Fault Detection and Identification in NPP Instruments Using Kernel Principal Component Analysis

Fault Detection ◽

Power Plants ◽

Principal Component ◽

Component Analysis ◽

Fault Isolation ◽

Isolation And Identification ◽

In this paper, kernel principal component analysis (KPCA) is studied for fault detection and identification in the instruments of nuclear power plants. We propose to use mean values of the sensor reconstruction errors of a KPCA model for fault isolation and identification. They provide useful information about the directions and magnitudes of detected faults, which are usually not available from other fault isolation techniques. The performance of the method is demonstrated by applications to real NPP measurements.

Detection and Identification of Faults in NPP Instruments Using Kernel Principal Component Analysis

Journal of Engineering for Gas Turbines and Power ◽

10.1115/1.4004596 ◽

2011 ◽

Vol 134 (3) ◽

Cited By ~ 3

Author(s):

Jianping Ma ◽

Jin Jiang

Keyword(s):

Nuclear Power ◽

Power Plants ◽

Principal Component ◽

Component Analysis ◽

Fault Isolation ◽

Isolation And Identification ◽

In this paper, kernel principal component analysis (KPCA) is studied for fault detection and identification of the instruments in nuclear power plants. A KPCA model for fault isolation and identification is proposed by using the average sensor reconstruction errors. Based on this model, faults in multiple sensors can be isolated and identified simultaneously. Performance of the KPCA-based method is demonstrated with real NPP measurements.

The instrument fault detection and identification based on kernel principal component analysis and coupling analysis in process industry

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220960247 ◽

2020 ◽

pp. 014233122096024

Author(s):

Yanjie Liang ◽

Zhiyong Gao ◽

Jianmin Gao ◽

Guangnan Xu ◽

Rongxi Wang

Keyword(s):

Fault Detection ◽

Principal Component ◽

Process Industry ◽

New Method ◽

Fault Identification ◽

Coupling Analysis ◽

This paper investigates the fault detection problem of instruments in process industry. Considering the difficulty of fault identification and the problems of multivariable and large computation complexity based on traditional kernel principal component analysis (KPCA), this paper presents a new method for fault detection and identification, which combines the coupling analysis with kernel principal component for multivariable fault detection and employed the local outlier factor (LOF) for multivariable fault identification. The new method consists of three parts. Firstly, according to nonlinear correlation of multivariable, coupling analysis and module division of variables based on detrended cross-correlation analysis (DCCA) are considered to reduce false alarm rate (FAR) and missed detection rate (MDR) in fault detection and identification. Secondly, KPCA is employed to detect fault in each sub-module of variables. Finally, for the sub-module which has the fault detected in second step, the LOF is adopted to calculate abnormal contribution of each variable in sub-modules to realize fault identification. To prove that the new method has the better capability of processing multivariable fault detection and the more accuracy rate on fault detection and identification than the conventional methods of KPCA, a case study on Tennessee process is carried out at the end.

Fault detection and isolation in nonlinear systems with partial Reduced Kernel Principal Component Analysis method

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331216679250 ◽

2016 ◽

Vol 40 (4) ◽

pp. 1289-1296 ◽

Cited By ~ 4

Author(s):

Ines Jaffel ◽

Okba Taouali ◽

Mohamed Faouzi Harkat ◽

Hassani Messaoud

Keyword(s):

Fault Detection ◽

Principal Component ◽

Component Analysis ◽

Fault Isolation ◽

Fault Detection And Isolation ◽

Continuous Stirred Tank Reactor ◽

Analysis Method ◽

Continuous Stirred Tank

In this article, we suggest an extension of our proposed method in fault detection called Reduced Kernel Principal Component Analysis (RKPCA) (Taouali et al., 2015) to fault isolation. To this end, a set of structured residues is generated by using a partial RKPCA model. Furthermore, each partial RKPCA model was performed on a subset of variables to generate structured residues according to a properly designed incidence matrix. The relevance of the proposed algorithm is revealed on Continuous Stirred Tank Reactor.

Fault detection and identification using a Kullback-Leibler divergence based multi-block principal component analysis and bayesian inference

Korean Journal of Chemical Engineering ◽

10.1007/s11814-013-0295-1 ◽

2014 ◽

Vol 31 (6) ◽

pp. 930-943 ◽

Cited By ~ 19

Author(s):

Bei Wang ◽

Qingchao Jiang ◽

Xuefeng Yan

Keyword(s):

Bayesian Inference ◽

Fault Detection ◽

Principal Component ◽

Component Analysis ◽

Leibler Divergence ◽

Sensor Fault Detection and Identification Method with KPCA in the Process of Aero-Engine Ground Testing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.303-306.297 ◽

2013 ◽

Vol 303-306 ◽

pp. 297-301 ◽

Cited By ~ 1

Author(s):

Tao Xu

Keyword(s):

Fault Detection ◽

Principal Component ◽

Component Analysis ◽

Sensor Fault ◽

Ground Testing ◽

Fault Detection And Identification ◽

Aero Engine ◽

Sensor Fault Detection

This paper focuses on Kernel Principal Component Analysis (KPCA) in order to solve the problem of sensor fault detection and identification without linear relationship between sensors. After samples are projected into high-dimensional space, new Principal Component Analysis (PCA) model is established in the kernel principal component eigenvector space. By the contrast with SPE, Hotelling T2 is an appropriate parameter to detect sensor fault because it is more sensitive to sensor failure. The contribution of different sensor to Hotelling T2 is utilized to identify sensor fault for the faulty sensor’s contribution is large than others significantly. Finally, the proposed method is illustrated by the sensors of oil-providing system in the process of aero-engine ground testing. Experiment results show the applicability and effectiveness of the proposed method.

Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach

Applied Sciences ◽

10.3390/app11146370 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6370

Author(s):

Elena Quatrini ◽

Francesco Costantino ◽

David Mba ◽

Xiaochuan Li ◽

Tat-Hean Gan

Keyword(s):

Fault Detection ◽

Water Purification ◽

Principal Component ◽

Temporal Correlation ◽

Component Analysis ◽

Original Dataset ◽

Monitoring Process ◽

Machine Health

The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the one hand, the monitoring process reveals various discontinuities due to different characteristics of the input water. On the other hand, the monitoring process is discontinuous and random itself, thus not guaranteeing continuity of the parameters and hindering a straightforward analysis. Consequently, further research on water purification processes is paramount to identify the most suitable techniques able to guarantee good performance. Against this background, this paper proposes an application of kernel principal component analysis for fault detection in a process with the above-mentioned characteristics. Based on the temporal variability of the process, the paper suggests the use of past and future matrices as input for fault detection as an alternative to the original dataset. In this manner, the temporal correlation between process parameters and machine health is accounted for. The proposed approach confirms the possibility of obtaining very good monitoring results in the analyzed context.