Prognosis of Bearing and Gear Wears Using Convolutional Neural Network with Hybrid Loss Function

This study aimed to propose a prognostic method based on a one-dimensional convolutional neural network (1-D CNN) with clustering loss by classification training. The 1-D CNN was trained by collecting the vibration signals of normal and malfunction data in hybrid loss function (i.e., classification loss in output and clustering loss in feature space). Subsequently, the obtained feature was adopted to estimate the status for prognosis. The open bearing dataset and established gear platform were utilized to validate the functionality and feasibility of the proposed model. Moreover, the experimental platform was used to simulate the gear mechanism of the semiconductor robot to conduct a practical experiment to verify the accuracy of the model estimation. The experimental results demonstrate the performance and effectiveness of the proposed method.

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Research on fault diagnosis of automobile engines based on the deep learning 1D-CNN method

Engineering Research Express ◽

10.1088/2631-8695/ac4834 ◽

2022 ◽

Author(s):

Canyi Du ◽

Rui Zhong ◽

Yishen Zhuo ◽

Xinyu Zhang ◽

Feifei Yu ◽

...

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Deep Learning ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Pattern Recognition Method ◽

Recognition Method ◽

Vibration Signals ◽

One Dimensional ◽

Sample Set

Abstract Traditional engine fault diagnosis methods usually need to extract the features manually before classifying them by the pattern recognition method, which makes it difficult to solve the end-to-end fault diagnosis problem. In recent years, deep learning has been applied in different fields, bringing considerable convenience to technological change, and its application in the automotive field also has many applications, such as image recognition, language processing, and assisted driving. In this paper, a one-dimensional convolutional neural network (1D-CNN) in deep learning is used to process vibration signals to achieve fault diagnosis and classification. By collecting the vibration signal data of different engine working conditions, the collected data are organized into several sets of data in a working cycle, which are divided into a training sample set and a test sample set. Then, a one-dimensional convolutional neural network model is built in Python to allow the feature filter (convolution kernel) to learn the data from the training set and these convolution checks process the input data of the test set. Convolution and pooling extract features to output to a new space, which is characterized by learning features directly from the original vibration signals and completing fault diagnosis. The experimental results show that the pattern recognition method based on a one-dimensional convolutional neural network can be effectively applied to engine fault diagnosis and has higher diagnostic accuracy than traditional methods.

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Multiscale convolutional neural network and decision fusion for rolling bearing fault diagnosis

Industrial Lubrication and Tribology ◽

10.1108/ilt-09-2020-0335 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Defeng Lv ◽

Huawei Wang ◽

Changchang Che

Keyword(s):

Neural Network ◽

Time Series ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Rolling Bearing ◽

Decision Fusion ◽

Fusion Model ◽

Vibration Signals ◽

Content Type ◽

Proposed Model

Purpose The purpose of this study is to achieve an accurate intelligent fault diagnosis of rolling bearing. Design/methodology/approach To extract deep features of the original vibration signal and improve the generalization ability and robustness of the fault diagnosis model, this paper proposes a fault diagnosis method of rolling bearing based on multiscale convolutional neural network (MCNN) and decision fusion. The original vibration signals are normalized and matrixed to form grayscale image samples. In addition, multiscale samples can be achieved by convoluting these samples with different convolution kernels. Subsequently, MCNN is constructed for fault diagnosis. The results of MCNN are put into a data fusion model to obtain comprehensive fault diagnosis results. Findings The bearing data sets with multiple multivariate time series are used to testify the effectiveness of the proposed method. The proposed model can achieve 99.8% accuracy of fault diagnosis. Based on MCNN and decision fusion, the accuracy can be improved by 0.7%–3.4% compared with other models. Originality/value The proposed model can extract deep general features of vibration signals by MCNN and obtained robust fault diagnosis results based on the decision fusion model. For a long time series of vibration signals with noise, the proposed model can still achieve accurate fault diagnosis.

