Research and Application on Fault Diagnosis for Steam Turbine Using Multi-Parameter Fusion

2014 ◽  
Vol 543-547 ◽  
pp. 1068-1073
Author(s):  
Xiao Wen Deng ◽  
Zhen Yu Zhou ◽  
Lei Song ◽  
Peng Li ◽  
Yu Jiong Gu
Keyword(s):  
Fault Diagnosis ◽  
Steam Turbine ◽  
Comprehensive Evaluation ◽  
Mechanical Equipment ◽  
Operational Parameters ◽  
Multiple Parameters ◽  
Equipment Operation ◽  
State Evaluation ◽  
Fault Mode ◽  
Vibration Parameters

For the running situation of supercritical and ultra-supercritical steam turbine unit, a fault diagnosis method of steam turbine based on multiple parameters fusion is proposed. The association of the fault mode with the vibration parameters, the thermodynamic parameters and the operational parameters is built, according to fault development mechanism, equipment operation data and professional experience. The overall state evaluation of mechanical equipment is given, the reliability of fault diagnosis is improved, and the need of the steam turbine fault diagnosis is met, by means of comprehensive evaluation of multiple parameters. Example applications verify this method.

scholarly journals New Technique for Evaluation of Global Vibration Levels in Rolling Bearings

2002 ◽  
Vol 9 (4-5) ◽  
pp. 225-234 ◽  
Author(s):  
Rui Gomes Teixeira de Almeida ◽  
Silmara Alexandra da Silva Vicente ◽  
Linilson Rodrigues Padovese
Keyword(s):  
Fault Diagnosis ◽  
New Technologies ◽  
Residual Energy ◽  
New Technique ◽  
Mechanical Equipment ◽  
Rolling Bearings ◽  
Global Parameter ◽  
Spectrum Density ◽  
The Difference ◽  
Vibration Parameters

In the last years new technologies and methodologies have been developed for increasing the reliability of fault diagnosis in mechanical equipment, mainly in rotating machinery. Global vibration indexes as RMS, Kurtosis, etc., are widespread known in industry and in addition, are recommended by international norms. Despite that, these parameters do not allow reaching reliable equipment condition diagnosis. They are attractive for their apparent simplicity of interpretation. This work presents a discussion about the diagnosis possibilities based on these traditional parameters. The database used comprises rolling bearings vibration signals taking into account different fault conditions, several shaft speeds and loading. The obtained results show that these global vibration parameters are limited regarding correct fault diagnosis, especially in initial faults condition. As an alternative method a new technique is proposed. This technique seeks to obtain a global parameter that makes better characterization of fault condition. This methodology, named Residual Energy, uses integration of the difference between the power spectrum density of the fault condition and the normal one. The results obtained with this technique are compared with the traditional RMS and Kurtosis.

scholarly journals A Novel Domain Adaptation-Based Intelligent Fault Diagnosis Model to Handle Sample Class Imbalanced Problem

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3382
Author(s):  
Zhongwei Zhang ◽  
Mingyu Shao ◽  
Liping Wang ◽  
Sujuan Shao ◽  
Chicheng Ma
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Class Imbalance ◽  
Rolling Bearing ◽  
Industrial Applications ◽  
Mechanical Equipment ◽  
Reliable Operation ◽  
Cross Domain ◽  
Geometric Difference ◽  
Model Generalization

As the key component to transmit power and torque, the fault diagnosis of rotating machinery is crucial to guarantee the reliable operation of mechanical equipment. Regrettably, sample class imbalance is a common phenomenon in industrial applications, which causes large cross-domain distribution discrepancies for domain adaptation (DA) and results in performance degradation for most of the existing mechanical fault diagnosis approaches. To address this issue, a novel DA approach that simultaneously reduces the cross-domain distribution difference and the geometric difference is proposed, which is defined as MRMI. This work contains three parts to improve the sample class imbalance issue: (1) A novel distance metric method (MVD) is proposed and applied to improve the performance of marginal distribution adaptation. (2) Manifold regularization is combined with instance reweighting to simultaneously explore the intrinsic manifold structure and remove irrelevant source-domain samples adaptively. (3) The ℓ2-norm regularization is applied as the data preprocessing tool to improve the model generalization performance. The gear and rolling bearing datasets with class imbalanced samples are applied to validate the reliability of MRMI. According to the fault diagnosis results, MRMI can significantly outperform competitive approaches under the condition of sample class imbalance.

