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sensor anomaly detection for small modular reactors utilizing improved autoencoder

NUCLEAR ENGINEERING AND DESIGN 2024年 417卷

作者： Zhang, Bowen Zhu, Hao Cheng, Shouyu Ma, Haochen Harbin Engn Univ Fundamental Sci Nucl Safety & Simulat Technol Lab Harbin 150001 Peoples R China Res Inst Nucl Power Operat Beijing 430223 Peoples R China

The anomaly detection of widely distributed sensors in small modular reactors (SMRs) is critical to the safe operation of systems. Existing methods of sensor anomaly detection for transient conditions are less studied and have some shortcomings, necessitating further research in this context. Therefore, this paper presents an unsupervised deep-learning framework for a gated recurrent autoencoder with the Luong attentional mechanism and the residual connection, which is a multiple-input and multiple-output model. A single model can monitor SMR systems equipped with many sensors, and the training of the model does not require an anomaly label. The general threshold-setting method of the autoencoder for anomaly detection is also improved. The Bayesian estimation algorithm sets a dynamic threshold value in the residual evaluation stage. Finally, simulation tests are performed on experimental data sets, and the residual series obtained from the output of this framework are examined for anomalous sensors using Bayesian estimation methods. The mean squared error (MSE), that is, the average squared difference between the reconstructed value and the actual standard value, is used as the evaluation index. The experimental results demonstrate that the MSE of the improved autoencoder is smaller than that of the unimproved model in both transient and steady-state conditions. In addition, the dynamic threshold method reduces the false alarm rate of anomaly detection.

关键词： Small modular reactors (SMR) sensor anomaly detection Autoencoder Bayesian estimation

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sensor anomaly detection in Nuclear Power Plant Using Deep LSTM Denoising Autoencoder and Isolation Forest

IEEE SENSORS LETTERS

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IEEE sensorS LETTERS 2024年第12期8卷 1页

作者： Gandhimathinathan, A. Ananthakrishnan, C. G. Lavanya, R. Jehadeesan, R. Reddy, Pidapa Raghava Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Elect & Commun Engn Coimbatore 641112 India Indira Gandhi Ctr Atom Res Kalpakkam 603102 India

Industrial health monitoring is essential for managing and maintaining infrastructures in a process industry where the primary goals are reducing downtime, improving health, and ensuring safety performance. On the contrary, unplanned downtimes caused by regular maintenance often result in financial losses. This scenario calls for automated fault diagnosis that facilitates online health monitoring to predict faults before irreversible damage occurs. This letter proposes a deep learning approach based on long short-term memory denoising autoencoder (LSTM-DAE) combined with Isolation Forest (IF) for early detection of sensor anomalies in nuclear power plants. Residual signals from LSTM-DAE are fed to IF to generate anomaly scores for early fault detection. The proposed approach is validated using the dataset obtained from KALBR-SIM, a full scope operator training replica simulator, which replicates the Prototype Fast Breeder Reactor at Indira Gandhi Centre for Atomic Research. Results demonstrate that the proposed approach detects faults much earlier than the state-of-the-art approaches, with an accuracy of 98.2%.

关键词： sensors anomaly detection Training Long short term memory Noise reduction Forestry Temperature sensors sensor systems Data models Tuning sensor signal processing denoising autoencoder (AE) isolation forest (IF) long short-term memory (LSTM) nuclear power plant (NPP) sensor anomaly detection

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Data-Driven Remaining Useful Life Prediction Considering sensor anomaly detection and Data Recovery

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IEEE ACCESS 2019年 7卷 58336-58345页

作者： Liu, Liansheng Guo, Qing Liu, Datong Peng, Yu Harbin Inst Technol Sch Elect & Informat Engn Harbin 150080 Heilongjiang Peoples R China

