Fault Diagnosis of Rolling Bearing Using Wireless Sensor Networks and Convolutional Neural Network

Rolling bearings are widely used in modern production equipment. Effective bearing fault diagnosis method will improve the reliability of the machinery and increase its operating efficiency. In this paper, a novel fault diagnosis method based on WSN and CNN has been proposed to fully utilize the strong fault classification capability of CNN and the inherent merits of WSNs, such as relatively low cost, convenience of installation, and ease of relocation. The feasibility and effectiveness of proposed system are evaluated using the vibration data sets of seven motor operating conditions released by the Case Western Reserve University Bearing Data Center. The experimental results show the fault diagnosis accuracy of the proposed approach can reach 97.6%.

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Fault Diagnosis of Rolling Bearing Based on Tunable Q-Factor Wavelet Transform and Convolutional Neural Network

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v16i02.11953 ◽

2020 ◽

Vol 16 (02) ◽

pp. 47

Author(s):

Liqun Hou ◽

Zijing Li

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Vibration Signal ◽

Rolling Bearing ◽

Operating Conditions ◽

Fault Classification ◽

Rolling Ball ◽

Diagnosis Method

Rolling bearing plays an important role in rotary machines and industrial processes. Effective fault diagnosis technology for rolling bearing directly affects the life and operator safety of the devices. In this paper, a fault diagnosis method based on tunable-Q wavelet transform (TQWT) and convolutional neural network (CNN) is proposed to reduce the influence of noise on bearing vibration signal and the dependence on the experience of traditional diagnosis methods. TQWT is used to decompose and denoise the vibration signal, while the CNN is adopted to extract fault features and carry out fault classification. Seven motor operating conditions—normal, drive end rolling ball failure (DE-B), drive end inner raceway failure (DE-IR), drive end outer raceway failure (DE-OR), fan end rolling ball failure (FE-B), fan end inner raceway fault (FE-IR) and fan end outer raceway fault (FE-OR)—are used to evaluate the proposed approach. The experimental results indicate that the fault diagnosis accuracy of the proposed method reaches 99.8%.

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Symbolic Important Point Perceptually and Hidden Markov Model Based Hydraulic Pump Fault Diagnosis Method

Sensors ◽

10.3390/s18124460 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4460 ◽

Cited By ~ 5

Author(s):

Yunzhao Jia ◽

Minqiang Xu ◽

Rixin Wang

Keyword(s):

Fault Diagnosis ◽

Markov Model ◽

Hidden Markov Model ◽

Hydraulic System ◽

Hidden Markov ◽

Operating Conditions ◽

Fault Classification ◽

Hydraulic Pump ◽

Diagnosis Method ◽

Original Time

Hydraulic pump is a driving device of the hydraulic system, always working under harsh operating conditions, its fault diagnosis work is necessary for the smooth running of a hydraulic system. However, it is difficult to collect sufficient status information in practical operating processes. In order to achieve fault diagnosis with poor information, a novel fault diagnosis method that is the based on Symbolic Perceptually Important Point (SPIP) and Hidden Markov Model (HMM) is proposed. Perceptually important point technology is firstly imported into rotating machine fault diagnosis; it is applied to compress the original time-series into PIP series, which can depict the overall movement shape of original time series. The PIP series is transformed into symbolic series that will serve as feature series for HMM, Genetic Algorithm is used to optimize the symbolic space partition scheme. The Hidden Markov Model is then employed for fault classification. An experiment involves four operating conditions is applied to validate the proposed method. The results show that the fault classification accuracy of the proposed method reaches 99.625% when each testing sample only containing 250 points and the signal duration is 0.025 s. The proposed method could achieve good performance under poor information conditions.

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A Novel Intelligent Fault Diagnosis Method for Rolling Bearing Based on Integrated Weight Strategy Features Learning

Sensors ◽

10.3390/s20061774 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1774

Author(s):

Jun He ◽

Ming Ouyang ◽

Chen Yong ◽

Danfeng Chen ◽

Jing Guo ◽

...

Keyword(s):

Fault Diagnosis ◽

Standard Deviation ◽

Rolling Bearing ◽

Fault Classification ◽

Intelligent Fault Diagnosis ◽

Softmax Classifier ◽

Bearing Fault Diagnosis ◽

Diagnosis Method ◽

Testing Accuracy ◽

Intelligent Methods

Intelligent methods have long been researched in fault diagnosis. Traditionally, feature extraction and fault classification are separated, and this process is not completely intelligent. In addition, most traditional intelligent methods use an individual model, which cannot extract the discriminate features when the machines work in a complex condition. To overcome the shortcomings of traditional intelligent fault diagnosis methods, in this paper, an intelligent bearing fault diagnosis method based on ensemble sparse auto-encoders was proposed. Three different sparse auto-encoders were used as the main architecture. To improve the robustness and stability, a novel weight strategy based on distance metric and standard deviation metric was employed to assign the weights of three sparse auto-encodes. Softmax classifier is used to classify the fault types of integrated features. The effectiveness of the proposed method is validated with extensive experiments, and comparisons with the related methods and researches on the widely-used motor bearing dataset verify the superiority of the proposed method. The results show that the testing accuracy and the standard deviation are 99.71% and 0.05%.

