scholarly journals Study on Gas Turbine Gas-Path Fault Diagnosis Method Based on Quadratic Entropy Feature Extraction

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 89118-89127 ◽  
Author(s):  
Jingchao Li ◽  
Yulong Ying ◽  
Chunlei Ji
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Quadratic Entropy ◽  
Diagnosis Method ◽  
Gas Path Fault

scholarly journals Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4701 ◽  
Author(s):  
Yunpeng Cao ◽  
Xinran Lv ◽  
Guodong Han ◽  
Junqi Luan ◽  
Shuying Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Probabilistic Neural Network ◽  
Second Law Of Thermodynamics ◽  
Fault Detection And Isolation ◽  
Exergy Loss ◽  
Diagnosis Method ◽  
Gas Path Fault ◽  
Faulty Component

In order to improve the accuracy of gas-path fault detection and isolation for a marine three-shaft gas turbine, a gas-path fault diagnosis method based on exergy loss and a probabilistic neural network (PNN) is proposed. On the basis of the second law of thermodynamics, the exergy flow among the subsystems and the external environment is analyzed, and the exergy model of a marine gas turbine is established. The exergy loss of a marine gas turbine under the healthy condition and typical gas-path faulty condition is analyzed, and the relative change of exergy loss is used as the input of the PNN to detect the gas-path malfunction and locate the faulty component. The simulation case study was conducted based on a three-shaft marine gas turbine with typical gas-path faults. Several results show that the proposed diagnosis method can accurately detect the fault and locate the malfunction component.

scholarly journals Application of Reinforcement Learning Algorithm Model in Gas Path Fault Intelligent Diagnosis of Gas Turbine

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yulong Luo
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Learning Algorithm ◽  
Working Environment ◽  
Intelligent Diagnosis ◽  
Diagnosis Method ◽  
Gas Path Fault ◽  
Reinforcement Learning Algorithm

Gas turbine is widely used because of its advantages of fast start and stop, no pollution, and high thermal efficiency. However, the working environment of high temperature, high pressure, and high speed makes the gas turbine prone to failure. The traditional gas path fault intelligent diagnosis scheme of the gas turbine has the problems of poor control effect and low scheduling accuracy. Experiment studies the application of neural network and reinforcement learning algorithm in gas path fault intelligent diagnosis of the gas turbine. The accurate control of fault diagnosis planning is realized from gas path fault diagnosis, daily maintenance, service condition monitoring, power utilization rate, and other aspects of the gas turbine. The reinforcement learning model can realize the intelligent diagnosis and record of gas path fault of the gas turbine, to achieve diversified analysis and intelligent diagnosis scheme. Through neural network algorithm and deep learning technology, the whole process monitoring of the gas turbine is realized, and the failure rate of the gas turbine in the working process is reduced. The experimental results show that, compared with the thermal fault diagnosis method and the fault diagnosis method of the electric percussion drill, using thermal imaging, the gas turbine gas path fault intelligent diagnosis model based on the reinforcement learning algorithm can complete the data information in the process of real-time data transmission. The quantified conversion and processing of the system has the advantages of higher control accuracy and faster response speed, which can effectively improve the diagnostic efficiency and accuracy.

scholarly journals A Marine Gas Turbine Fault Diagnosis Method Based on Endogenous Irreversible Loss

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4677 ◽  
Author(s):  
Yunpeng Cao ◽  
Junqi Luan ◽  
Guodong Han ◽  
Xinran Lv ◽  
Shuying Li
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Thermal Efficiency ◽  
Simulation Experiment ◽  
Second Law Of Thermodynamics ◽  
Second Law ◽  
Irreversible Loss ◽  
Loss Analysis ◽  
Diagnosis Method ◽  
Gas Path Fault

When a malfunction occurs in a marine gas turbine, its thermal efficiency will decrease slightly, and the gas path fault is often difficult to distinguish. In order to solve this problem, based on the second law of thermodynamics, the endogenous irreversible loss (EIL) model of the marine gas turbine is established, and the exergy loss analysis under normal conditions is carried out to verify the accuracy of the model. The fault diagnosis of gas turbine gas path based on EIL is proposed, and a simulation experiment conducted on a three-shaft marine gas turbine demonstrated that the proposed approach can detect and isolate gas path fault accurately under different operating conditons and enviroments.

A Two-Layer Multi-Model Gas Path Fault Diagnosis Method

2018 ◽  
Author(s):  
Yunpeng Cao ◽  
Yinghui He ◽  
Fang Yu ◽  
Jianwei Du ◽  
Shuying Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Fault Isolation ◽  
Fuzzy Membership Function ◽  
Distance Method ◽  
Isolation Layer ◽  
Diagnosis Method ◽  
Gas Path Fault

This paper presents a two-layer multi-model gas path fault diagnosis method for gas turbines that includes a fault detection layer and a fault isolation layer. A health model and a gas path fault model based on a back propagation neural network are used for the real-time estimation of the output parameters of a gas turbine in the fault detection layer and the output parameter residual in the fault isolation layer, respectively. A fault detection algorithm is proposed based on fuzzy inference, and the fuzzy membership function of the output parameters residual is realized using data statistics. A similarity distance method is used to realize fault isolation, and a fault probability algorithm based on the Mahalanobis distance is presented. Finally, the proposed method is verified by a three-shaft gas turbine simulation platform, and the simulation test results show that the two-layer multi-model gas path fault diagnosis method can detect and isolate the gas path fault accurately with a low calculation cost and good extensibility.

