Early Fault Identification of Two-Wheeler Wheel Fasteners Using Mechanical Vibrations

Abstract

Fastener loosening in two-wheeler wheels is a major safety risk that can cause instability and accidents if not detected early, making manual inspection methods unreliable during actual riding conditions. To address this issue, this project introduces a vibration-based early fault detection system using a MEMS accelerometer to continuously monitor wheel vibration patterns, where loosened fasteners produce abnormal signatures compared to properly tightened conditions. These vibration signals are processed by an ESP32 microcontroller, which analyzes the data in real time using threshold-based detection to identify potential loosening. Once abnormal vibrations are detected, the system activates a buzzer and relay module to alert the rider immediately, while a GSM module sends an SMS notification to a registered mobile number for remote awareness. Experimental results validate that vibration monitoring is an effective and low-cost method for early identification of wheel fastener faults, providing a reliable and easily installable solution that enhances two-wheeler safety through preventive maintenance and real-time fault detection.

Introduction

Two-wheelers such as motorcycles and scooters are widely used, especially in developing countries like India, due to their affordability, fuel efficiency, and convenience for daily commuting. However, rider safety remains a major concern, particularly due to mechanical failures. One critical but often overlooked issue is wheel fastener loosening. Wheel bolts or nuts secure the wheel to the vehicle, but continuous vibrations, road conditions, braking forces, and dynamic loads can gradually reduce their clamping force. If loosening is not detected early, it can lead to wheel wobbling, instability, severe vibrations, or even complete wheel detachment, which may result in serious accidents.

Traditional inspection methods rely on manual checks during periodic servicing, which are unreliable because loosening may occur between service intervals. To address this limitation, the project proposes an ESP32-enabled vibration monitoring system for early detection of wheel fastener loosening in two-wheelers. The system uses a MEMS accelerometer (MPU6050) mounted near the wheel hub to continuously monitor vibration levels. An ESP32 microcontroller processes the vibration data and compares it with predefined threshold values derived from baseline readings when the fasteners are properly tightened. If abnormal vibration patterns indicating loosening are detected, the system immediately alerts the rider through a buzzer and sends an SMS notification via a GSM module.

The methodology involves studying vibration behavior under normal and loosened conditions, selecting and mounting the accelerometer sensor, interfacing it with the ESP32, developing firmware for real-time monitoring, and integrating an alert system and wireless communication. The complete hardware system is assembled in a compact enclosure and tested under different riding conditions. Calibration and optimization are performed to improve accuracy, sensitivity, and reliability.

The literature review highlights previous research on vibration-based fault detection, IoT-based vehicle monitoring systems, MEMS sensor applications, and embedded safety alert mechanisms. These studies confirm that changes in vibration amplitude and frequency can effectively indicate early-stage mechanical faults, including bolt loosening.

Experimental testing showed that when fasteners were properly tightened, vibration levels remained within safe limits and the system displayed a normal status. When bolts were intentionally loosened, sudden spikes and irregular vibration patterns were detected. Once the vibration exceeded predefined thresholds, the system successfully triggered alerts through a buzzer, displayed warnings, and activated IoT notifications. The results demonstrated quick response, stable operation, and minimal false alarms.

Overall, the proposed system provides a low-cost, reliable, and real-time monitoring solution for detecting wheel fastener loosening in two-wheelers. By continuously analyzing vibration signals and providing early warnings, the system helps prevent mechanical failures and improves rider safety through smart embedded monitoring technology.

Conclusion

The proposed system for early fault identification of two-wheeler wheel fasteners using mechanical vibrations has been successfully designed and implemented. By using vibration monitoring through MEMS sensors and ESP32 microcontroller, the system effectively detects abnormal vibration patterns caused by loosened wheel bolts. This approach helps in reducing the risk of accidents caused by undetected mechanical failures and improves rider safety. The following are the conclusions from the evaluation and testing carried out on the system: The vibration-based monitoring system successfully differentiates between normal and loosened bolt conditions by analyzing vibration amplitude variations. The MPU6050 sensor effectively captures vibration spikes generated during early-stage bolt loosening, ensuring timely fault identification. The ESP32 microcontroller processes real-time vibration data accurately and triggers alert mechanisms without significant delay. The buzzer and display alert system provides immediate warning to the rider when unsafe vibration thresholds are exceeded. The integration of IoT/GSM communication enables remote alert notification, enhancing preventive maintenance and safety awareness. The system operates reliably under simulated road vibration conditions with minimal false triggering. The proposed setup is compact, cost-effective, and suitable for integration into two-wheeler wheel assemblies for real-time safety monitoring. Overall, the developed system proves that vibration-based monitoring is an effective method for early detection of wheel fastener loosening, contributing to improved vehicle safety and preventive maintenance in two-wheelers.

References

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Copyright

Copyright © 2026 Thangapandian P, Kishore S, Sreejay A, Mohanraj P, Deepasri R. 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.