Fault Diagnosis of Heavy-Loaded AGV Based on Digital Mirror

Automated guided vehicles (AGVs) have emerged as crucial machinery in enterprise production and transportation as intelligent factories have gained traction. This study proposes a multi-source data enhancement fusion convolutional neural network (ME-CNN) approach for the mechanical fault diagnosis of heavy-duty AGVs. Specifically, an improved stacked autoencoder (SAE) was employed to augment the available data and address the issue of insufficient data samples during fault diagnosis. Subsequently, the augmented data were processed using a continuous wavelet transform (CWT). This method employs a combination of diverse measurement signals and data sources to facilitate a more precise and resilient diagnosis of heavy-duty AGVs. In addition, to further promote the coverage of smart factories, this method is combined with digital mirroring, and the virtual platform receives data to monitor the status of heavy-loaded AGVs in real-time and determine the type of fault. Following testing, the system response time was less than 500 ms, allowing for the timely diagnosis of faults. Finally, the validity of the method was verified by comparing a factory-heavy load AGV with various other algorithms, resulting in an accuracy rate of 97.0% for fault diagnosis.