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Rolling Bearing Fault Diagnosis and Prediction Based on VMD-CWT and MobileNet

10.5121/csit.2021.110305 ◽

2021 ◽

Author(s):

Jing Zhu ◽

Aidong Deng ◽

Shuo Xue ◽

Xue Ding ◽

Shun Zhang

Keyword(s):

Fault Diagnosis ◽

Rolling Bearing ◽

Model Complexity ◽

Data Sets ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Network Training ◽

Signal Features ◽

Order Of Magnitude ◽

Diagnosis Method

When deep learning is used for rolling bearing fault diagnosis, there are problems of high model complexity, time-consuming, and large memory. In order to solve this problem. This paper presents an intelligent diagnosis method of rolling bearings based on VMD-CWT feature extraction and MobileNet, VMD is used to extract the signal features, and then wavelet transform is used to extract the timefrequency features. After the image is enhanced, the MobileNet network is trained. In order to accelerate the convergence speed, this paper adds transfer learning in the network training process, and migrates the weights of the first several layers pretrained to the corresponding network. Experimental results based on bearing fault data sets show that after adopting VMD-CWT, the accuracy of mobilenet increased from 68.7% to 94%, and its network parameters were reduced by an order of magnitude compared with CNN.

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Compound Fault Diagnosis of Rolling Bearing Based on ALIF-KELM

Mathematical Problems in Engineering ◽

10.1155/2021/2636302 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Jie Ma ◽

Shitong Liang ◽

Zhengyu Du ◽

Ming Chen

Keyword(s):

Fault Diagnosis ◽

Rolling Bearing ◽

Fault Classification ◽

Training Set ◽

Sample Data ◽

Diagnosis Method ◽

Main Fault ◽

Bp Model ◽

Fault Information

Aiming at the shortcomings of difficult classification of rolling bearing compound faults and low recognition accuracy, a composite fault diagnosis method of rolling bearing combined with ALIF and KELM is proposed. First, the basic concepts of ALIF and KELM are introduced, and then ALIF is used to decompose the sample data of vibration signals of different bearing states so that each sample can get several IMFs, select the top K IMFs containing the main fault information from each sample, calculate the energy feature and sample entropy of each IMF, and construct a fault feature vector with a dimension of 2K. Finally, the feature vectors of the training set and the test set are input into the KELM model for fault classification. Experimental results show that, compared with EMD-KELM model, ALIF-ELM model, ALIF-BP model, and IFD-KELM model, the rolling bearing composite fault diagnosis method based on the ALIF-KELM model has higher classification accuracy.

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Vibration Fault Diagnosis for Wind Turbine Based on Enhanced Supervised Locally Linear Embedding

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1008-1009.983 ◽

2014 ◽

Vol 1008-1009 ◽

pp. 983-987

Author(s):

Xiang Wang ◽

Yuan Zheng

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Learning Algorithm ◽

Operating Conditions ◽

Fault Classification ◽

Maintenance Cost ◽

Locally Linear Embedding ◽

Diagnosis Method ◽

Linear Embedding ◽

Locally Linear

Fault diagnosis for wind turbine is an important task for reducing their maintenance cost. However, the non-stationary dynamic operating conditions of wind turbines pose a challenge to fault diagnosis for wind turbine. Fault diagnosis is essentially a kind of pattern recognition. In this paper, a novel fault diagnosis method based on enhanced supervised locally linear embedding is proposed for wind turbine. The approach first performs the recently proposed manifold learning algorithm locally linear embedding on the high-dimensional fault signal samples to learn the intrinsic embedded multiple manifold features corresponding to different fault modes. Enhanced supervised locally linear embedding not only can map them into a low-dimensional embedded space to achieve fault feature extraction, but also can deal with new fault samples. Finally fault classification is carried out in the embedded manifold space. The wind turbine gearbox ball bearing vibration fault signals are used to validate the proposed fault diagnosis method. The results indicate that the proposed approach obviously improves the fault classification performance and outperforms the other traditional approaches.

