AGV Status Monitoring and Fault Diagnosis based on CNN

Abstract

In order to solve the problem of AGV fault detection system’s complexion and low accuracy, a convolutional neural network (CNN) based on the status monitoring and fault diagnosis method for automatic guided vehicle (AGV) is proposed. Firstly, the vibration signals of the core components of AGV are converted into two-dimensional (2D) images. Secondly, 2D images are input into convolution neural network for training. Finally, the trained model is used to monitor the running status of AGV and identify faults. The results show that the proposed method can effectively monitor the status of AGV in operation.

Aircraft Engine Fault Diagnosis Model Based on 1DCNN-BiLSTM with CBAM

“…To verify the effectiveness of the proposed model, the results are compared with traditional shallow machine learning models, such as Support Vector Machine (SVM) [23][24][25][26] and Random Forest (RF) [25,27,28], and deep learning models: Fully Connected Neural Network (FNN) [29], Recurrent Neural Network (RNN) [30], 1DCNN [7,10,11], LSTM [17], and BiLSTM [18] models. The average accuracy, Macro-precision, Macro-recall, and F1-Score on the test set are shown in Table 6.…”

“…Raw data can be directly input into the model for training, allowing the exploration of temporal dependencies between data and addressing the limitations of previous models. Wang et al proposed a novel method that combines features from multiple sensors using 1DCNN for predicting bearing faults [7]. Du X et al proposed a fault diagnosis method for rotating machinery using a sequence Transformer model based on SPBO-SDAE and attention mechanism.…”