Abstract
This paper develops and compares a methodology for gearbox fault diagnosis based on calculus-enhanced energy operator (CEEO) and machine learning. This paper used three directional, i.e. X, Y, and Z, vibration signals of a bevel gearbox to classify the gearbox faults. Therefore, three directional, i.e. X, Y, and Z, vibration signals of a bevel gearbox were extracted based on three different speeds and loading conditions. Then, the raw vibration signal from all directions was pre-processed using CEEO. Next, the CEEO vibration signal was used to compute twelve time features for all directional vibration responses. Afterwards, a random tree (RT) and J48 were used to select the significant time features. Lastly, five different types of neural networks were used to classify the gearbox fault based on specified time features of RT and J48. A comparative assessment of the classification accuracy for gearbox fault diagnosis is also presented. The results showed that the RT performed significantly better than the J48 for gearbox fault classifications based on CEEO vibration signals.
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Mukherjee, S. et al. (2024). A Smart Technique of Gearbox Fault Diagnosis Based on Advanced Signal Processing and Machine Learning. In: Ghoshal, S.K., Samantaray, A.K., Bandyopadhyay, S. (eds) Recent Advances in Industrial Machines and Mechanisms. IPROMM 2022. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-99-4270-1_37
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DOI: https://doi.org/10.1007/978-981-99-4270-1_37
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