Health Monitoring of Chain Sprocket Drive System based on IoT Device and Convolutional Neural Network

This paper proposes a health monitoring method for the early detection of defects in a chain sprocket drive (CSD) system and classification of fault types before a catastrophic failure occurs. In the operation of a CSD system, the early detection of defects is very useful to prevent system failure. Specially, if the type of defect is known, it will be easy to find a method to fix it. In this work, eight fault types in the components of the CSD system, such as the gear tooth, bearings, and shaft of the drive motor, were arbitrarily made and assembled. To detect the fault signals during the CSD system operation, the vibration is measured by an Internet of Things (IoT) device, which features a wireless MEMS accelerometer, Bluetooth function, Wi-Fi function, and battery. The IoT device is mounted on the gearbox housing. The measured one-dimensional vibration time-series is transformed into time-scale images by continuous wavelet transform (CWT). A convolution neural network (CNN) is employed to extract deep features embedded in the images, which are closely related to fault types. To update the learning parameters of the CNN, the RMSprop learning algorithm is applied, and the CNN is trained using 500 image samples. Multiple classification performance of the trained network is tested using 100 image samples. Feature maps for different fault types are obtained from the final convolution layer of the CNN. For the visualization of fault types, t-stochastic neighbor embedding is employed and applied to the feature maps to convert high-dimensional data into two-dimensional data. Two-dimensional features enabled excellent classification of the eight fault types and one normal type.