Fault Diagnosis Techniques for Nuclear Power Plants: A Review from the Artificial Intelligence Perspective
Abstract
:1. Introduction
2. Fault Diagnosis Classification from the AI Perspective
2.1. Development History of AI
2.2. AI-Based Fault Diagnosis Classification
3. Knowledge-Driven Fault Diagnosis Methods
3.1. Fault Diagnosis Methods Based on If–Then
3.2. Fault Diagnosis Methods Based on New Theories
3.2.1. Signed Directed Graphs
3.2.2. Bayesian Networks
3.2.3. Dynamic Uncertain Causality Graphs
3.3. Summary of Knowledge-Driven Fault Diagnosis Methods
4. Data-Driven Fault Diagnosis Methods
4.1. Fault Diagnosis Methods Based on Single Algorithms
4.1.1. Artificial Neural Network
4.1.2. Support Vector Machine
4.1.3. Decision Tree
4.1.4. Principal Component Analysis
4.1.5. Clustering
4.1.6. Comparison of Single Algorithms
4.2. Fault Diagnosis Methods Based on Hybrid Algorithms
Topic | Reference | Algorithm | Diagnosis Object | Highlights |
---|---|---|---|---|
ANN+X | [171] | WPT and LSTM | NPP converter | The operation mode of the power system is analyzed in depth when a failure occurs. |
[174] | WPT and ANN | NPP system faults | Disturbing perturbations in training set are reduced by WPT. | |
[173] | RNN, WOLP, and ARTD | NPP system faults | The paper improved the practical applicability and scalability of diagnosis systems to real processes and machinery. | |
[69] | CN and DBN | NPP system faults | Correlation analysis is used for dimensionality reduction. | |
[69] | FNN and MSIF | NPP system faults | The system is able to achieve a single-fault and some multiple-fault diagnoses. | |
[172] | CGRN and EPSO | NPP system faults | The technical framework of digital twin model, deep learning, and heuristic algorithm is established. | |
[158] | BPNN and RBF | NPP system faults | First adopts the BP-ANN for a rapid group pre-diagnosis, then uses the RBF ANNs to verify the results. | |
[159] | FNN and RBF | NPP system faults | A combination of NN and fuzzy theory is proposed. | |
[160] | ANN and VF | NPP system faults | Many neural networks diagnose the same fault, and the result is obtained by voting fusion. | |
[161] | ANN and LF | NPP system faults | The logical fusion method was employed to fuse the diagnosing results of different neural networks. | |
[162] | ELM and AdaBoost | NPP system faults | The paper verifies the feasibility and validity of the ensemble learning method for fault diagnosis. | |
[168] | ANN and SW | NPP system faults | Adaptive feature learning using a sliding window strategy. | |
[67] | ANN and MFM | NPP system faults | MFM can provide explanations on how the malfunctions originated and propagated to the current situation. | |
[169] | ANN and FL | Small break loss of coolant accident | High sensitivity and superior prediction capabilities. | |
SVM + X | [166] | LS-SVM and GPR | NPP system faults | PSO is applied to find the optimal GPR model to better assess the severity of the fault. |
[170] | SVM and RS | NPP system faults | The uncertain data is reduced based on RS theory. | |
[73] | SVM and EPSO | NPP system faults | The optimization of hyperparameters of SVM by improved PSO is compared with others. | |
[167] | SVM and GA | Physical parameter | A new GASVM is proposed to classify multiple faults. | |
PCA+ X | [72] | PCA and MFM | NPP system faults | Mechanism simulation is implemented to provide training data with fault signatures. |
DT + X | [145] | DT and RS | NPP system faults | A parameter reduction method based on neighborhood rough sets was proposed. |
Clustering +X | [176] | Clustering and FBS | NPP turbine | The paper developed a framework of unsupervised classification of transients. |
Comparison | [164] | PCA and (SVM, KNN, LDA, DT, and LR) | NPP system fault s | The state information imaging is used to construct the different condition images. |
[177] | PCA and SVM | NPP system fault s | A three-layer fault classification model was established to diagnose the fault type, location, and degree. | |
[178] | PCA and ANN | SG tube; RCS pump | Radial basis network provides better prediction and diagnoses the faults faster than Elman neural network. | |
[165] | ANN, D-S, and SDG | NPP system faults | To the different diagnostic object, we adopted the different diagnostic methods. | |
[179] | ANN and SVM | Feed-water pump | A comparative analysis of ANN and SVM was performed. | |
[147] | PCA and ANN | NPP system faults | The method utilizes the (PCA) technique to reduce the problem dimension. |
4.3. Summary of Data-Driven Fault Diagnosis Techniques
5. Results
6. Conclusions and Future Directions
- The combination of data-driven and knowledge-driven fault methods. At present, their respective theories have become mature, but there is still a lack of theoretical research integrating their advantages. In the context of the digital transformation of NPPs, data resources can be easier to obtain, and knowledge resources can be obtained from NPP’s Deterministic Security Analysis Report (DSAR) and Probabilistic Risk Analysis Report (PRAR). DSAR and PRAR contain detailed knowledge descriptions of fault mechanisms, which can be used to build knowledge-driven models such as Bayesian network models, and data resources of nuclear power plants can help build data-driven models such as neural networks. For example, when strong interpretability of diagnostic results is required, it is necessary to consider incorporating knowledge into the model. We have explored the combination of knowledge-driven and data-driven methods by using PRAR and DASR to build Bayesian networks for fault type diagnoses as well as using data to build neural networks for fault severity diagnoses [181]. Studying new technologies to make full use of these two resources is a field worthy of research in the future.
