An integrated approach to sensor FDI and signal reconstruction in HTGRs – Part I: Theoretical framework

Uren, Kenneth R.; Schoor, George van; Rand, C.P. du; Botha, Anrika

doi:10.1016/j.anucene.2015.06.010

Cited by 11 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For fault correction of the related variables, fuzzy similarity is used, which involves reconstructing the faulty variable based on the relationships between the related variables. Furthermore, Uren et al [72] proposed a PCA-based sensor fault detection, isolation, and reconstruction for bias, drifts, noise, constant value, and stuck-at-zero errors. For fault detection, non-temporal parity space is used to check for inconsistencies among a set of redundant sensors.…”

Section: Bayesian Network Dereszynski and Dietterichmentioning

confidence: 99%

Sensor data quality: a systematic review

2020

View full text Add to dashboard Cite

Sensor data quality plays a vital role in Internet of Things (IoT) applications as they are rendered useless if the data quality is bad. This systematic review aims to provide an introduction and guide for researchers who are interested in quality-related issues of physical sensor data. The process and results of the systematic review are presented which aims to answer the following research questions: what are the different types of physical sensor data errors, how to quantify or detect those errors, how to correct them and what domains are the solutions in. Out of 6970 literatures obtained from three databases (ACM Digital Library, IEEE Xplore and ScienceDirect) using the search string refined via topic modelling, 57 publications were selected and examined. Results show that the different types of sensor data errors addressed by those papers are mostly missing data and faults e.g. outliers, bias and drift. The most common solutions for error detection are based on principal component analysis (PCA) and artificial neural network (ANN) which accounts for about 40% of all error detection papers found in the study. Similarly, for fault correction, PCA and ANN are among the most common, along with Bayesian Networks. Missing values on the other hand, are mostly imputed using Association Rule Mining. Other techniques include hybrid solutions that combine several data science methods to detect and correct the errors. Through this systematic review, it is found that the methods proposed to solve physical sensor data errors cannot be directly compared due to the non-uniform evaluation process and the high use of non-publicly available datasets. Bayesian data analysis done on the 57 selected publications also suggests that publications using publicly available datasets for method evaluation have higher citation rates.

show abstract

Section: Bayesian Network Dereszynski and Dietterichmentioning

confidence: 99%

Sensor data quality: a systematic review

2020

View full text Add to dashboard Cite

show abstract

“…An evidence theory is a kind of uncertain reasoning and decision-making method that can handle inaccurate, uncertain, and fuzzy problems. As a good decision model, the evidence theory has been widely used in multi-sensor information fusion, target recognition, and uncertain information decision-making (Uren et al, 2016).…”

Section: Evidence Theorymentioning

confidence: 99%

Distributed Fault Diagnosis Framework for Nuclear Power Plants

Duan

Diping

et al. 2021

Front. Energy Res.

View full text Add to dashboard Cite

A fault diagnosis can quickly and accurately diagnose the cause of a fault. Focusing on the characteristics of nuclear power plants (NPPs), this study proposes a distributed fault diagnosis method based on a back propagation (BP) neural network and decision tree reasoning. First, the fault diagnosis was carried out using the BP neural network and decision tree reasoning, and then a global fusion diagnosis was performed by fusing the resulting information. Second, the key technologies of the BP neural network and decision tree sample construction were studied. Finally, the simulation results show that the proposed distributed fault diagnosis system is highly reliable and has strong diagnostic ability, enabling efficient and accurate diagnoses to be realized. The distributed fault diagnosis system for NPPs provides a solid foundation for future research.

show abstract

Fault detection, identification and reconstruction of sensors in nuclear power plant with optimized PCA method

Peng

Liu

et al. 2018

Annals of Nuclear Energy

View full text Add to dashboard Cite

An integrated approach to sensor FDI and signal reconstruction in HTGRs – Part I: Theoretical framework

Cited by 11 publications

References 22 publications

Sensor data quality: a systematic review

Sensor data quality: a systematic review

Distributed Fault Diagnosis Framework for Nuclear Power Plants

Fault detection, identification and reconstruction of sensors in nuclear power plant with optimized PCA method

Contact Info

Product

Resources

About