Optimizing Failure Diagnosis in Helical Gear Transmissions with Stochastic Gradient Descent Logistic Regression using Vibration Signal Analysis for Timely Detection

Vibration analysis plays a pivotal role in the initial identification and reduction of defects in helical gear transmissions, underscoring the significance of precise fault detection. This research paper presents a comprehensive examination of vibration analysis as a means of detecting tooth wear faults in helical gear transmissions. The study utilizes the logistic regression (LR) algorithm and stochastic gradient descent (SGD) optimizer within a machine learning framework. A thorough examination of existing literature elucidated the importance of gear fault diagnosis and identified shortcomings in previous research efforts. To overcome these constraints, the suggested methodology incorporates sophisticated vibration analysis methodologies, techniques for enhancing data quality, and algorithms based on machine learning. Experimental trials, from the acquired vibration signals, conducted on a manufactured helical gear transmission system provide evidence of the effectiveness of the methodology, leading to a substantial enhancement in classification accuracy from 30.3% using the LR-based model to 97.8% using the LR-SGD-based model. The examination of the confusion matrix and ROC analysis provides additional evidence for the improved classification efficiency, as indicated by a substantial increase in the area under the curve from 0.653 to 0.997 in the LR and LR-SGD models, respectively. The results of this study demonstrate significant progress in the field of gear fault diagnosis, offering a sturdy and dependable methodology for practical implementations. Future research directions may encompass the augmentation of the dataset, investigation of alternative machine learning algorithms, and integration of supplementary diagnostic techniques to further amplify fault diagnosis capabilities.