A novel on-line PD monitoring and diagnostic system for power transformers

2003 ◽  
Author(s):  
D. Lin ◽  
L. Jiang ◽  
F.Q. Li ◽  
D.H. Zhu ◽  
K.X. Tan ◽  
...  
Keyword(s):  
Diagnostic System ◽  
Power Transformers ◽  
On Line

Detection of Roller Bearing Conditions Using Mechatronics Approach

1999 ◽  
Author(s):  
T. I. Liu ◽  
F. Ordukhani
Keyword(s):  
Neural Networks ◽  
Roller Bearing ◽  
Diagnostic System ◽  
Feature Selection Technique ◽  
Vibration Signals ◽  
Selection Technique ◽  
On Line ◽  
Line Classification ◽  
Nonlinear Pattern

Abstract An on-line monitoring and diagnostic system is needed to detect faulty bearings. In this work, by applying the feature selection technique to the data obtained from vibration signals, six indices were selected. Artificial neural networks were used for nonlinear pattern recognition. An attempt was made to distinguish between normal and defective bearings. Counterpropagation neural networks with various network sizes were trained for these tasks. The counterpropagation neural networks were able to recognize a normal from a defective bearing with the success rate between 88.3% to 100%. The best results were obtained when all the six indices were used for the on-line classification of roller bearings.

scholarly journals A Novel Fault Diagnosis System on Polymer Insulation of Power Transformers Based on 3-stage GA–SA–SVM OFC Selection and ABC–SVM Classifier

Polymers ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1096 ◽  
Author(s):  
Xiaoge Huang ◽  
Yiyi Zhang ◽  
Jiefeng Liu ◽  
Hanbo Zheng ◽  
Ke Wang
Keyword(s):  
Artificial Intelligence ◽  
Fault Diagnosis ◽  
Diagnostic Accuracy ◽  
Diagnostic System ◽  
Gas Analysis ◽  
Diagnostic Methods ◽  
Power Transformers ◽  
Svm Classifier ◽  
Diagnosis System ◽  
Optimal Feature

Dissolved gas analysis (DGA) has been widely used in various scenarios of power transformers’ online monitoring and diagnoses. However, the diagnostic accuracy of traditional DGA methods still leaves much room for improvement. In this context, numerous new DGA diagnostic models that combine artificial intelligence with traditional methods have emerged. In this paper, a new DGA artificial intelligent diagnostic system is proposed. There are two modules that make up the diagnosis system. The two modules are the optimal feature combination (OFC) selection module based on 3-stage GA–SA–SVM and the ABC–SVM fault diagnosis module. The diagnosis system has been completely realized and embodied in its outstanding performances in diagnostic accuracy, reliability, and efficiency. Comparing the result with other artificial intelligence diagnostic methods, the new diagnostic system proposed in this paper performed superiorly.

scholarly journals Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of an Enhanced System

2008 ◽  
Author(s):  
Takahisa Kobayashi ◽  
Donald L. Simon
Keyword(s):  
Fault Detection ◽  
Diagnostic System ◽  
Aircraft Engine ◽  
Fault Detection And Isolation ◽  
Channel Measurements ◽  
Engine Model ◽  
Dual Channel ◽  
Sensor Fault Detection ◽  
Baseline System ◽  
On Line

In this paper, an enhanced on-line diagnostic system which utilizes dual-channel sensor measurements is developed for the aircraft engine application. The enhanced system is composed of a nonlinear on-board engine model (NOBEM), the hybrid Kalman filter (HKF) algorithm, and fault detection and isolation (FDI) logic. The NOBEM provides the analytical third channel against which the dual-channel measurements are compared. The NOBEM is further utilized as part of the HKF algorithm which estimates measured engine parameters. Engine parameters obtained from the dual-channel measurements, the NOBEM, and the HKF are compared against each other. When the discrepancy among the signals exceeds a tolerance level, the FDI logic determines the cause of discrepancy. Through this approach, the enhanced system achieves the following objectives: 1) anomaly detection, 2) component fault detection, and 3) sensor fault detection and isolation. The performance of the enhanced system is evaluated in a simulation environment using faults in sensors and components, and it is compared to an existing baseline system.

Integrated Diagnostic System for the Equipment of Power Plants: Part II — The Expert System

2002 ◽  
Author(s):  
J. Kubiak S. ◽  
G. Urquiza B. ◽  
A. Garci´a-Gutierrez
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Vibration Analysis ◽  
Power Plants ◽  
Diagnostic System ◽  
Heat Rate ◽  
Auxiliary Equipment ◽  
On Line ◽  
Knowledge Levels ◽  
Generating Equipment

This paper describes the development of an Expert System for identification of generating equipment faults caused by wearing out of their components, which decrease the efficiency and thus the heat rate of a generating plant. In a sister paper [1], the formulation was presented and the algorithms for the principal equipment were developed. The Expert Systems are based on the above algorithms. Also, in some case a vibration analysis is used jointly with thermodynamic analysis to locate precisely a fault, for example in a case of rubbing which damaged the seals of the turbine and/or compressors. The system is used off-line, however it can be installed on-line with a monitoring system. The Expert Systems identify the faults of the gas turbine, the compressor and the steam turbine. Auxiliary equipment faults are presented in the form of tables also, listing the symptoms and their causes [1]. The knowledge levels and the separate bases are built into the systems.

Experience in Application of Thermal Diagnostics in the Turow Power Station

2003 ◽  
Author(s):  
Andrzej Gardzilewicz ◽  
Jerzy Gluch ◽  
Malgorzata Bogulicz ◽  
Roman Walkowiak ◽  
Malgorzata Najwer ◽  
...  
Keyword(s):  
Thermal Cycle ◽  
Diagnostic System ◽  
Operating Parameters ◽  
Power Station ◽  
Direct Evaluation ◽  
Flow Measurements ◽  
Steam Power ◽  
System Components ◽  
On Line ◽  
Thermal Diagnostics

The thermal diagnostics of a steam power unit in the TUROW Power Station is based on the DIAGAR system and thermal and flow measurements, recorded on-line by the DCS system. Along with direct evaluation of the operating parameters of the thermal cycle, the diagnostic system evaluates degradation of the system components and prognoses economically justified repair actions.

Method for Evaluating Machining Toolholder-Spindle Unbalance Due to Chips at the Toolholder Interface

2010 ◽  
Author(s):  
John Agapiou
Keyword(s):  
Real Time ◽  
Process Monitoring ◽  
Diagnostic System ◽  
Machining Process ◽  
Monitoring Method ◽  
Spindle System ◽  
On Line

Machining process monitoring method is developed for detecting and diagnosis of the presence of chips at the toolholder-spindle interface. Although toolholders can be simply balanced before they are placed in the spindle, there can be some balancing problems remaining when one or more loose machining chips are attached at the toolholder-spindle interface(s) during a tool change. A method is developed by considering the natural and geometric unbalances of the toolholder-spindle system combined with an analysis of the toolholder tilt due to the presence of loose machining chips around the spindle. The method can be integrated on-line as a real-time expert diagnostic system for toolholder tilt due to the presence of loose machining chips at the spindle nose. The expert diagnostic system makes intelligent decisions on toolholder unbalance and concerns with chips at the interface that result in unwanted tilting and vibrations. The tool unbalance algorithm was able to monitor the toolholder tilting according to the results of this study.

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