Semiconductor Bearing Fault Recognition
Nikhita Mishra1, Ipshitta Chaturvedi2, Janhvi Mehta2
1Nikhita Mishra*, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
2Ipshitta Chaturvedi, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
3Janhvi Mehta, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
Manuscript received on October 21, 2021. | Revised Manuscript received on October 24, 2021. | Manuscript published on October 30, 2021. | PP: 21-26 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.F30900810621 | DOI: 10.35940/ijeat.F3090.1011121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Semiconductor manufacturing is consid-ered to be one of the most technologically complicated manufacturing processes. Bearing, being a critical part of the rotating machinery used in the process, plays an essential role as it supports the mechanical rotating body and decreases the friction coefficient. However, extensive use makes this element a target of health degradation, which indirectly causes machine failure. A defective bearing causes approximately 50% of failures in electrical machines. Hence, there arises a dire need for effective fault detection and diagnosis methods to recog-nise fault patterns and help take preventive measures. This paper carries out a comprehensive comparative study of the pre-existing machine learning and deep learning techniques used for diagnosing bearing faults and further devises a novel framework for bearing fault diagnosis based on the results. Unlike the conventional Fault Detection Classifiers (FDC) that operate in the original data space, this algorithm explores the scope for feature extraction and transferability empowered by the deep learning models used.
Keywords: semiconductor manufacturing, defective bearing, machine learning, deep learning.