scholarly journals Fault Diagnosis of Rolling Bearings Based on a Residual Dilated Pyramid Network and Full Convolutional Denoising Autoencoder

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5734 ◽  
Author(s):  
Hongmei Shi ◽  
Jingcheng Chen ◽  
Jin Si ◽  
Changchang Zheng
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Fine Tuning ◽  
Noise Robustness ◽  
Intelligent Fault Diagnosis ◽  
Denoising Autoencoder ◽  
Rolling Bearings ◽  
Convolutional Network ◽  
Time Frequency ◽  
Diagnosis Algorithm

Intelligent fault diagnosis algorithm for rolling bearings has received increasing attention. However, in actual industrial environments, most rolling bearings work under severe working conditions of variable speed and strong noise, which makes the performance of many intelligent fault diagnosis methods deteriorate sharply. In this regard, this paper proposes a new intelligent diagnosis algorithm for rolling bearing faults based on a residual dilated pyramid network and full convolutional denoising autoencoder (RDPN-FCDAE). First, a continuous wavelet transform (CWT) is used to convert original vibration signals into time-frequency images. Secondly, a deep two-stage RDPN-FCDAE model is constructed, which is divided into three parts: encoding network, decoding network and classification network. In order to obtain efficient expression of data denoising feature of encoding network, time-frequency images are first input into the encoding-decoding network for unsupervised pre-training. Then pre-trained coding network and classification network are combined into residual dilated pyramid full convolutional network (RDPFCN) for parameter fine-tuning and testing. The proposed method is applied to bearing vibration datasets of test rig with different speeds and noise modes. Compared with representative machine learning and deep learning method, the results show that the algorithm proposed is superior to other methods in diagnostic accuracy, noise robustness and feature segmentation ability.

Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings

2020 ◽  
Vol 88 ◽  
pp. 106060 ◽  
Author(s):  
Haiping Zhu ◽  
Jiaxin Cheng ◽  
Cong Zhang ◽  
Jun Wu ◽  
Xinyu Shao
Keyword(s):  
Fault Diagnosis ◽  
Intelligent Fault Diagnosis ◽  
Denoising Autoencoder ◽  
Rolling Bearings

scholarly journals A Novel Intelligent Fault Diagnosis Method for Rolling Bearings Based on Wasserstein Generative Adversarial Network and Convolutional Neural Network under Unbalanced Dataset

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6754
Author(s):  
Hongtao Tang ◽  
Shengbo Gao ◽  
Lei Wang ◽  
Xixing Li ◽  
Bing Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rolling Bearing ◽  
Environmental Noise ◽  
Experimental Result ◽  
Grayscale Image ◽  
Intelligent Fault Diagnosis ◽  
Rolling Bearings ◽  
Generative Adversarial Network

Rolling bearings are widely used in industrial manufacturing, and ensuring their stable and effective fault detection is a core requirement in the manufacturing process. However, it is a great challenge to achieve a highly accurate rolling bearing fault diagnosis because of the severe imbalance and distribution differences in fault data due to weak early fault features and interference from environmental noise. An intelligent fault diagnosis strategy for rolling bearings based on grayscale image transformation, a generative adversative network, and a convolutional neural network was proposed to solve this problem. First, the original vibration signal is converted into a grayscale image. Then more training samples are generated using GANs to solve severe imbalance and distribution differences in fault data. Finally, the rolling bearing condition detection and fault identification are carried out by using SECNN. The availability of the method is substantiated by experiments on datasets with different data imbalance ratios. In addition, the superiority of this diagnosis strategy is verified by comparing it with other mainstream intelligent diagnosis techniques. The experimental result demonstrates that this strategy can reach more than 99.6% recognition accuracy even under substantial environmental noise interference or changing working conditions and has good stability in the presence of a severe imbalance in fault data.

Fault diagnosis of rolling bearing based on multimodal data fusion and deep belief network

2021 ◽  
pp. 095440622110084
Author(s):  
Defeng Lv ◽  
Huawei Wang ◽  
Changchang Che
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Rolling Bearing ◽  
Typical Feature ◽  
Deep Belief Network ◽  
Fine Tuning ◽  
Multimodal Data ◽  
Belief Network ◽  
Time Frequency ◽  
Multimodal Data Fusion

Aiming at raw vibration signal of rolling bearing with long time series, a fault diagnosis model based on multimodal data fusion and deep belief network is proposed in this paper. First, multimodal data composed of artificial features and model features can be obtained by time-frequency domain analysis and unsupervised learning based on restricted Boltzmann machine (RBM). Second, canonical correlation analysis method is used to extract the typical feature pairs from the multimodal data to realize the feature-level multimodal data fusion. Third, deep belief network is applied to extract deep feature mapping between typical feature pairs and fault types. After greedy layer-wise pre-training and fine-tuning, it is available to achieve the trained model for fault diagnosis of rolling bearing. Typical rolling bearing datasets are used to testify the effectiveness of the proposed method. It is verified that the robustness and accuracy of the proposed method are superior to common methods.

