The multilabel fault diagnosis model of bearing based on integrated convolutional neural network and gated recurrent unit

Abstract

PurposeIntelligent diagnosis of equipment faults can effectively avoid the shutdown caused by equipment faults and improve the safety of the equipment. At present, the diagnosis of various kinds of bearing fault information, such as the occurrence, location and degree of fault, can be carried out by machine learning and deep learning and realized through the multiclassification method. However, the multiclassification method is not perfect in distinguishing similar fault categories and visual representation of fault information. To improve the above shortcomings, an end-to-end fault multilabel classification model is proposed for bearing fault diagnosis.Design/methodology/approachIn this model, the labels of each bearing are binarized by using the binary relevance method. Then, the integrated convolutional neural network and gated recurrent unit (CNN-GRU) is employed to classify faults. Different from the general CNN networks, the CNN-GRU network adds multiple GRU layers after the convolutional layers and the pool layers.FindingsThe Paderborn University bearing dataset is utilized to demonstrate the practicability of the model. The experimental results show that the average accuracy in test set is 99.7%, and the proposed network is better than multilayer perceptron and CNN in fault diagnosis of bearing, and the multilabel classification method is superior to the multiclassification method. Consequently, the model can intuitively classify faults with higher accuracy.Originality/valueThe fault labels of each bearing are labeled according to the failure or not, the fault location, the damage mode and the damage degree, and then the binary value is obtained. The multilabel problem is transformed into a binary classification problem of each fault label by the binary relevance method, and the predicted probability value of each fault label is directly output in the output layer, which visually distinguishes different fault conditions.

Research on the mechanical fault diagnosis method based on sound signal and IEMD-DDCNN

“…Zhang et al proposed a fault diagnosis method combining EMD, singular value decomposition and deep convolutional network and verified the superiority of this method (Zhang et al, 2020a, b). Han et al proposed a model that integrates convolutional neural network and gated recurrent unit (CNN-GRU) to classify faults, and the experimental results show that the model can classify faults visually with higher accuracy (Han et al, 2022). In addition to these, there are research papers (Levent et al, 2019;Ma et al, 2019;Patel, 2022;Latha and Rooban, 2023).…”

“…Han et al . proposed a model that integrates convolutional neural network and gated recurrent unit (CNN-GRU) to classify faults, and the experimental results show that the model can classify faults visually with higher accuracy (Han et al ., 2022). In addition to these, there are research papers (Levent et al , 2019; Ma et al , 2019; Patel, 2022; Latha and Rooban, 2023).…”

Steel surface defect classification approach using an All-optical Neuron-based SNN with attention mechanism

“…In recent years, deep learning-based algorithms, especially convolutional neural networks (CNN), have achieved remarkable success in image classification tasks. CNN-based surface defect classification is also widely adopted in various industries (Zhu et al, 2022;Han et al, 2022). Gfa et al proposed a fast and robust lightweight network model based on SqueezeNet, which emphasizes the learning of the underlying features and adds the multi-receptive field (MRF) module, thus achieving accurate identification of defect types using a small number of defect samples (Gfa et al, 2019).…”

“…In recent years, deep learning-based algorithms, especially convolutional neural networks (CNN), have achieved remarkable success in image classification tasks. CNN-based surface defect classification is also widely adopted in various industries (Zhu et al , 2022; Han et al , 2022). Gfa et al .…”

Fault Detection in Three-phase Induction Motor based on Data Acquisition and ANN based Data Processing

“…A Convolutional Neural Network (abbreviated as CNN) algorithm that can take a set of input data, assign importance to various aspects or characteristics in the data and be able to make a distinction between them. Such algorithms are used for fault determination for real-time monitoring [1], for bearings monitoring in electric motors [17,36] or for fault detection based on vibration [16]. CNN is used also for processing signals with a significant noise component [32].…”