Notice of Violation of IEEE Publication Principles - On-line insulation diagnostic system and off-line PD monitoring with HVAC testing

2008 ◽  
Author(s):  
Yong-Sung Choi ◽  
Ju-Ho Yun ◽  
Kyung-Sup Lee
Keyword(s):  
Diagnostic System ◽  
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 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.

Functions and Theoretical Basis of Partial Discharge On-Line Monitoring System for High-Voltage Cables

2014 ◽  
Vol 672-674 ◽  
pp. 854-857
Author(s):  
Dan Pang ◽  
Xi Lin Zhang ◽  
Zhen Hao Wang ◽  
Dan Zhang ◽  
Xiao Juan Han
Keyword(s):  
High Voltage ◽  
Partial Discharge ◽  
Diagnostic System ◽  
Signal Acquisition ◽  
Xlpe Cable ◽  
Remote Diagnostics ◽  
Testing Technology ◽  
Discharge Status ◽  
On Line ◽  
Frequency Current

A partial discharge (PD) on-line monitoring and positioning system for high-voltage cables based on double-ended testing technology is developed. The hardware of this system includes a high-frequency current sensor, site PD signal acquisition and PD monitoring server. In order to achieve real-time monitoring and remote diagnostics of the XLPE cable partial discharge status, the designed software system is divided into front-end control system and remote diagnostic system. And finally, the correctness and effectiveness of the system is verified by XLPE cable partial discharge testing.

Mine Fan Fault Diagnosis Based on EMD and SVM

2011 ◽  
Vol 63-64 ◽  
pp. 449-452 ◽  
Author(s):  
Jun Fa Leng ◽  
Shuang Xi Jing
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Pattern Classification ◽  
Diagnostic System ◽  
New Method ◽  
Intelligent Diagnosis ◽  
Linear Pattern ◽  
Non Linear ◽  
On Line ◽  
Integrative Method

In this research, a new method based on EMD and SVM for mine fan fault diagnosis is introduced. With EMD, fault feature can be extracted quickly and accurately, and taken as the input samples for SVM with the outstanding non-linear pattern classification performances. 5 two-class SVM classifiers are designed in order to classify and diagnosis 5 typical fault patterns of mine fan. The result of this research shows that the integrative method of EMD and SVM is very fit for the intelligent diagnosis and fault patterns recognition, and it will lead to the possible development of an automated and on-line mine fan condition monitoring and diagnostic system.

Intelligent Real-Time Predictive Diagnostics for Cutting Tools and Supervisory Control of Machining Operations

1993 ◽  
Vol 115 (3) ◽  
pp. 268-277 ◽  
Author(s):  
K. Ramamurthi ◽  
C. L. Hough
Keyword(s):  
Real Time ◽  
Metal Cutting ◽  
Failure Modes ◽  
Cutting Tools ◽  
Diagnostic System ◽  
Training Time ◽  
Process Diagnosis ◽  
Modes Of Failure ◽  
On Line ◽  
Cutting Processes

Machining economics may be improved by automating the replacement of cutting tools. In-process diagnosis of the cutting tool using multiple sensors is essential for such automation. In this study, an intelligent real-time diagnostic system is developed and applied towards that objective. A generalized Machining Influence Diagram (MID) is formulated for modeling different modes of failure in conventional metal cutting processes. A faster algorithm for this model is developed to solve the diagnostic problem in real-time applications. A formal methodology is outlined to tune the knowledge base during training with a reduction in training time. Finally, the system is implemented on a drilling machine and evaluated on-line. The on-line response is well within the desired response time of actual production lines. The instance and the accuracy of diagnosis are quite promising. In cases where drill wear is not diagnosed in a timely manner, the system predicts wear induced failure and vice versa. By diagnosing at least one of the two failure modes, the system is able to prevent any abrupt failure of the drill during machining.

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