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An Enhanced Recurrent Convolutional Neural Network for Predicting the Status Stage of Patients with Chronic obstructive Pulmonary Diseases: Method Design (Preprint)

10.2196/preprints.28294 ◽

2021 ◽

Author(s):

Wei Li ◽

Tianyong Hao ◽

Zhanjie Mai ◽

Pengjiu Yu ◽

Chunli Liu ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real World ◽

The State ◽

Disease Stage ◽

Chronic Obstructive ◽

Clinical Dataset ◽

Copd Patients ◽

Proposed Model ◽

The Status

BACKGROUND Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of death in China and has caused serious affect to health and life quality. However, the status stage of a patient is difficult to be accurately assessed because of dynamic changes in the condition and complex risk factors. A rapid and accurate methods to predict disease stage of COPD patients is of great significance. OBJECTIVE This study aims to explore an enhanced recurrent convolutional neural networks model for predicting correct staging of patients with COPD in China for assistant disease prevention and treatment. METHODS Data was collected from The First Affiliate Hospital of Guangzhou Medical University, which had standardized disease registration and follow-up management for 5108 patients with COPD. Our enhanced recurrent convolutional neural network consists of a bidirectional LSTM layer, a convolutional layer, a max-pooling layer, and an output layer. RESULTS The model proposed was evaluated on the real-world clinical dataset of 5108 COPD patients to predict the state stage of the disease. The performance of the proposed model achieved 93.2% in terms of accuracy, outperforming a list of baseline models. CONCLUSIONS This paper proposes an enhanced recurrent convolutional neural network model which is experimented on a real-world clinical dataset containing around 5,000 patients with COPD. The proposed model achieves the best performance on all evaluation metrics indicating its feasibility in predicting the state stage of diseases.

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Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning

Sensors ◽

10.3390/s21113608 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3608

Author(s):

Chiao-Sheng Wang ◽

I-Hsi Kao ◽

Jau-Woei Perng

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Deep Learning ◽

Convolutional Neural Network ◽

Permanent Magnet ◽

Support Vector ◽

One Dimensional ◽

Proposed Model ◽

Wide Range ◽

Fault State

The early diagnosis of a motor is important. Many researchers have used deep learning to diagnose motor applications. This paper proposes a one-dimensional convolutional neural network for the diagnosis of permanent magnet synchronous motors. The one-dimensional convolutional neural network model is weakly supervised and consists of multiple convolutional feature-extraction modules. Through the analysis of the torque and current signals of the motors, the motors can be diagnosed under a wide range of speeds, variable loads, and eccentricity effects. The advantage of the proposed method is that the feature-extraction modules can extract multiscale features from complex conditions. The number of training parameters was reduced so as to solve the overfitting problem. Furthermore, the class feature map was proposed to automatically determine the frequency component that contributes to the classification using the weak learning method. The experimental results reveal that the proposed model can effectively diagnose three different motor states—healthy state, demagnetization fault state, and bearing fault state. In addition, the model can detect eccentric effects. By combining the current and torque features, the classification accuracy of the proposed model is up to 98.85%, which is higher than that of classical machine-learning methods such as the k-nearest neighbor and support vector machine.

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Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN

Sensors ◽

10.3390/s21217319 ◽

2021 ◽

Vol 21 (21) ◽

pp. 7319

Author(s):

Jiajun He ◽

Ping Wu ◽

Yizhi Tong ◽

Xujie Zhang ◽

Meizhen Lei ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Vibration Signals ◽

Maintenance Costs ◽

One Dimensional ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Multi Scale ◽

Diagnosis Method

Bearings are the key and important components of rotating machinery. Effective bearing fault diagnosis can ensure operation safety and reduce maintenance costs. This paper aims to develop a novel bearing fault diagnosis method via an improved multi-scale convolutional neural network (IMSCNN). In traditional convolutional neural network (CNN), a fixed convolutional kernel is often employed in the convolutional layer. Thus, informative features can not be fully extracted for fault diagnosis. In the proposed IMSCNN, a 1D dimensional convolutional layer is used to mitigate the effect of noise contained in vibration signals. Then, four dilated convolutional kernels with different dilation rates are integrated to extract multi-scale features through the inception structure. Experimental results from the popular CWRU and PU datasets show the superiority of the proposed method by comparison with other related methods.

