###### Abstract  
Condition monitoring of gear train assembly has been carried out with vibration signals acquired from an all-terrain vehicle (ATV) gearbox. The location of the defect in the gear was identified based on finite element analysis results. The vibration signals were acquired using an accelerometer under good and simulated fault conditions of the gear. The raw vibration signatures acquired from all the possible conditions of the gear train assembly were processed using the descriptive statistics tool. A set of descriptive statistical features were extracted from the raw vibrational signals. This study used a deep learning algorithm based on the tree family, which includes the decision tree, random forest, and random tree algorithms, to classify gear train conditions. Among the tree family algorithms, the random forest algorithm produced maximum classification accuracy of 99%. The decision rules were used to design an online monitoring system to display the gear condition. This study will help to implement online gear health monitoring in ATVs, ensuring the safety of drivers. ATV; gear train; vibration signals; statistical features; decision tree; confusion matrix +
Footnote †: journal: Medical Engineering  
School of Mechanical Engineering, Vellore Institute of Technology, Chennai 600127, India 1
into small segments of information called features [12]. This study also extracted statistical features using Visual Basic. The extracted features comprise the source data for classifying the condition of the gear train. There are many algorithmic approaches available for analyzing the extracted statistical features. The tree family algorithm is one of the most efficient methods to analyze vibration data with better accuracy. Earlier, among the performances of various deep learning algorithms, decision trees were found to be more accurate in detecting faults [13]. The decision tree algorithm was used to analyze the statistical features of vibration signals for misfires in an internal combustion engine [14; 15]. Additionally, the same classifier algorithm was used to analyze the vibration signals due to the faulty gearbox [16] and cutting tool wear [11]. The accuracy of the statistical analysis obtained after post-pruning the decision tree was higher than the C4.5 decision tree [17]. In another study, the defects in a helical gearbox of a commercial vehicle were recognized using the decision tree algorithms [18]. Gear pitting is the most commonly occurring fault in the power transmission system. A test rig was used for the experiments, and vibration signals were captured. A deep learning algorithm was used to monitor the pitting level [19]. Gear pitting can also be monitored using vibration, current, and AE signals [20]. A detailed summary of studies that applied machine learning algorithms for fault monitoring was reviewed [21]. The application of deep learning algorithms in fault diagnosis of various rotating elements was discussed [22; 23]. Fault diagnosis of a gearbox was performed with a standard gear, a gear with wear and tear, a gear with cracks, a gear with broken teeth, and a gear with tooth deficiency conditions. A deep learning algorithm was used to monitor gear health [24]. As listed in the literature, all the diagnosis applications were reported using test setups. Many studies have been reported for a single-stage spur gear assembly and a single-stage helical gear assembly. A gear was tested in the rig with seven different gear pitting conditions, and AE and vibration signals were captured. Convolutional neural network (CNN) and gated recurrent unit (GRU) networks were integrated to monitor gear health [25]. The condition of the worm gearbox was monitored with thermal images using a deep learning algorithm [26]. However, no studies have been reported to monitor the condition of a real-time two-stage gear train fitted with an ATV. In this study, a gear train assembly has been considered for condition monitoring applications.