- On-demand system fault diagnosis. In practice, Zhao et al. classify the types of NPP system-level faults into two types, operational faults and on-demand faults [106]. An operational fault is defined as an unexpected abnormal behavior during the operation of a nuclear power plant, such as a rupture of primary coolant pipes and a rupture of heat transfer pipes of steam generators. An on-demand fault is defined as the fault of the response system to perform a predetermined function after an operational fault occurs, such as high pressure in the primary circuit, which makes the pressure of the regulator higher than the set value and prevents the relief of the safety valve from opening. At present, there is little research on on-demand faults, but it is of great significance to nuclear safety.
- Introduction of digital twin technology. Digital twin refers to the simulation process of integrating multi-disciplinary, multi-physical quantity, multi-scale, and multi-probability technology by making full use of the data such as the physical model, sensor update, and operation history and finally completing the mapping in the virtual space, to reflect the whole life cycle process of the corresponding physical equipment [182]. At present, most studies are based on data from NPP simulators. These simulators are not high-fidelity models, which means that the application of the diagnostic model in actual NPP has great uncertainty. The digital twin technology can accurately simulate the actual equipment. The reliability and safety of its practical application will be greatly improved based on this technology. Nguyen et al. studied a digital twin approach to system-level fault detection and diagnosis for thermal hydraulic systems [183]. Therefore, it is of great significance to establish the digital twin model of NPPs.
- More detailed diagnostic hierarchy. As shown in Figure 15, most of the current studies focus on system-level faults in NPPs, while there are few studies on human factor faults, sensor faults, control room faults, network security faults, etc.
- Construction of the generalized model. In many cases, the fault diagnosis models we construct are only applicable to specific tasks. When encountering a new task, it is an important challenge to reuse the previous data and experiences. Transfer learning provides a possible solution [184]. Some related studies are in progress [185,186,187,188].
- Interdisciplinary cooperation. Fault diagnosis is a comprehensive technology involving multiple disciplines (modern control theory, mathematical statistics, signal processing, pattern recognition, artificial intelligence, etc.). Most of the current fault diagnosis research is limited intra-disciplinary exploration. An optimal fault diagnosis research should gather multidisciplinary knowledge as a way to drive the fault diagnosis technology in a more efficient, sensitive, and intelligent direction. Therefore, a cross-disciplinary perspective is crucial for researchers.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Rule Number | If | Then |
---|---|---|
1 | (PCS pressure decreasing) (HPIS on) | PCS integrity challenged |
2 | PCS temperature increasing | PCS–SCS heat transfer inadequate |
3 | PCS temperature increasing | SG inventory inadequate |
4 | (High containment radiation) (High containment pressure) | Containment integrity challenged |
5 | (PCS–SCS heat transfer inadequate) (Low feedwater flow) | Accident is LOFW |
6 | (SG inventory inadequate) (Low feedwater flow) | Accident is LOFW |
7 | (PCS integrity challenged) (Low feedwater flow) | Accident is LOCA |
8 | (PCS integrity challenged) (SG level increasing) | Accident is SGTR |
9 | (SG inventory inadequate) (High steam flow) | Accident is MSLB |
0.5 | 0.90 | 0.05 | 0.95 | 0.05 | 0.90 | 0.05 | ||||
0.5 | 0.10 | 0.95 | 0.05 | 0.95 | 0.10 | 0.95 |
0.90 | 0.05 | 0.95 | 0.10 | |
0.10 | 0.95 | 0.05 | 0.90 |
Methods | Type | Advantages | Disadvantages |
---|---|---|---|
Artificial neural network | Supervised learning | Various networks; strong non-linear fitting ability. | Large amount of data is needed. |
Support vector machine | Supervised learning | High accuracy; suitable for small samples; avoid overfitting. | Difficult to train large-scale data; sensitive to kernel function selection. |
Decision tree | Supervised learning | Strong explanatory; acceptable for a sample with incomplete information. | Easy overfitting; easy to ignore attribute relations in data. |
Principal component analysis | Supervised learning | Data dimension reduction. | Poor interpretability. |
Clustering | Unsupervised learning | Diagnose untrained faults. | Sensitive to K value selection. |
Characteristic | Ease of Modeling | Interpretability | Robustness | Reasoning Efficiency |
---|---|---|---|---|
Knowledge-driven | no | yes | yes | no |
Data-driven | yes | no | no | yes |
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Qi, B.; Liang, J.; Tong, J. Fault Diagnosis Techniques for Nuclear Power Plants: A Review from the Artificial Intelligence Perspective. Energies 2023, 16, 1850. https://doi.org/10.3390/en16041850
Qi B, Liang J, Tong J. Fault Diagnosis Techniques for Nuclear Power Plants: A Review from the Artificial Intelligence Perspective. Energies. 2023; 16(4):1850. https://doi.org/10.3390/en16041850
Chicago/Turabian StyleQi, Ben, Jingang Liang, and Jiejuan Tong. 2023. "Fault Diagnosis Techniques for Nuclear Power Plants: A Review from the Artificial Intelligence Perspective" Energies 16, no. 4: 1850. https://doi.org/10.3390/en16041850
APA StyleQi, B., Liang, J., & Tong, J. (2023). Fault Diagnosis Techniques for Nuclear Power Plants: A Review from the Artificial Intelligence Perspective. Energies, 16(4), 1850. https://doi.org/10.3390/en16041850