Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning

Li, Xueyi; Li, Jialin; Qu, Yongzhi; He, David

doi:10.1016/j.cja.2019.04.018

Cited by 81 publications

(31 citation statements)

References 19 publications

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“…Equipment Based on Neural Network Algorithm. Firstly, the parameters related to the health status of medical imaging equipment are extracted by calculating the characteristic parameters of the vibration signal characterization signal, which are used as the input characteristics of the health degree prediction model [19]. When the medical machinery is in the same healthy state, its vibration signal belongs to stable signal [20].…”

Section: Health Evaluation Technology Of Medical Imagingmentioning

confidence: 99%

Health Evaluation and Fault Diagnosis of Medical Imaging Equipment Based on Neural Network Algorithm

Zhao

Jiang

Gao

2021

Computational Intelligence and Neuroscience

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In recent years, high-precision medical equipment, especially large-scale medical imaging equipment, is usually composed of circuit, water, light, and other structures. Its structure is cumbersome and complex, so it is difficult to detect and diagnose the health status of medical imaging equipment. Based on the vibration signal of mechanical equipment, a PLSR-DNN hybrid network model for health prediction of medical equipment is proposed by using partial least squares regression (PLSR) algorithm and deep neural networks (DNNs). At the same time, in the diagnosis of medical imaging equipment fault, the paper proposes to use rough set to screen the fault factors and then use BP neural network to classify and identify the fault and analyzes the practical application effect of the two technologies. The results show that the PLSR-DNN hybrid network model for health prediction of medical imaging equipment is basically consistent with the actual health value of medical equipment; medical imaging equipment fault diagnosis technology is based on rough set and BP neural network. In the test set, the sensitivity, specificity, and accuracy of medical imaging equipment fault identification are 75.0%, 83.3%, and 85.0%. The above results show that the proposed health prediction method and fault diagnosis method of medical imaging equipment have good performance in health prediction and fault diagnosis of medical equipment.

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Section: Health Evaluation Technology Of Medical Imagingmentioning

confidence: 99%

Health Evaluation and Fault Diagnosis of Medical Imaging Equipment Based on Neural Network Algorithm

Zhao

Jiang

Gao

2021

Computational Intelligence and Neuroscience

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“…In the past few years, there has been increasing research interest in fault detection techniques using vibration signals and deep learning [30,31,32,33,34,35,36]. For instance, Li et al [37] proposed augmented deep sparse autoencoder to diagnose the gear pitting condition using raw signal of vibration. Classification of bearing faults using CNN and vibration spectrum imaging was studied in [38], where temporal vibration signals were extracted using a time-moving segmentation window.…”

Section: B Deep Learning For Machine Fault Diagnosis Using Vibration Datamentioning

confidence: 99%

Vision-Based Fault Diagnostics Using Explainable Deep Learning With Class Activation Maps

et al. 2020

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In the era of the fourth industrial revolution (Industry 4.0) and the Internet of Things (IoT), real-time data is enormously collected and analyzed from mechanical equipment. By classifying and characterizing the measured signals, the fault condition of mechanical components could be identified. However, most current health monitoring techniques utilize time-consuming and labor-intensive feature engineering, i.e., feature extraction and selection, that are carried out by experts. This paper, on the contrary, deals with an automatic diagnosis method of machine monitoring using a convolutional neural network (CNN) with class activation maps (CAM). A class activation map enables us to discriminate the fault region in the images, thus allowing us to localize the fault precisely. The goal of the paper is to demonstrate how CNN and CAM could be employed to real-world vibration video to characterize the machine's status, representing normal or fault conditions. The performance of the proposed model is validated with a baseexcited cantilever beam dataset and a water pump dataset. This paper presents a novel industrial application by developing a promising method for automatic machine condition-based monitoring.INDEX TERMS Convolutional neural network, class activation maps, discriminative region, fault detection, mechanical component, explainable AI

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“…The advantage of DAE is that it can learn useful information from damaged data, and CAE learns more stable feature representations through penalty items. The latest AE model also combines with the GAN network to generate labeled samples [28][29][30] and also embeds the semisupervised learning method into the VAE model [31,32]. The Recurrent Neural Network has no advantage in classification, and it is more commonly used in mechanical life prediction.…”

Section: Introductionmentioning

confidence: 99%

Distilling the Knowledge of Multiscale Densely Connected Deep Networks in Mechanical Intelligent Diagnosis

Wang

Chen

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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At present, deep neural network (DNN) technology is often used in intelligent diagnosis research. However, the huge amount of calculation of DNN makes it difficult to apply in industrial practice. In this paper, an advanced multiscale dense connection deep network MSDC-NET is designed. A well-designed multiscale parallel branch module is used in the network. This module can greatly improve the acceptance domain of MSDC-NET, so as to learn useful information from input samples more effectively. Based on the inspiration of Densely Connected Convolutional Networks, MSDC-NET designed a similar dense connection technology, so that the model will not have the problem of gradient vanishing because of the deep network. The experimental data of MSDC-NET on MFPT, SEU, and Pu datasets show that our method has higher performance than other latest technologies. At the same time, we carried out knowledge distillation based on the high-precision classification level of MSDC-NET, which makes the diagnosis ability and robustness of the lightweight CNN model improve significantly.

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Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning

Cited by 81 publications

References 19 publications

Health Evaluation and Fault Diagnosis of Medical Imaging Equipment Based on Neural Network Algorithm

Health Evaluation and Fault Diagnosis of Medical Imaging Equipment Based on Neural Network Algorithm

Vision-Based Fault Diagnostics Using Explainable Deep Learning With Class Activation Maps

Distilling the Knowledge of Multiscale Densely Connected Deep Networks in Mechanical Intelligent Diagnosis

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