A Survey on Testing and Monitoring Methods for Stator Insulation Systems of Low-Voltage Induction Machines Focusing on Turn Insulation Problems

2008 ◽  
Vol 55 (12) ◽  
pp. 4127-4136 ◽  
Author(s):  
S. Grubic ◽  
J.M. Aller ◽  
Bin Lu ◽  
T.G. Habetler
Keyword(s):  
Low Voltage ◽  
Induction Machines ◽  
Monitoring Methods ◽  
Insulation Systems ◽  
Turn Insulation

A New Concept for Online Surge Testing for the Detection of Winding Insulation Deterioration in Low-Voltage Induction Machines

2011 ◽  
Vol 47 (5) ◽  
pp. 2051-2058 ◽  
Author(s):  
Stefan Grubic ◽  
Jose Restrepo ◽  
Jose M. Aller ◽  
Bin Lu ◽  
Thomas G. Habetler
Keyword(s):  
Preventive Maintenance ◽  
Online Monitoring ◽  
Low Voltage ◽  
Induction Machines ◽  
Electrical Machine ◽  
Insulation System ◽  
Monitoring Method ◽  
New Concepts ◽  
Turn Insulation ◽  
Winding Insulation

A breakdown of the electrical insulation system causes catastrophic failure of the electrical machine and brings large process downtime losses. Preventive maintenance and online monitoring are some of the methods to improve the reliability and to reduce unscheduled downtime. One of the main reasons for the failure of the machine is the breakdown of the stator turn insulation. The offline surge test is the most commonly used offline test to assess the condition of the turn insulation. There is no online monitoring method that is applicable to low-voltage machines and has the same capabilities as the surge test. This paper introduces new concepts to implement an online surge test. The possibilities and limitations of the online surge test are presented, as well as the simulation and experimental results, to validate the concepts.

scholarly journals Degradation Monitoring of Insulation Systems Used in Low-Voltage Electromagnetic Coils under Thermal Loading Conditions from a Creep Point of View

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3696
Author(s):  
Kai Wang ◽  
Haifeng Guo ◽  
Aidong Xu ◽  
Michael Pecht
Keyword(s):  
Creep Deformation ◽  
High Frequency ◽  
Thermal Loading ◽  
Point Of View ◽  
Electromagnetic Coils ◽  
Degradation Monitoring ◽  
Insulation Degradation ◽  
Insulation Systems ◽  
Micro Level ◽  
Turn Insulation

Electromagnetic coils are a key component in a variety of systems and are widely used in many industries. Because their insulation usually fails suddenly and can have catastrophic effects, degradation monitoring of coil insulation systems plays a vital role in avoiding unexpected machine shutdown. The existing insulation degradation monitoring methods are based on assessing the change of coil high-frequency electrical parameter response, whereas the effects of the insulation failure mechanisms are not considered, which leads to inconsistency between experimental results. Therefore, this paper investigates degradation monitoring of coil insulation systems under thermal loading conditions from a creep point of view. Inter-turn insulation creep deformation is identified as a quantitative index to manifest insulation degradation changes at the micro level. A method is developed to map coil high-frequency electrical monitoring parameters to inter-turn insulation creep deformation in order to bridge the gap between the micro-level and macro-level changes during the incipient insulation degradation process. Thermally accelerated tests are performed to validate the developed method. The mapping method helps to determine the physical meaning of coil electrical monitoring parameters and presents opportunities for predictive maintenance of machines that incorporate electromagnetic coils.

scholarly journals Fusion of Vibration and Current Signatures for the Fault Diagnosis of Induction Machines

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Meng-Kun Liu ◽  
Minh-Quang Tran ◽  
Peng-Yi Weng
Keyword(s):  
Wavelet Packet ◽  
Induction Machines ◽  
Fault Detection And Isolation ◽  
Support Vector ◽  
Monitoring Methods ◽  
Time Frequency ◽  
Isolation Techniques ◽  
Level Sensor ◽  
Common Signal ◽  
Air Compressors

Induction machines are widely used in the industry as one of the major actuators, such as water pumps, air compressors, and fans. It is necessary to monitor and diagnose these induction motors to prevent any sudden shut downs caused by premature failures. Numerous fault detection and isolation techniques for the diagnosis of induction machines have been proposed over the past few decades. Among these techniques, motor current signature analysis (MCSA) and vibration analysis are two of the most common signal-based condition monitoring methods. They are often adopted independently, but each method has its strengths and weaknesses. This research proposed a systemic method to integrate the information received from the vibration and current measurements. We applied the wavelet packet decomposition to extract the time-frequency features of the vibration and current measurements and used the support vector machines as classifiers for the initial decision-making. The significant features were identified, and the performances of several classifiers were compared. As a result, the decision-level sensor fusion based on the Sugeno fuzzy integral was proposed to integrate the vibration and current information to improve the accuracy of the diagnosis.

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