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An Automatic Light Stress Grading Architecture Based on Feature Optimization and Convolutional Neural Network

Agriculture ◽

10.3390/agriculture11111126 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1126

Author(s):

Xia Hao ◽

Man Zhang ◽

Tianru Zhou ◽

Xuchao Guo ◽

Federico Tomasetto ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Loss Function ◽

Euclidean Distance ◽

Light Stress ◽

Feature Space ◽

Leafy Vegetables ◽

Factors Affecting ◽

Class Differences ◽

Triplet Loss

The identification of light stress is crucial for light control in plant factories. Image-based lighting classification of leafy vegetables has exhibited remarkable performance with high convenience and economy. Convolutional Neural Network (CNN) has been widely used for crop image analysis because of its architecture, high accuracy and efficiency. Among them, large intra-class differences and small inter-class differences are important factors affecting crop identification and a critical challenge for fine-grained classification tasks based on CNN. To address this problem, we took the Lettuce (Lactuca sativa L.) widely grown in plant factories as the research object and constructed a leaf image set containing four stress levels. Then a light stress grading model combined with classic pre-trained CNN and Triplet loss function is constructed, which is named Tr-CNN. The model uses the Triplet loss function to constrain the distance of images in the feature space, which can reduce the Euclidean distance of the samples from the same class and increase the heterogeneous Euclidean distance. Multiple sets of experimental results indicate that the model proposed in this paper (Tr-CNN) has obvious advantages in light stress grading dataset and generalized dataset.

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Multi-input Convolutional Neural Network Fault Diagnosis Algorithm Based on the Hydraulic Pump

Journal of Physics Conference Series ◽

10.1088/1742-6596/2095/1/012069 ◽

2021 ◽

Vol 2095 (1) ◽

pp. 012069

Author(s):

Lishan Zhang ◽

Lei Han ◽

Yuzhen Meng ◽

Wenkui Zhao

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Network Models ◽

Radial Vibration ◽

Hydraulic Pump ◽

Neural Network Models ◽

Vibration Signals ◽

One Dimensional ◽

Fully Connected

Abstract Convolutional neural network used in fault diagnosis can effectively extract fault features in vibration signals. However, in the feature extraction of mechanical fault diagnosis, usually more than two feature signals including at least axial and radial vibration signals can be extracted. This paper proposes two multi-input convolutional neural network models based on the fault data of the aircraft hydraulic pump including axial and radial vibration. The first is the Independent Input Multi-input Convolutional Neural Network model. The two inputs are respectively used for convolution pooling operation with CNN, and are combined through the concatenate function before the fully connected layer, and then all frames are integrated and flattened by the flatten function. A one-dimensional array, finally enters the fully connected layer and outputs the result through the softmax function. The second is the Combined Input Multiinput Convolutional Neural Network, that is, combine two one-dimensional signals into a twodimensional signal in the input layer of the convolutional neural network and then perform convolution pooling, and finally output the result through the softmax function. The results show that the two models have good accuracy and stability, and the second one has a higher convergence and fitting efficiency than the first one.

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Compound Fault Diagnosis for Gearbox Based Using of Euclidean Matrix Sample Entropy and One-Dimensional Convolutional Neural Network

Shock and Vibration ◽

10.1155/2021/6669006 ◽

2021 ◽

Vol 2021 ◽

pp. 1-26

Author(s):

Decai Zhang ◽

Xueping Ren ◽

Hanyue Zuo

Keyword(s):

Neural Network ◽

Signal Processing ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Sample Entropy ◽

Processing Methods ◽

Vibration Signals ◽

One Dimensional ◽

Signal Processing Methods ◽

Matrix Sample

Vibration signals of gearbox under different loads are sensitive to the existence of the fault and composite fault vibration signals are complex. Traditional fault diagnosis methods mostly rely on signal processing methods. It is difficult for signal processing methods to separate effective information from those fault signals. Therefore, traditional fault diagnosis methods are difficult to accurately identify those faults. In this paper, a one-dimensional convolutional neural network (1-D CNN) intelligent diagnosis method with improved SoftMax function is proposed. Local mean decomposition (LMD) decomposes the signals into different physical fictions (PF). PFs are input into the matrix sample entropy based on Euclidean distance (MESE), and the PFs which best reflect fault characteristics are selected. Finally, the PFs by MESE are used to train the CNN to identify the faults of parallel-shaft gearbox. Experiment shows that MESE can quickly and accurately select the PFs with the most significant fault features. 1-D CNN can get nearly 100% recognition rate with less time and the CNN of SoftMax improved can effectively eliminate LMD endpoint effect. This method can successfully identify single faults, combination faults, and faults under different loads of the gearbox. Compared with other methods, this method has the characteristics of high efficiency, accuracy, and strong anti-interference. Therefore, it can effectively solve the problem of complex fault signal decomposition of gearbox and can diagnose the gearbox fault under different load operation. It has great significance for gearbox fault diagnosis in actual production.

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