Prognostics and health management (PHM) is being adopted more and more in the modern engineering systems. As one of the most important technologies in the PHM domain, remaining useful life (RUL) prediction has attracted much attention from the researchers in the scholar and industrial field. Although many methods have been proposed to improve the prediction result, the problem of sensor anomaly detection and data recovery has not been considered together. To achieve this object, the data-driven RUL prediction framework considering sensor anomaly detection and data recovery is proposed, which is expected to improve the performance of RUL prediction caused by sensor anomaly. The selected sensor data are first detected to decide whether they are anomalous. If the data of this selected sensor are normal, they are continuously adopted as the input of the RUL prediction algorithm. But, if the data are anomalous, they will be recovered by the related algorithm. The recovered data will be utilized as the input of the RUL prediction algorithm. In the proposed framework, mutual information, Kernel principal component analysis (KPCA), least square-support vector machine (LS-SVM), and Gaussian process regression (GPR) are utilized. Both simulation data and practical data are used to evaluate the performance of the proposed method. Compared with abandoning the anomalous sensor data, the recovered data can indeed help to enhance the RUL prediction result.

关键词： Remaining useful life mutual information sensor anomaly detection data recovery for prognostics

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sensor anomaly detection and Recovery in the Roll Dynamics of a Delta-Wing Aircraft via Monte Carlo and Maximum Likelihood Methods*

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IFAC-PapersOnLine 2017年第1期50卷 12791-12796页

作者： Deghat, Mohammad Lampiri, Evangelia CSIRO's Data61 Canberra Lab Australia Australia

This paper studies the problem of sensor anomaly detection, estimation and recovery for the roll dynamic model of a generic delta-wing aircraft. The proposed algorithm employs particle filtering and maximum likelihood methods to detect and estimate the anomaly. The estimated anomaly is then used to correct the sensor readings. It is assumed that both the system model and sensor outputs are corrupted by noise, which are not necessarily Gaussian. Simulation results are presented to show the performance of the proposed algorithm. © 2017

关键词： Delta wing aircraft Aircraft detection Maximum likelihood estimation Monte Carlo methods Gaussians Maximum likelihood methods Particle Filtering Roll dynamics sensor anomaly detection sensor output sensor readings System modeling

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sensor anomaly detection in Wireless sensor Networks for Healthcare

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sensorS 2015年第4期15卷 8764-8786页

作者： Haque, Shah Ahsanul Rahman, Mustafizur Aziz, Syed Mahfuzul Univ S Australia Sch Engn Mawson Lakes SA 5095 Australia Def Sci & Technol Org Dept Def Adelaide SA 5111 Australia

Wireless sensor Networks (WSN) are vulnerable to various sensor faults and faulty measurements. This vulnerability hinders efficient and timely response in various WSN applications, such as healthcare. For example, faulty measurements can create false alarms which may require unnecessary intervention from healthcare personnel. Therefore, an approach to differentiate between real medical conditions and false alarms will improve remote patient monitoring systems and quality of healthcare service afforded by WSN. In this paper, a novel approach is proposed to detect sensor anomaly by analyzing collected physiological data from medical sensors. The objective of this method is to effectively distinguish false alarms from true alarms. It predicts a sensor value from historic values and compares it with the actual sensed value for a particular instance. The difference is compared against a threshold value, which is dynamically adjusted, to ascertain whether the sensor value is anomalous. The proposed approach has been applied to real healthcare datasets and compared with existing approaches. Experimental results demonstrate the effectiveness of the proposed system, providing high detection Rate (DR) and low False Positive Rate (FPR).

关键词： wireless sensor networks healthcare medical sensors sensor fault sensor anomaly detection prediction

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sensor ANOMALIES CHARACTERIZATION AND detection VIA MACHINE LEARNING METHODS FOR NUCLEAR POWER PLANTS 18

SENSOR ANOMALIES CHARACTERIZATION AND DETECTION VIA MACHINE ...

