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 Simultaneous-Fault Diagnosis of Automotive Engine Ignition Systems Using Prior Domain Knowledge and Relevance Vector Machine

2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Chi-Man Vong ◽  
Pak-Kin Wong ◽  
Weng-Fai Ip ◽  
Chi-Chong Chiu
Keyword(s):  
Fault Diagnosis ◽  
Domain Knowledge ◽  
Diagnostic System ◽  
Relevance Vector Machine ◽  
Training Dataset ◽  
Decision Threshold ◽  
Single Fault ◽  
Automotive Engine ◽  
Probabilistic Classification ◽  
Threshold Optimization

Engine ignition patterns can be analyzed to identify the engine fault according to both the specific prior domain knowledge and the shape features of the patterns. One of the challenges in ignition system diagnosis is that more than one fault may appear at a time. This kind of problem refers to simultaneous-fault diagnosis. Another challenge is the acquisition of a large amount of costly simultaneous-fault ignition patterns for constructing the diagnostic system because the number of the training patterns depends on the combination of different single faults. The above problems could be resolved by the proposed framework combining feature extraction, probabilistic classification, and decision threshold optimization. With the proposed framework, the features of the single faults in a simultaneous-fault pattern are extracted and then detected using a new probabilistic classifier, namely, pairwise coupling relevance vector machine, which is trained with single-fault patterns only. Therefore, the training dataset of simultaneous-fault patterns is not necessary. Experimental results show that the proposed framework performs well for both single-fault and simultaneous-fault diagnoses and is superior to the existing approach.

scholarly journals An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jian-Hua Zhong ◽  
JieJunYi Liang ◽  
Zhi-Xin Yang ◽  
Pak Kin Wong ◽  
Xian-Bo Wang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Extraction Method ◽  
Support Vector ◽  
Redundant Information ◽  
Turbine Generator ◽  
Generator System ◽  
Feature Extraction Method ◽  
Fault Feature Extraction

Fault diagnosis is very important to maintain the operation of a gas turbine generator system (GTGS) in power plants, where any abnormal situations will interrupt the electricity supply. The fault diagnosis of the GTGS faces the main challenge that the acquired data, vibration or sound signals, contain a great deal of redundant information which extends the fault identification time and degrades the diagnostic accuracy. To improve the diagnostic performance in the GTGS, an effective fault feature extraction framework is proposed to solve the problem of the signal disorder and redundant information in the acquired signal. The proposed framework combines feature extraction with a general machine learning method, support vector machine (SVM), to implement an intelligent fault diagnosis. The feature extraction method adopts wavelet packet transform and time-domain statistical features to extract the features of faults from the vibration signal. To further reduce the redundant information in extracted features, kernel principal component analysis is applied in this study. Experimental results indicate that the proposed feature extracted technique is an effective method to extract the useful features of faults, resulting in improvement of the performance of fault diagnosis for the GTGS.

Real-time fault diagnosis for gas turbine generator systems using extreme learning machine

2014 ◽  
Vol 128 ◽  
pp. 249-257 ◽  
Author(s):  
Pak Kin Wong ◽  
Zhixin Yang ◽  
Chi Man Vong ◽  
Jianhua Zhong
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Gas Turbine ◽  
Extreme Learning Machine ◽  
Turbine Generator ◽  
Learning Machine

scholarly journals KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Xiao Hu ◽  
Zhihuai Xiao ◽  
Dong Liu ◽  
Yongjun Tang ◽  
O. P. Malik ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Frequency Spectrum ◽  
Extraction Method ◽  
Principal Component ◽  
Rotating Machinery ◽  
Kernel Principal Component Analysis ◽  
Global Feature ◽  
Vibration Signals ◽  
Feature Extraction Method

Feature extraction plays a key role in fault diagnosis of rotating machinery. Many methods reported in the literature are based on masses of labeled data and need much prior knowledge to select the most discriminating features or establish a complex deep-learning model. To solve the dilemma, a novel feature extraction method based on kernel principal component analysis (KPCA) and an autoencoder (AE), namely, SFS-KPCA-AE, is presented in this paper to automatically extract the most discriminative features from the frequency spectrum of vibration signals. First, fast Fourier transform is calculated on the entire vibration signal to get the frequency spectrum. Next, the spectrum is divided into several segments. Then, local-global feature extraction is performed by applying KPCA to these segments. Finally, an AE is employed to obtain the low-dimensional representations of the high-dimensional global feature. The proposed feature extraction method combined with a classifier achieves fault diagnosis for rotating machinery. A rotor dataset and a bearing dataset are utilized to validate the performance of the proposed method. Experimental results demonstrate that the proposed method achieved satisfactory performance in feature extraction when training samples or motor load changed. By comparing with other methods, the superiority of the proposed SFS-KPCA-AE is verified.

Real-time fault diagnostic system for a steam turbine generator set by using a fuzzy cerebellar model articulation controller

2009 ◽  
Vol 223 (5) ◽  
pp. 1253-1262 ◽  
Author(s):  
C Yan ◽  
H Zhang ◽  
D Peng ◽  
H Li ◽  
L Yang
Keyword(s):  
Fault Diagnosis ◽  
Steam Turbine ◽  
Real Time ◽  
Diagnostic System ◽  
Cerebellar Model Articulation Controller ◽  
Turbine Generator ◽  
Diagnosis System ◽  
The Real ◽  
Fuzzy Cmac ◽  
Fault Diagnosis System

Because a serious fault would result in a reduced amount of electricity supply in a power plant, the real-time fault diagnosis system is extremely important for a steam turbine generator set. A novel real-time intelligent fault diagnosis system is proposed by using a fuzzy cerebellar model articulation controller (CMAC) neural network to detect and identify the faults and failures of critical components. A framework of the fault diagnosis system is described. The model of a novel fault diagnosis system by using a fuzzy CMAC is built and analysed in detail step by step. A case of the diagnosis including three faults is simulated with a fuzzy CMAC. The results show that the real-time fault diagnostic system is of high accuracy, quick convergence, and high noise rejection. Moreover, the model is verified by two examples. It is found that this model is feasible. Finally, the effects of the generalization parameter and address number in fault diagnosis are discussed.

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