Fault diagnosis method of peak-load-regulation steam turbine based on improved PCA-HKNN artificial neural network

2021 ◽  
pp. 1748006X2110105
Author(s):  
Yifan Wu ◽  
Wei Li ◽  
Deren Sheng ◽  
Jianhong Chen ◽  
Zitao Yu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Steam Turbine ◽  
Power Plants ◽  
Peak Load ◽  
Clean Energy ◽  
Steam Turbines ◽  
Peak Shaving ◽  
Diagnosis Method ◽  
Load Regulation

Clean energy is now developing rapidly, especially in the United States, China, the Britain and the European Union. To ensure the stability of power production and consumption, and to give higher priority to clean energy, it is essential for large power plants to implement peak shaving operation, which means that even the 1000 MW steam turbines in large plants will undertake peak shaving tasks for a long period of time. However, with the peak load regulation, the steam turbines operating in low capacity may be much more likely to cause faults. In this paper, aiming at peak load shaving, a fault diagnosis method of steam turbine vibration has been presented. The major models, namely hierarchy-KNN model on the basis of improved principal component analysis (Improved PCA-HKNN) has been discussed in detail. Additionally, a new fault diagnosis method has been proposed. By applying the PCA improved by information entropy, the vibration and thermal original data are decomposed and classified into a finite number of characteristic parameters and factor matrices. For the peak shaving power plants, the peak load shaving state involving their methods of operation and results of vibration would be elaborated further. Combined with the data and the operation state, the HKNN model is established to carry out the fault diagnosis. Finally, the efficiency and reliability of the improved PCA-HKNN model is discussed. It’s indicated that compared with the traditional method, especially handling the large data, this model enhances the convergence speed and the anti-interference ability of the neural network, reduces the training time and diagnosis time by more than 50%, improving the reliability of the diagnosis from 76% to 97%.

Application of Multiple Level Rules Set in Fault Diagnosis of the Wind Power Generation System

2012 ◽  
Vol 512-515 ◽  
pp. 679-685
Author(s):  
Gui Mei Gu
Keyword(s):  
Power Generation ◽  
Fault Diagnosis ◽  
Wind Power ◽  
Comprehensive Evaluation ◽  
Wind Power Generation ◽  
Decision Table ◽  
Multiple Level ◽  
Generation System ◽  
Power Generation System ◽  
Rules Set

For the incompletion problem of sensors’ collected data in fault diagnosis of the wind power system, this article puts forward a kind of multiple level rules set based on rough set. First, let the sensors’ collected data go through Fourier transform and extract its feature attributes as well as discrete them. Establish the decision table of fault diagnosis according to attribute values. Then set out from the decision table to establish a multiple level set of nodes with diverse reduced levels and deduce the rules of each node, which has a corresponding belief level. When in reasoning and decision-making of the new data using the multiple level rules set, match the information of the new data with the rule of its corresponding node. Finally, achieve the fault diagnosis of wind power generation system by choosing comprehensive evaluation algorithm. The result of the diagnosis example shows the reliability and accuracy of this method in the diagnosis of fault types for wind power generation system.

scholarly journals Increase of power and efficiency of the 900 MW supercritical power plant through incorporation of the ORC

2013 ◽  
Vol 34 (4) ◽  
pp. 51-71 ◽  
Author(s):  
Paweł Ziółkowski ◽  
Dariusz Mikielewicz ◽  
Jarosław Mikielewicz
Keyword(s):  
Power Plant ◽  
Heat Source ◽  
Steam Turbine ◽  
Hybrid System ◽  
Heat Recovery ◽  
Reference Conditions ◽  
Hot Water ◽  
Operational Parameters ◽  
Reference Case ◽  
Unit Power

Abstract The objective of the paper is to analyse thermodynamical and operational parameters of the supercritical power plant with reference conditions as well as following the introduction of the hybrid system incorporating ORC. In ORC the upper heat source is a stream of hot water from the system of heat recovery having temperature of 90 °C, which is additionally aided by heat from the bleeds of the steam turbine. Thermodynamical analysis of the supercritical plant with and without incorporation of ORC was accomplished using computational flow mechanics numerical codes. Investigated were six working fluids such as propane, isobutane, pentane, ethanol, R236ea and R245fa. In the course of calculations determined were primarily the increase of the unit power and efficiency for the reference case and that with the ORC.

Remote Diagnosis Method of Substation Equipment Fault Based on Image Recognition Technology

2021 ◽  
Author(s):  
Pei Zhang ◽  
Wenshuai Hu ◽  
Xiaolong Hao ◽  
Dingding Xi ◽  
Shuaishuai Yan
Keyword(s):  
Fault Diagnosis ◽  
Image Recognition ◽  
Remote Diagnosis ◽  
Application Process ◽  
Equipment Operation ◽  
Actual Application ◽  
Accurate Detection ◽  
Diagnosis Method ◽  
Substation Equipment ◽  
Information Characteristics

In order to better guarantee the operation effect of substation equipment, a remote fault diagnosis method of substation equipment based on image recognition technology is proposed. Combined with image recognition technology, the running image of substation equipment is tracked and collected, the information characteristics of substation equipment are deeply excavated, and the fault area of substation equipment is accurately judged. Remote positioning has been carried out to realize the accurate detection of substation equipment fault. Finally, through the experiment, the remote diagnosis method of substation equipment fault based on image recognition technology is in the actual application process With higher accuracy, it can effectively ensure the safety of substation equipment operation.

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