scholarly journals Nuclear power plant sensor fault detection using singular value decomposition-based method

Sadhana ◽  
2017 ◽  
Vol 42 (9) ◽  
pp. 1473-1480 ◽  
Author(s):  
Shyamapada Mandal ◽  
N Sairam ◽  
S Sridhar ◽  
P Swaminathan
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Fault Detection ◽  
Singular Value Decomposition ◽  
Nuclear Power ◽  
Singular Value ◽  
Sensor Fault ◽  
Sensor Fault Detection ◽  
Value Decomposition

Sensor fault detection in Nuclear Power Plant using statistical methods

2017 ◽  
Vol 324 ◽  
pp. 103-110 ◽  
Author(s):  
Shyamapada Mandal ◽  
B. Santhi ◽  
S. Sridhar ◽  
K. Vinolia ◽  
P. Swaminathan
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Fault Detection ◽  
Statistical Methods ◽  
Nuclear Power ◽  
Sensor Fault ◽  
Sensor Fault Detection

Data-Driven Fault Diagnosis for Nuclear Power Plant: The Implicit Model Approach

2018 ◽  
Author(s):  
Zhaoxu Chen ◽  
Xianling Li ◽  
Zhiwu Ke ◽  
Mo Tao ◽  
Yi Feng
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Fault Detection ◽  
Nuclear Power ◽  
Model Identification ◽  
Industrial Process ◽  
Data Driven ◽  
Implicit Model ◽  
Complicated Model ◽  
Model Approach

This paper proposes a data-driven fault detection approach for nuclear power plant. The approach starts from input and output (I/O) data obtained from operating data of industrial process. Due to the model is not explicitly appeared, the proposed approach is named as implicit model approach (IMA). Residual generator is obtained directly from I/O data rather than from the mechanism, based which the algorithm of IMA-based fault detection is proposed. The main advantage of IMA-based fault detection is that it can circumvent complicated model identification. The approach generates parameterized matrices of residual signal inspired by subspace relevant technology without any prior knowledge about mechanisms of the plant. Fault information has been injected to a simulating platform of a compact reactor in the simulation part, by which we verify the effectiveness of IMA-based fault detection.

scholarly journals Rolling Bearing Incipient Fault Detection Based on a Multi-Resolution Singular Value Decomposition

2019 ◽  
Vol 9 (20) ◽  
pp. 4465 ◽  
Author(s):  
Jiesi Luo ◽  
Shaohui Zhang
Keyword(s):  
Fault Detection ◽  
Singular Value Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Singular Value ◽  
Incipient Fault ◽  
Periodic Impulse ◽  
Value Decomposition ◽  
Impulse Characteristics ◽  
Incipient Fault Detection

The periodic impulse characteristics caused by rolling bearing damage are weak in the incipient failure stage. Thus, these characteristics are always drowned out by background noise and other harmonic interference. A novel approach based on multi-resolution singular value decomposition (MRSVD) is proposed in order to extract the periodic impulse characteristics for incipient fault detection. With the MRSVD method, the vibration signal is first decomposed to obtain a group of approximate signals and detailed signals with different resolutions. The first detail signal is mainly composed of noise and the last approximate signal is mainly composed of harmonic interference. Combined with the kurtosis index, the hidden periodic impulse signal will be extracted from the detail signals (in addition to the first detail signal). Thus, the incipient fault detection of a rolling bearing can be fulfilled according to the envelope demodulation spectrum of the extracted periodic impulse signal. The effectiveness of the proposed method has been demonstrated with both simulation and experimental analyses.

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