A Practical Method for Multi-Fault Diagnosis of Rolling Element Bearings Using Discrete Wavelet Transform and Support Vector Machine

2006 ◽  
Author(s):  
Saeed Abbasion ◽  
Anoushiravan Farshidianfar ◽  
Nilgoon Irani ◽  
Mohamad Bashari
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Practical Method ◽  
Rolling Bearing ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Good Resolution ◽  
Rolling Bearings ◽  
Time Frequency

Due to importance of rolling bearings as one of the most widely used industrial machinery elements, development of proper monitoring and fault diagnosis procedure to prevent malfunctioning and failure of these elements during operation is necessary. For rolling bearing fault detection, it is expected that a desired time-frequency analysis method have good computational efficiency, and have good resolution in both, time and frequency domain. The point of interest in this investigation is the present of an effective method for multi fault diagnosis in such systems with optimizing signal decomposition levels by using wavelet analysis and support vector machine (SVM). The system that is under study is an electric motor which has two rolling bearings, one of them is next to the output shaft and the other one is next to the fan and for each of them there is one normal form and three false forms, which make 8 forms for study. The outcome that we have achieved from wavelet analysis and SVM are fully in agreement with empirical result.

scholarly journals Study on Fault Diagnosis of Rolling Bearing Based on Time-Frequency Generalized Dimension

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yu Yuan ◽  
Xing Zhao ◽  
Jiyou Fei ◽  
Yulong Zhao ◽  
Jiahui Wang
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Early Stage ◽  
Fractal Theory ◽  
Rolling Bearing ◽  
Mechanical Equipment ◽  
Rolling Bearings ◽  
Common Component ◽  
Time Frequency ◽  
Multifractal Method

The condition monitoring technology and fault diagnosis technology of mechanical equipment played an important role in the modern engineering. Rolling bearing is the most common component of mechanical equipment which sustains and transfers the load. Therefore, fault diagnosis of rolling bearings has great significance. Fractal theory provides an effective method to describe the complexity and irregularity of the vibration signals of rolling bearings. In this paper a novel multifractal fault diagnosis approach based on time-frequency domain signals was proposed. The method and numerical algorithm of Multi-fractal analysis in time-frequency domain were provided. According to grid typeJand order parameterqin algorithm, the value range ofJand the cut-off condition ofqwere optimized based on the effect on the dimension calculation. Simulation experiments demonstrated that the effective signal identification could be complete by multifractal method in time-frequency domain, which is related to the factors such as signal energy and distribution. And the further fault diagnosis experiments of bearings showed that the multifractal method in time-frequency domain can complete the fault diagnosis, such as the fault judgment and fault types. And the fault detection can be done in the early stage of fault. Therefore, the multifractal method in time-frequency domain used in fault diagnosis of bearing is a practicable method.

An intelligent fault diagnosis method for rolling bearings based on feature transfer with improved DenseNet and joint distribution adaptation

2021 ◽  
Author(s):  
Chenhui Qian ◽  
Quansheng Jiang ◽  
Yehu Shen ◽  
Chunran Huo ◽  
Qingkui Zhang
Keyword(s):  
Fault Diagnosis ◽  
Joint Distribution ◽  
Marginal Distribution ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Mechanical Equipment ◽  
Intelligent Fault Diagnosis ◽  
Rolling Bearings ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

Abstract Mechanical intelligent fault diagnosis is an important method to accurately identify the health status of mechanical equipment. Traditional fault diagnosis methods perform poorly in the diagnosis of rolling bearings under complex conditions. In this paper, a feature transfer learning model based on improved DenseNet and joint distribution adaptation (FT-IDJ) is proposed. With this model, we apply it to implement rolling bearing fault diagnosis. A lightweight DenseNet model is firstly proposed to extract the transferable features of the raw vibration signal. Furthermore, the parameters in the DenseNet are constrained by the domain adaptive regularization term and pseudo label learning. The marginal distribution discrepancy and the conditional distribution discrepancy of the learned transferable features are reduced by this way. The proposed method is validated by the diagnosis experiments with CWRU and Jiangnan University rolling bearing datasets. The experimental results showed that the proposed FT-IDJ has higher classification accuracy than DAN and other eight methods, which demonstrated its effectively learning transferable features from auxiliary data.

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