CvT Fault Diagnosis Method of Manifold Sensitive Modal Matrix Under Variable Speed

2021 ◽  
Author(s):  
Hao DeChen ◽  
HuaLing Li ◽  
JinYing Huang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
Network Structure ◽  
Rotating Machinery ◽  
Variable Speed ◽  
Data Set ◽  
Public Data ◽  
Diagnosis Method ◽  
Diagnosis Model

Abstract Rotating machinery (RM) is one of the most common mechanical equipment in engineering applications and has a broad and vital role. Rotating machinery includes gearboxes, bearing motors, generators, etc. In industrial production, the important position of rotating machinery and its variable speed and complex working conditions lead to unstable vibration characteristics, which have become a research hotspot in mechanical fault diagnosis. Aiming at the multi-classification problem of rotating machinery with variable speed and complex working conditions, this paper proposes a fault diagnosis method based on the construction of improved sensitive mode matrix (ISMM), isometric mapping (ISOMAP) and Convolution-Vision Transformer network (CvT) structure. After overlapping and sampling the variable speed signals, a high-dimensional ISMM is constructed, and the ISMM is mapped into the manifold space through ISOMAP manifold learning. This method can extract the fault transient characteristics of the variable speed signal, and the experiment proves that it can solve the problem that the conventional method cannot effectively extract the characteristics of the variable speed data. CvT combines the advantages of self-attention mechanism and convolution in CNN, so the CvT network structure is used for feature extraction and fault recognition and classification. The CvT network structure takes into account both global feature extraction and local feature extraction, which greatly reduces the number of training iterations and the size of the network model. Two data sets (the HFXZ-I planetary gearbox variable speed data set in the laboratory and the bearing variable speed public data set of the University of Ottawa in Canada) are used to experimentally verify the proposed fault diagnosis model. Experimental results show that the proposed fault diagnosis model has good recognition accuracy and robustness.

scholarly journals Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1523 ◽  
Author(s):  
Chang Liu ◽  
Gang Cheng ◽  
Xihui Chen ◽  
Yusong Pang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Planetary Gears ◽  
Diagnosis Method

scholarly journals Simultaneous-Fault Diagnosis of Gas Turbine Generator Systems Using a Pairwise-Coupled Probabilistic Classifier

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Zhixin Yang ◽  
Pak Kin Wong ◽  
Chi Man Vong ◽  
Jianhua Zhong ◽  
JieJunYi Liang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Diagnostic System ◽  
Kernel Principal Component Analysis ◽  
Training Dataset ◽  
Decision Threshold ◽  
Turbine Generator ◽  
Single Fault ◽  
Probabilistic Classifier

A reliable fault diagnostic system for gas turbine generator system (GTGS), which is complicated and inherent with many types of component faults, is essential to avoid the interruption of electricity supply. However, the GTGS diagnosis faces challenges in terms of the existence of simultaneous-fault diagnosis and high cost in acquiring the exponentially increased simultaneous-fault vibration signals for constructing the diagnostic system. This research proposes a new diagnostic framework combining feature extraction, pairwise-coupled probabilistic classifier, and decision threshold optimization. The feature extraction module adopts wavelet packet transform and time-domain statistical features to extract vibration signal features. Kernel principal component analysis is then applied to further reduce the redundant features. The features of single faults in a simultaneous-fault pattern are extracted and then detected using a probabilistic classifier, namely, pairwise-coupled relevance vector machine, which is trained with single-fault patterns only. Therefore, the training dataset of simultaneous-fault patterns is unnecessary. To optimize the decision threshold, this research proposes to use grid search method which can ensure a global solution as compared with traditional computational intelligence techniques. Experimental results show that the proposed framework performs well for both single-fault and simultaneous-fault diagnosis and is superior to the frameworks without feature extraction and pairwise coupling.

scholarly journals A New Method of Fault Feature Extraction Based on Hierarchical Dispersion Entropy

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Peng Chen ◽  
Xiaoqiang Zhao ◽  
HongMei Jiang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
White Noise ◽  
Gaussian White Noise ◽  
Rolling Bearing ◽  
K Nearest Neighbor ◽  
Vibration Signals ◽  
Diagnosis Method ◽  
Feature Information ◽  
Fault Feature Extraction

In the process of fault feature extraction of rolling bearing, the feature information is difficult to be extracted fully. A novel method of fault feature extraction called hierarchical dispersion entropy is proposed in this paper. In this method, the vibration signals firstly are decomposed hierarchically. Secondly, dispersion entropies of different nodes are calculated. Hierarchical dispersion entropy can realize the comprehensive feature extraction of the high- and low-frequency band information of vibration signals and overcome the problems that dispersion entropy and multiscale dispersion entropy are insufficient to extract the fault feature information of vibration signals. The feasibility of hierarchical dispersion entropy is obtained by analyzing the hierarchical dispersion entropy of Gaussian white noise and compared with the multiscale dispersion entropy of Gaussian white noise. Meanwhile, a fault diagnosis method for rolling bearings based on hierarchical dispersion entropy and k-nearest neighbor (KNN) classifier is developed. Finally, the superiority of the proposed fault diagnosis method is verified in the realization of fault diagnosis of the rolling bearing in different positions and different degrees of damage.

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