Impedance-based Health Monitoring of Electromagnetic Coil Insulation Subjected to Corrosive Deterioration

N. Jordan Jameson, Michael H. Azarian, and Michael Pecht
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_16_053.pdf2.92 MBSeptember 7, 2016 - 9:47am

Electromagnetic coils are widely used components in a variety of industries and systems, and their failure can lead to catastrophic failure of the system in which they are placed. Past studies have revealed the electromagnetic coil insulation to be a weakness, and there are presently no available methods to detect degradation in the coil insulation in-situ. Prior work in the AC motor community on twisted pairs of magnet wire has shown that insulation capacitance measurements can reveal useful diagnostic information. Yet, no studies have tracked the coil impedance spectrum over an aging/degradation period, no methods are available to identify frequencies in the impedance spectrum to be used, nor is there discussion concerning methods to employ coil impedance to determine a degradation mechanism. This paper develops an approach of detection of the aging of insulation in low-voltage electromagnetic coils when subjected to corrosive environmental conditions by assessing changes in impedance responses. The complex impedance was resolved into its real and imaginary parts and the Spearman correlation coefficient was used to find regions of interest within the impedance spectrum. The results indicate that real and imaginary impedance provide information that can assist in condition-based maintenance procedures for electromagnetic coils. Furthermore, by incorporating frequency-correlation analysis, the individual frequencies which provide diagnostic and prognostic information can be identified.

Publication Year: 
2016
Publication Volume: 
7
Publication Control Number: 
053
Page Count: 
7
Submission Keywords: 
electromagnetic coil
impedance monitoring
insulation degradation
correlation analysis
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Industrial applications
Sensors
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