder (DPAE) is proposed for representation extraction of monitoring data, with representation extractor still effective on disturbed data with signal-to-noise ratios up to 25.0 and monitoring data missing up to 40.0%. Secondly, a diagnostic framework using DPAE encoder for extraction of representations followed by shallow statistical learning algorithms is proposed, and such stepwise diagnostic approach is tested on disturbed datasets with 41.8% and 80.8% higher classification and regression task evaluation metrics, in comparison with the end-to-end diagnostic approaches. Finally, a hierarchical interpretation algorithm using SHAP and feature ablation is presented to analyze the importance of the input monitoring parameters and validate the effectiveness of the high importance parameters. The outcomes of this study provide a referential method for building robust and interpretable intelligent reactor anomaly diagnosis systems in scenarios with high safety requirements

Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Data-driven machine learning (DDML) methods for the fault diagnosis and detection (FDD) in the nuclear power plant (NPP) are of emerging interest in the recent years. However, there still lacks research on comprehensive reviewing the state-of-the-art progress on the DDML for the FDD in the NPP. In this review, the classifications, principles, and characteristics of the DDML are firstly introduced, which include the supervised learning type, unsupervised learning type, and so on. Then, the latest applications of the DDML for the FDD, which consist of the reactor system, reactor component, and reactor condition monitoring are illustrated, which can better predict the NPP behaviors. Lastly, the future development of the DDML for the FDD in the NPP is concluded

Using artificial intelligence to detect human errors in nuclear power plants: A case in operation and maintenance

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems

A constraint-based genetic algorithm for optimizing neural network architectures for detection of loss of coolant accidents of nuclear power plants

© 2018 Elsevier B.V. The loss of coolant accident (LOCA) of a nuclear power plant (NPP) is a severe accident in the nuclear energy industry. Nowadays, neural networks have been trained on nuclear simulation transient datasets to detect LOCA. This paper proposes a constraint-based genetic algorithm (GA) to find optimised 2-hidden layer network architectures for detecting LOCA of a NPP. The GA uses a proposed constraint satisfaction algorithm called random walk heuristic to create an initial population of neural network architectures of high performance. At each generation, the GA population is split into a sub-population of feature subsets and a sub-population of 2-hidden layer architectures to breed offspring from each sub-population independently in order to generate a wide variety of network architectures. During breeding 2-hidden layer architectures, a constraint-based nearest neighbor search algorithm is proposed to find the nearest neighbors of the offspring population generated by mutation. The results showed that for LOCA detection, the GA-optimised network outperformed a random search, an exhaustive search and a RBF kernel support vector regression (SVR) in terms of generalization performance. For the skillcraft dataset of the UCI machine learning repository, the GA-optimised network has a similar performance to the RBF kernel SVR and outperformed the other approaches

Application of Artificial Intelligence in Detection and Mitigation of Human Factor Errors in Nuclear Power Plants: A Review

Human factors and ergonomics have played an essential role in increasing the safety and performance of operators in the nuclear energy industry. In this critical review, we examine how artificial intelligence (AI) technologies can be leveraged to mitigate human errors, thereby improving the safety and performance of operators in nuclear power plants (NPPs). First, we discuss the various causes of human errors in NPPs. Next, we examine the ways in which AI has been introduced to and incorporated into different types of operator support systems to mitigate these human errors. We specifically examine (1) operator support systems, including decision support systems, (2) sensor fault detection systems, (3) operation validation systems, (4) operator monitoring systems, (5) autonomous control systems, (6) predictive maintenance systems, (7) automated text analysis systems, and (8) safety assessment systems. Finally, we provide some of the shortcomings of the existing AI technologies and discuss the challenges still ahead for their further adoption and implementation to provide future research directions