Intelligent monitoring of bolt loosening in industrial customized vehicles using vibration analysis and deep learning

Abstract Bolt loosening in industrial vehicles poses substantial safety and operational risks, highlighting the need for reliable monitoring systems. Despite progress in diagnostic techniques, research specifically focused on detecting bolt loosening in complex rotating joints remains limited. This study introduces a novel approach that integrates vibration analysis with deep learning to enhance fault detection in bolt loosening in a rotating joint of a custom sewer cleaning vehicle transmission. The method uses kurtogram-based frequency domain analysis to convert vibration signals into images, which are then processed using a customized convolutional neural network. This approach eliminates the need for manual feature extraction and enhances diagnostic reliability. The results indicate that a single accelerometer (sensor A1) outperforms multi-sensor configurations. Furthermore, measurements from the accelerometer showed the highest accuracy, demonstrating that longitudinal vibrations are the most reliable for detecting bolt loosening. This study contributes to predictive maintenance and intelligent fault detection, offering a scalable, high-performance solution for monitoring bolt loosening. The integration of kurtogram-based spectral analysis and deep learning paves the way for next-generation autonomous fault detection systems, capable of real-time deployment in dynamic and complex industrial settings.