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Fine-Grained Fault Diagnosis Method of Rolling Bearing Combining Multisynchrosqueezing Transform and Sparse Feature Coding Based on Dictionary Learning

Shock and Vibration ◽

10.1155/2019/1531079 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13

Author(s):

Guodong Sun ◽

Yuan Gao ◽

Kai Lin ◽

Ye Hu

Keyword(s):

Fault Diagnosis ◽

Dictionary Learning ◽

Nonnegative Matrix ◽

Rolling Bearing ◽

Fault Classification ◽

Support Vector ◽

Time Frequency ◽

Fine Grained ◽

Diagnosis Method

To accurately diagnose fine-grained fault of rolling bearing, this paper proposed a new fault diagnosis method combining multisynchrosqueezing transform (MSST) and sparse feature coding based on dictionary learning (SFC-DL). Firstly, the high-resolution time-frequency images of raw vibration signals, including different kinds of fine-grained faults of rolling bearing, were constructed by MSST. Then, the basis dictionary was trained through nonnegative matrix factorization with sparseness constraints (NMFSC), and the trained basis dictionary was employed to extract features from time-frequency matrixes by using nonnegative linear equations. Finally, a linear support vector machine (LSVM) was trained with features of training samples, and the trained LSVM was employed to diagnosis the fault classification of test samples. Compared with state-of-the-art fault diagnosis methods, the proposed method, which was tested on the bearing dataset from Case Western Reserve University (CWRU), achieved the fine-grained classification of 10 mixed fault states. Meanwhile, the proposed method was applied on the dataset from the Machinery Failure Prevention Technology (MFPT) Society and realized the classification of 3 fault states under different working conditions. These results indicate that the proposed method has great robustness and could better meet the needs of practical engineering.

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Intelligent Fault Diagnosis Based on Dynamic Convolutional Depth Domain Adaptive Network

10.21203/rs.3.rs-903138/v1 ◽

2021 ◽

Author(s):

Huafeng Zhou ◽

Pei-Yuan Cheng ◽

Si-Yu Shao ◽

Yu-Wei Zhao ◽

Xin-Yu Yang

Keyword(s):

Fault Diagnosis ◽

High Performance ◽

Domain Adaptation ◽

Operating Conditions ◽

Training Data ◽

Fault Classification ◽

Target Domain ◽

Second Order Statistics ◽

Testing Data ◽

Diagnosis Method

Abstract Deep learning-based mechanical fault diagnosis method has made great achievements. A high-performance neural network model requires sufficient labelled data for training to obtain accurate classification results. Desired results mainly depends on assumption that training and testing data are collected under the same working conditions, environment and operating conditions, where the data have the same probability distribution. However, in the practical scenarios, training data and the testing data follow different distributions to some degree, and the newly collected testing data are usually unlabeled. In order to solve the problems above, a model based on transfer learning and domain adaptation is proposed to achieve efficient fault diagnosis under different data distributions. The proposed framework adapts the features extracted by multiple dynamic convolutional layers, and creatively utilizes correlation alignment(CORAL) to perform a non-linear transformation to align the second-order statistics of the two distributions for fault diagnosis, which greatly improves the accuracy of fault classification in the target domain under unlabeled data. Finally, experimental verifications have been carried out among two different datasets.

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Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition

Shock and Vibration ◽

10.1155/2016/1948029 ◽

2016 ◽

Vol 2016 ◽

pp. 1-14 ◽

Cited By ~ 3

Author(s):

Bo Zhou ◽

Yujie Cheng

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Image Recognition ◽

Working Conditions ◽

Rolling Bearing ◽

Fault Classification ◽

Kernel Principal Component Analysis ◽

Two Dimensional ◽

Rolling Bearings ◽

Diagnosis Method

Rolling bearing faults often lead to electromechanical system failure due to its high speed and complex working conditions. Recently, a large amount of fault diagnosis studies for rolling bearing based on vibration data has been reported. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper proposes a fault diagnosis method based on image recognition for rolling bearings to realize fault classification under variable working conditions. The proposed method includes the following steps. First, the vibration signal data are transformed into a two-dimensional image based on recurrence plot (RP) technique. Next, a popular feature extraction method which has been widely used in the image field, scale invariant feature transform (SIFT), is employed to extract fault features from the two-dimensional RP and subsequently generate a 128-dimensional feature vector. Third, due to the redundancy of the high-dimensional feature, kernel principal component analysis is utilized to reduce the feature dimensionality. Finally, a neural network classifier trained by probabilistic neural network is used to perform fault diagnosis. Verification experiment results demonstrate the effectiveness of the proposed fault diagnosis method for rolling bearings under variable conditions, thereby providing a promising approach to fault diagnosis for rolling bearings.

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