Version 1
: Received: 25 June 2021 / Approved: 28 June 2021 / Online: 28 June 2021 (09:57:04 CEST)
How to cite:
Mukherjee, S.; Kumar, V.; Sarangi, S. A Brief Study on a Novel Approach for the Gearbox Fault Diagnosis. Preprints2021, 2021060633. https://doi.org/10.20944/preprints202106.0633.v1
Mukherjee, S.; Kumar, V.; Sarangi, S. A Brief Study on a Novel Approach for the Gearbox Fault Diagnosis. Preprints 2021, 2021060633. https://doi.org/10.20944/preprints202106.0633.v1
Mukherjee, S.; Kumar, V.; Sarangi, S. A Brief Study on a Novel Approach for the Gearbox Fault Diagnosis. Preprints2021, 2021060633. https://doi.org/10.20944/preprints202106.0633.v1
APA Style
Mukherjee, S., Kumar, V., & Sarangi, S. (2021). A Brief Study on a Novel Approach for the Gearbox Fault Diagnosis. Preprints. https://doi.org/10.20944/preprints202106.0633.v1
Chicago/Turabian Style
Mukherjee, S., Vikash Kumar and Somnath Sarangi. 2021 "A Brief Study on a Novel Approach for the Gearbox Fault Diagnosis" Preprints. https://doi.org/10.20944/preprints202106.0633.v1
Abstract
Fault diagnosis of the gearbox is a decisive part of the modern industry to find the many gearbox defects like gear tooth crack, chipped or broken, etc. But sometimes, the nonstationary properties of vibration signal and low energy of minimal faults make this procedure very challenging. Previously, many types of techniques have been developed for gearbox condition monitoring. But most of the methods are dealing with conventional techniques of the gearbox condition monitoring, such as time-domain analysis or frequency domain analysis. Most of the conventional methods are not suitable for the nonstationary vibration signal. Thus, this paper presents a novel gearbox fault diagnosis technique using conditional temporal moments and an optimizable support vector machine (SVM). This work also presents an integrated features extraction technique based on the standard features, i.e., statistical and spectral features with the combinations of moment features. The impact of the four conditional temporal moments of each gearbox condition is also presented. This work shows that the proposed method successfully classifies and categorizes the gearbox faults at an early stage.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
