[Objective] A method has been proposed to preprocess

predict

and classify the load data of automated guided vehicle (AGV) drive motors

and then predict the health status and failure of AGVs. The aim is to evaluate the health status and failure probability of AGV motors

and improve the maintenance and work strategies of AGVs. [Methods] Based on the experimental collection of AGV motor channel current

vibration signal data

and temperature data

the data is sampled. The proposed method uses autoregressive model and convolutional neural network model to predict the health status and calculate the failure probability of AGV drive motor's current

vibration signal

and temperature rise data trends. The collected and predicted data are converted into symmetrized dot pattern (SDP) images using SDP algorithm for classification detection

thereby determining the health level of the working motor. After classifying the health level of the dataset into three levels based on the temperature rise data of the motor

the autoregressive model and Convolutional network model is used to detect the health status of the driving motor and estimate the probability of the driving motor health level based on the load current and vibration signals of the AGV motor. Based on the determination of the health status

the statistical model can calculate the failure probability of the AGV driving motor. [Results] The data verification through the acceleration test of the driving motor shows that the accuracy of this method in evaluating the health status diagnosis of AGV driving motors reaches an average of about 99.7%

with an accuracy of 100% in classifying the test samples as healthy and unhealthy. When predicting the probability of AGV drive motor failure under planned workload

the root mean square error of AGV motor state data prediction reaches around 0.053. [Conclusion] By applying deep learning methods to the current

vibration

temperature rise and other data of AGV motors

the health level classification of AGV motors (healthy

sub healthy

unhealthy) is achieved. Based on the evaluation results of health status and the calculation of failure probability

reference is provided for the assessment of AGV workload intensity and maintenance plan.