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ASME 18th International Conference on Energy Sustainability (ES)

作者： Pohlmann, Liam M. Bhowmik, Palash K. Wang, Congjian Sabharwall, Piyush Univ New Mexico Dept Nucl Engn MSC01 11201 Univ New Mexico Albuquerque NM 87131 USA Idaho Natl Lab Reactor Syst Design & Anal Div 1955 Fremont Ave Idaho Falls ID 83415 USA

ISBN: (纸本)9780791887899

Next-generation nuclear reactors pose challenges for effective system monitoring and data management, necessitating the differentiation of anomalous sensor data from noise. While digital twin models hold promise, fundamental analyses using simplified models are needed. This study proposes a foundational approach utilizing simulated data from PCTRAN-generated datasets to emulate reference pressurized-water reactor (PWR) conditions and introduces typical sensor performance and anomalies. The method employs data partitioning, linear regression for preprocessing, and a K-means clustering algorithm for anomaly detection, achieving over 95% precision in identifying anomalies. A parametric study using Monte Carlo Sampling on the anomaly detection algorithm's input values reveals critical factors such as window size's impact on accuracy and computational time. Utilizing the Risk Analysis Virtual Environment (RAVEN) tools, a Pareto optimal frontier is determined to balance accuracy and execution time. Sensitivity and uncertainty analysis highlight window size as a critical factor. While further refinement is necessary for practical application, these techniques show promise for enhancing nuclear system monitoring and data management.

关键词： sensor anomaly detection nuclear reactor system linear regression K-means clustering unsupervised learning RAVEN optimization

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sensor Fault Diagnosis Using a Machine Fuzzy Lyapunov-Based Computed Ratio Algorithm

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sensorS 2022年第8期22卷 2974页

作者： TayebiHaghighi, Shahnaz Koo, Insoo Univ Ulsan Dept Elect Elect & Comp Engn Ulsan 680749 South Korea

anomaly identification for internal combustion engine (ICE) sensors has become an important research area in recent years. In this work, a proposed indirect fuzzy Lyapunov-based computed ratio observer integrated with a support vector machine (SVM) was designed for sensor fault classification. The proposed fuzzy Lyapunov-based computed ratio observer integrated with SVM has three main layers. In the preprocessing (first) layer, the resampled root mean square (RMS) signals are extracted from the original signals to the designed indirect observer. The second (observation) layer is the principal part with the proposed indirect fuzzy sensor-fault-classification technique. This layer has two sub-layers: signal modeling and estimation. The Gaussian autoregressive-Laguerre approach integrated with the fuzzy approach is designed for resampled RMS fuel-to-air-ratio normal signal modeling, while the subsequent sub-layer is used for resampled RMS fuel-to-air-ratio signal estimation using the proposed fuzzy Lyapunov-based computed ratio observer. The third layer, for residual signal generation and classification, is used to identify ICE sensor anomalies, where residual signals are generated by the difference between the original and estimated resampled RMS fuel-to-air-ratio signals. Moreover, SVM is suggested for residual signal classification. To test the effectiveness of the proposed method, the results are compared with two approaches: a Lyapunov-based computed ratio observer and a computed ratio observer. The results show that the accuracy of sensor anomaly classification by the proposed fuzzy Lyapunov-based computed ratio observer is 98.17%. Furthermore, the proposed scheme improves the accuracy of sensor fault classification by 8.37%, 2.17%, 6.17%, 4.57%, and 5.37% compared to other existing methods such as the computed ratio observer, the Lyapunov-based computed ratio observer, fuzzy feedback linearization observation, self-tuning fuzzy robust multi-integral observer, an

关键词： internal combustion engine sensor anomaly detection Gaussian autoregressive method fuzzy approach computed ratio observer Lyapunov robust method support vector machine

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Dealing With Inaccurate sensor Data in the Context of Mobile Crowdsensing and mHealth

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第11期26卷 5439-5449页

作者： Kraft, Robin Hofmann, Fabian Reichert, Manfred Pryss, Ruediger Ulm Univ Inst Databases & Informat Syst D-89081 Ulm Germany Ulm Univ Dept Clin Psychol & Psychotherapy D-89081 Ulm Germany Univ Wurzburg Inst Clin Epidemiol & Biometry D-97080 Wurzburg Germany

