Abstract
Bearing is a critical component in an electrical machine which get continuous monitoring and included in scheduled predictive maintenance. The temperature of the bearing is a valuable information that may allow early fault detection, lubrication assessment, and overloading indication of the system driven. Using the temperature measurement of the bearing and comparing it to a baseline temperature in real time will allow early warning of any eventual fault. This paper proposes a thermal model for the bearing in a brushed DC machine, developed using transfer function that will predict the temperature increase contributed specifically by speed variation. The transfer function was found by identification using experimental temperature of the bearing at a speed ranging from 20 to 100% of its rated speed while being at no load. The result shows that the first-order transfer function was found to be the best with a model identification MSE of less than 0.23. The slight variation on the poles of the system indicates that the thermal system of the bearing inside an electrical machine does not obey exactly the LTI hypothesis.
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Acknowledgements
The authors would like to thank Universiti Malaysia Pahang for the financial support under Internal Research grant RDU220302 and the facilities in the Electrical Drive System Laboratories.
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Appendix
Appendix
Poles | Speed (%) | Transfer function | MSE |
|---|---|---|---|
1 | 20 | 0.0001494 s + 0.0003315 | 0.1271 |
40 | 0.0001693 s + 0.0005086 | 0.1185 | |
60 | 0.0001279 s + 0.0004985 | 0.2252 | |
80 | 0.0001028 s + 0.0005841 | 0.1599 | |
100 | 6.227e−05 s + 0.0005284 | 0.1198 | |
2 | 20 | 8.146e−07 s2 + 0.02468 s + 1.324e−07 | 4.861 |
40 | 2.077e−05 s2 + 0.1253 s + 6.245e−−05 | 0.1275 | |
60 | 6.931e−06 s2 + 0.05462 s + 2.701e−05 | 0.2248 | |
80 | 2.502e−06 s2 + 0.02476 s + 1.423e−05 | 0.1537 | |
100 | 3.006e−06 s2 + 0.04873 s + 2.551e−05 | 0.1195 | |
3 | 20 | (− 4.584e−11) s3 + 0.002856 s2 + 2.585e−06 s + 4.787e−18 | 19.06 |
40 | 3.575e−10 s3 + 0.02435 s2 + 1.417e−05 s + 1.086e−09 | 0.1148 | |
60 | 4.878e−09 s3 + 0.09851 s2 + 7.856e−05 s + 1.945e−08 | 0.2024 | |
80 | 2.458e−10 s3 + 0.02295 s2 + 1.439e−05 s + 1.378e−09 | 0.3212 | |
100 | 3.267e−05 s3 + 14.75 s2 + 0.5317 s + 0.0002773 | 0.1194 | |
4 | 20 | 1.039e−12 s4 + 0.01212 s3 + 4.786e−05 s2 + 1.076e−07 s + 1.259e−20 | 9.851 |
40 | 1.144e−09 s4 + 0.06602 s3 + 0.0006406 s2 + 7.035e−06 s + 3.438e−09 | 0.1165 | |
60 | 2.975e−11 s4 + 0.04506 s3 + 0.0006952 s2 + 5.139e−07 s + 1.191e−10 | 0.2062 | |
80 | 3.338e−11 s4 + 0.01291 s3 + 0.000101 s2 + 3.738e−07 s + 1.9e−10 | 0.1488 | |
100 | 5.133e−13 s4 + 0.01203 s3 + 4.973e−05 s2 + 3.445e−08 s + 4.182e−12 | 0.1548 |
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Jahak, M.S.M., Rasid, M.A.H. (2024). Modeling Bearing Temperature of DC Machine in No-Load Condition Using Transfer Function. In: Mohd. Isa, W.H., Khairuddin, I.M., Mohd. Razman, M.A., Saruchi, S.'., Teh, SH., Liu, P. (eds) Intelligent Manufacturing and Mechatronics. iM3F 2023. Lecture Notes in Networks and Systems, vol 850. Springer, Singapore. https://doi.org/10.1007/978-981-99-8819-8_19
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