## 3 Experimental Procedure  
The two-stage gear train assembly was modeled with the dimensions of the gears, as shown in Table 1, to identify the gear failure locations. Figure 5 shows the finite element analysis (FEA) of a gear. After the application of load, the bottom of the teeth becomes affected. FEA results reveal that obtained von Mises stress is in the permissible range. Based on this simulated study, the location of the fault was identified, and different faults mentioned in Table 2 were simulated. The size of the defects induced in the gears is slightly larger than those that will present themselves in a real-time scenario after being under load for a prolonged period. Cracks originating at the gear surface may lead to failure modes such as pitting and tooth breakage. Furthermore, the contact load on the flank surface induces stresses at the bottom of the tooth, which may lead to crack initiation. Over time the sub-surface crack propagation may lead to gear failure, referred to as \"tooth breakage\" [27]. Different crack growth models can estimate crack propagation [28].  
Figure 4: (a) Sensor mounting (front view). (b) Sensor mounting (top view).  
Most often, the gear fails due to fatigue and overload. The most common form of failure is actual breakage, and other failure modes include surface fatigue, pitting, normal and abnormal wear, and plastic flow [29]. As per our knowledge, in ATVs, the applied load is significantly less. Hence, the occurrence of the abovementioned failures may be significantly less. The different defects have been collected from the authorized service person. Based on the data collected, the following fault conditions, namely spur and helical gear in healthy condition (SHHC), spur gear with crack (SGWC), spur gear with broken tooth (SGBT), helical gear with crack (HGWC), helical gear with broken tooth (HGBT), and helical and spur gear with broken tooth (HSBT) were simulated on the gear. Figure 6a-d show the instigated defects for spur and helical gears.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Parameters** & **Spur Pinion** & **Spur Gear** & **Helical Pinion** & **Helical Gear** \\ \hline No. of teeth & 18 & 54 & 18 & 54 \\ Module (mm) & 2 & 2 & 2 & 2 \\ Normal Pressure angle & 20 & 20 & 20 & 20 \\ (Degree) & - & - & 20 & 20 \\ Health angle (Degree) & 14 & 14 & 23 & 20 \\ Tooth Thickness (mm) & EN353 & EN353 & EN353 & EN353 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spur and helical gear parameters.  
Figure 5: Finite element analysis of spur gear.  
In the experimental setup, different gear sets were used for every condition of the gears. The combination of gears used in the gearbox is mentioned in Table 2. The vibration signals were taken for three engine shaft speeds (1500, 1800, and 2000 RPM). The loading condition (0 N and 280 N) provided for 1800 and 2000 RPM to account for the rolling resistance encountered during the runtime of the vehicle.  
The vibration signals were collected with the help of an accelerometer (a piezo-ceramic sensing element) placed on the casing of the gear train assembly. The signals were taken after allowing the gearbox to run for a little while. The sample length was arbitrarily chosen as 10,000, and the sampling frequency was set at 25 kHz for all conditions. Generally, a large sample size ensures more accurate statistical measures. On the contrary, a large sample size would mean longer computational time. To find a solution to this problem, a sample size of 10,000 was decided. Many trial runs were conducted at one set speed, and the vibrational signals were recorded when the engine attained the optimum running  
Figure 6: **(a)** Crack on the tooth of spur gear. (b) Spur gear with a broken tooth. (c) Crack on the tooth of helical gear. (d) Helical gear with a broken tooth.  
conditions. A hundred data files containing 10,000 samples were considered for the analysis of each simulated condition of the gearbox.