The technological capabilities and ubiquity of smart mobile devices favor the combined utilization of Ecological Momentary Assessments (EMA) and Mobile Crowdsensing (MCS). In the healthcare domain, this combination particularly enables the collection of ecologically valid and longitudinal data. Furthermore, the context in which these data are collected can be captured through the use of smartphone sensors as well as externally connected sensors. The TrackYourTinnitus (TYT) mobile platform uses these concepts to collect the user's individual subjective perception of tinnitus as well as an objective environmental sound level. However, the sound level data in the TYT database are subject to several possible sensor errors and therefore do not allow a meaningful interpretation in terms of correlation with tinnitus symptoms. To this end, a data-centric approach based on Principal Component Analysis (PCA) is proposed in this paper to cleanse MCS mHealth data sets from erroneous sensor data. To further improve the approach, additional information (i.e., responses to the EMA questionnaire) is considered in the PCA and a prior check for constant values is performed. To demonstrate the practical feasibility of the approach, in addition to TYT data, where it is generally unknown which sensor measurements are actually erroneous, a simulation with generated data was designed and performed to evaluate the performance of the approach with different parameters based on different quality metrics. The results obtained show that the approach is able to detect an average of 29.02% of the errors, with an average false-positive rate of 14.11%, yielding an overall error reduction of 22.74%.

关键词： Principal component analysis Wireless sensor networks Biomedical measurement Mobile applications Bioinformatics Data models Crowdsensing Crowdsensing environmental sound mHealth noise measurement sensor anomaly detection tinnitus

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Low-complexity, robust algorithm for sensor anomaly detection and self-calibration of microphone arrays

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IET SIGNAL PROCESSING 2011年第1期5卷 97-103页

作者： Madhu, N. Martin, R. Ruhr Univ Bochum Inst Commun Acoust IKA D-44780 Bochum Germany

This study details a simple method for the detection of sensor degradation in microphone arrays and equalisation of the microphone gains. The described method is online in nature and based upon the long-term power spectra of the microphone signals of each array. The close proximity of the microphones within an array is essential for the application of the proposed detection and calibration method. The approach is tested on three sample degradation scenarios: complete sensor failure, non-linearity in the input and parasitic harmonic injection. While these scenarios are by no means exhaustive, they represent the most typical degradations in many applications. The results indicate that the developed approach is capable of detecting such failures. Further, the calibration factors computed using the proposed approach are similar to that obtained by established methods.

关键词： sensor degradation detection close proximity microphone arrays microphone gain equalisation sensor anomaly detection harmonic analysis sensors long-term power spectra Audio equipment and systems communication complexity microphone array self-calibration Measurement standards and calibration parasitic harmonic injection equalisers Sensing devices and transducers calibration

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Environmental sensor anomaly detection using learning machines

Environmental sensor anomaly detection using learning machin...

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作者： Conde, Erick F. Utah State University

学位级别：M.S.

The problem of quality assurance/quality control (QA/QC) for real-time measurements of environmental and water quality variables has been a field explored by many in recent years. The use of in situ sensors has become a common practice for acquiring real-time measurements that provide the basis for important natural resources management decisions. However, these sensors are susceptible to failure due to such things as human factors, lack of necessary maintenance, flaws on the transmission line or any part of the sensor, and unexpected changes in the sensors’ surrounding conditions. Two types of machine learning techniques were used in this study to assess the detection of anomalous data points on turbidity readings from the Paradise site on the Little Bear River, in northern Utah: Artificial Neural Networks (ANNs) and Relevance Vector Machines (RVMs). ANN and RVM techniques were used to develop regression models capable of predicting upcoming Paradise site turbidity measurements and estimating confidence intervals associated with those predictions, to be later used to determine if a real measurement is an anomaly. Three cases were identified as important to evaluate as possible inputs for the regression models created: (1) only the reported values from the sensor from previous time steps, (2) reported values from the sensor from previous time steps and values of other water types of sensors from the same site as the target sensor, and (3) adding as inputs the previous readings from sensors from upstream sites. The decision of which of the models performed the best was made based on each model’s ability to detect anomalous data points that were identified in a QA/QC analysis that was manually performed by a human technician. False positive and false negative rates for a range of confidence intervals were used as the measure of performance of the models. The RVM models were able to detect more anomalous points within narrower confidence intervals than the ANN models.

关键词： quality assurance quality control sensor anomaly detection Water quality

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