Fault Diagnosis of Air Compressor in Nuclear Power Plant Based on Vibration Observation Window

Duan, Qizhi; Lu, Ruijie; Xie, Hongyun; Ping, Jialin; Lu, Chao; Zhou, Xiaowei; Gao, Jie; Li, Jixue

doi:10.1109/access.2020.3043398

Cited by 12 publications

(7 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…v k i means the kth sampling data in V i . We introduce an observation window [22] with the size of n × m to cover the time series data cluster V ii�1...n , where n is the number of sensors in the NPS. Ten, a dynamic matrix ∨ containing the operation data from T 0 to T 0 + mT can be built.…”

Section: Data Organizationmentioning

confidence: 99%

A Data-Driven Fault Prediction Method for Nuclear Power Systems Based on End-to-End Deep Learning Framework

Wang

Chen

et al. 2022

Science and Technology of Nuclear Installations

Self Cite

View full text Add to dashboard Cite

With the increase in system complexity and operational performance requirements, nuclear energy systems are developing in the direction of intelligence and unmanned, which also requires a higher demand for its safety so that intelligent fault diagnosis and prediction have become a technology that nuclear power plants need to develop at present. At the same time, due to the rapid development of deep learning technology, it has become a meaningful development direction to predict the fault state of nuclear power plants within the framework of supervised deep learning. Usually, the network structure model used in fault diagnosis and prediction requires professional design, which may cost a lot of time and make it difficult to achieve optimal results. For this purpose, we present an end-to-end deep network for nuclear power system prediction (EDN-NPSP), which can automatically mine the transient features of various detection data in the NPS at the current moment through heterogeneous convolution kernels that can increase the receptive field and then predict the feature evolution results of the NPS in the future through a special deep CNN. The results provide an assessment of the future state of NPS. Based on EDN-NPSP presented in this work, we can avoid complicated manual feature extraction and provide the predicted state directly and rapidly. It will provide operators with useful prediction information and enhance the nuclear energy system fault prediction capabilities.

show abstract

Section: Data Organizationmentioning

confidence: 99%

A Data-Driven Fault Prediction Method for Nuclear Power Systems Based on End-to-End Deep Learning Framework

Wang

Chen

et al. 2022

Science and Technology of Nuclear Installations

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several applications related to fault detection and classification problems have been reported concerning classical machine learning techniques. The more common approaches are Support Vector Machines (SVM) [5,10], random forests [11], the logistic regression algorithm [3], k-Nearest Neighbors (k-NN) [12], and neural networks [13][14][15]. Some examples of research concerning the application of machine learning techniques to fault detection and classification in centrifugal pumps and reciprocating compressors are reported in [3,10,13,16].…”

Section: Introductionmentioning

confidence: 99%

Improved Mel Frequency Cepstral Coefficients for Compressors and Pumps Fault Diagnosis with Deep Learning Models

Cabrera,

Medina,

Cerrada

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Compressors and pumps are machines frequently used in petroleum and chemical industries for fluid transportation through flow systems to keep industrial processes running permanently. As their failure can produce costly disruption, developing fault detection and diagnosis tools is essential for accurately detecting and diagnosing faults. This research proposes a bi-dimensional representation of the vibration signal corresponding to the Mel Frequency Cepstral Coefficients (MFCC) and their first two derivatives as features. The pseudo-periodic nature of the fault signature in rotating machines is exploited to put forward an efficient and accurate patch-wise fault classification method. This approach enables the classification of 13 combined types of faults in a multi-stage centrifugal pump and 17 faults in a reciprocating compressor. Classification is performed using the Long Short-Term Memory (LSTM) network, the bidirectional Long Short-Term Memory (BiLSTM) neural network, and the Convolutional Neural Network (CNN). Accurate classification over 99% is attained, showing that the proposed feature extraction procedure correctly classifies a large set of faults simultaneously appearing in such rotating machines.

show abstract

“…Although the approach is accurate, it can only distinguish between healthy and faulty states. Concerning machine learning techniques used in various application examples related to fault detection and classification, consider neural networks [ 27 , 28 , 29 ], the logistic regression algorithm [ 5 ], k-Nearest neighbors (k-NN) [ 30 ], random forests [ 31 ], multi-layer perceptron [ 17 , 28 ], and support vector machines (SVMs) [ 32 , 33 ]. These mentioned approaches are applications for centrifugal pumps or reciprocating compressors.…”

Section: Introductionmentioning

confidence: 99%

Scale-Fractal Detrended Fluctuation Analysis for Fault Diagnosis of a Centrifugal Pump and a Reciprocating Compressor

Medina,

Sánchez,

Cabrera

et al. 2024

Sensors

View full text Add to dashboard Cite

Reciprocating compressors and centrifugal pumps are rotating machines used in industry, where fault detection is crucial for avoiding unnecessary and costly downtime. A novel method for fault classification in reciprocating compressors and multi-stage centrifugal pumps is proposed. In the feature extraction stage, raw vibration signals are processed using multi-fractal detrended fluctuation analysis (MFDFA) to extract features indicative of different types of faults. Such MFDFA features enable the training of machine learning models for classifying faults. Several classical machine learning models and a deep learning model corresponding to the convolutional neural network (CNN) are compared with respect to their classification accuracy. The cross-validation results show that all models are highly accurate for classifying the 13 types of faults in the centrifugal pump, the 17 valve faults, and the 13 multi-faults in the reciprocating compressor. The random forest subspace discriminant (RFSD) and the CNN model achieved the best results using MFDFA features calculated with quadratic approximations. The proposed method is a promising approach for fault classification in reciprocating compressors and multi-stage centrifugal pumps.

show abstract

Fault Diagnosis of Air Compressor in Nuclear Power Plant Based on Vibration Observation Window

Cited by 12 publications

References 24 publications

A Data-Driven Fault Prediction Method for Nuclear Power Systems Based on End-to-End Deep Learning Framework

A Data-Driven Fault Prediction Method for Nuclear Power Systems Based on End-to-End Deep Learning Framework

Improved Mel Frequency Cepstral Coefficients for Compressors and Pumps Fault Diagnosis with Deep Learning Models

Scale-Fractal Detrended Fluctuation Analysis for Fault Diagnosis of a Centrifugal Pump and a Reciprocating Compressor

Contact Info

Product

Resources

About