scholarly journals A Novel Anti-Noise Fault Diagnosis Approach for Rolling Bearings Based on Convolutional Neural Network Fusing Frequency Domain Feature Matching Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5532
Author(s):  
Xiangyu Zhou ◽  
Shanjun Mao ◽  
Mei Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Frequency Domain ◽  
Time Domain ◽  
Feature Matching ◽  
Evidence Theory ◽  
Noise Signal ◽  
Matching Algorithm ◽  
Noise Environment

The development of deep learning provides a new research method for fault diagnosis. However, in the industrial field, the labeled samples are insufficient and the noise interference is strong so that raw data obtained by the sensor are occupied with noise signal. It is difficult to recognize time-domain fault signals under the severe noise environment. In order to solve these problems, the convolutional neural network (CNN) fusing frequency domain feature matching algorithm (FDFM), called CNN-FDFM, is proposed in this paper. FDFM extracts key frequency features from signals in the frequency domain, which can maintain high accuracy in the case of strong noise and limited samples. CNN automatically extracts features from time-domain signals, and by using dropout to simulate noise input and increasing the size of the first-layer convolutional kernel, the anti-noise ability of the network is improved. Softmax with temperature parameter T and D-S evidence theory are used to fuse the two models. As FDFM and CNN can provide different diagnostic information in frequency domain, and time domain, respectively, the fused model CNN-FDFM achieves higher accuracy under severe noise environment. In the experiment, when a signal-to-noise ratio (SNR) drops to -10 dB, the diagnosis accuracy of CNN-FDFM still reaches 93.33%, higher than CNN’s accuracy of 45.43%. Besides, when SNR is greater than -6 dB, the accuracy of CNN-FDFM is higher than 99%.

scholarly journals Sound Visualization and Convolutional Neural Network in Fault Diagnosis of Electric Motorbike

2021 ◽  
Vol 38 (6) ◽  
pp. 1819-1827
Author(s):  
Jian-Da Wu ◽  
Che-Yuan Hsieh ◽  
Wen-Jun Luo
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Systems ◽  
Convolutional Neural Network ◽  
Frequency Domain ◽  
Time Domain ◽  
Electric Motor ◽  
Combustion Engine ◽  
Electric Power System ◽  
The Time Domain

This study proposed convolutional neural network (CNN) training for different figure recognition to diagnose electric motorbike faults. Traditional motorbike maintenance is usually carried out by technicians to find the problem step by step. Many resources are wasted and time consumed in diagnosing maintenance problems. Due to rising environmental protection awareness, motorbike power systems gradually transformed from combustion engines into the electric motor. The sound amplitude generated by the combustion engine is great and may cover other faulty sounds. The electric power system sound amplitude is greatly decreased, permitting various fault diagnosis to be performed by extracting the electric motor sound. With the development of computers and image processing, deep learning neural network for picture recognition technology becomes more feasible. This study presents the motor system sound visualization for fault diagnosis. First obtain the sound signals of the motor in the five different states of the operation in the laboratory and the road test, and draw the time domain graph, frequency domain graph and spectrogram graph to be used as the test database. The results graphs of various states were trained through a CNN. The signal states were then classified to achieve fault diagnosis. Experiments and identification results show that the spectrogram and CNN method can identify motorbike faults most effectively compared to the time domain graph and the frequency domain graph.

Variable Condition Bearing Fault Diagnosis Based on Time-Domain and Artificial Intelligence

2012 ◽  
Vol 203 ◽  
pp. 329-333 ◽  
Author(s):  
Qing Zhong Hu ◽  
Shu Lei Zhang ◽  
Sheng Yang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Time Domain ◽  
Experimental Result ◽  
Good Effect ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Variable Condition

Aim at some problem in fault diagnose: the characteristic frequency depends on the speed, the spectrum is complex , which are easy to diagnose error when in the variable conditions, and it is often difficult to identify the fault positioning in the frequency domain. the paper puts forward a new method: Variable condition bearing fault diagnosis basing on time-domain and artificial intelligence , not depend on speed and frequency domain. This method use vibration signal, calculates the kurtosis, skewness, rms etc 12 time-domain value, then these character vectors are sent to the neural network classifier to complete fault type pattern recognition, Finally, the same faults are sent to the next neural network for fault positioning and damage extent identification. The experimental result showed that using this method can obtain very good effect.

scholarly journals Gear Fault Diagnosis through Vibration and Acoustic Signal Combination Based on Convolutional Neural Network

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 266
Author(s):  
Liya Yu ◽  
Xuemei Yao ◽  
Jing Yang ◽  
Chuanjiang Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Working Conditions ◽  
Evidence Theory ◽  
Mechanical Equipment ◽  
Measuring Device ◽  
Gear Fault ◽  
Fusion Methods ◽  
Gear Fault Diagnosis

Equipment condition monitoring and diagnosis is an important means to detect and eliminate mechanical faults in real time, thereby ensuring safe and reliable operation of equipment. This traditional method uses contact measurement vibration signals to perform fault diagnosis. However, a special environment of high temperature and high corrosion in the industrial field exists. Industrial needs cannot be met through measurement. Mechanical equipment with complex working conditions has various types of faults and different fault characterizations. The sound signal of the microphone non-contact measuring device can effectively adapt to the complex environment and also reflect the operating state of the device. For the same workpiece, if it can simultaneously collect its vibration and sound signals, the two complement each other, which is beneficial for fault diagnosis. One of the limitations of the signal source and sensor is the difficulty in assessing the gear state under different working conditions. This study proposes a method based on improved evidence theory method (IDS theory), which uses convolutional neural network to combine vibration and sound signals to realize gear fault diagnosis. Experimental results show that our fusion method based on IDS theory obtains a more accurate and reliable diagnostic rate than the other fusion methods.

scholarly journals Bearing Fault Diagnosis Based on Shallow Multi-Scale Convolutional Neural Network with Attention

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3937 ◽  
Author(s):  
Tengda Huang ◽  
Sheng Fu ◽  
Haonan Feng ◽  
Jiafeng Kuang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Time Domain ◽  
Attention Mechanism ◽  
Intelligent Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale

Recently, deep learning technology was successfully applied to mechanical fault diagnosis. The convolutional neural network (CNN), as a prevalent deep learning model, occupies a place in intelligent fault diagnosis, which reduces the need for human feature extraction and prior knowledge, thereby achieving an end-to-end intelligent fault diagnosis model. However, the data for mechanical fault diagnosis in practical application are limited, the CNN model is too deep and too complex, making it prone to overfitting, and a model with too simple a structure and shallow layers cannot fully learn the effective features of the data. Convolutional filters with fixed window sizes are widely used in existing CNN models, which cannot flexibly select variable pivotal features. The model may be interfered with by redundant information in feature maps during training. Therefore, in this paper, a novel shallow multi-scale convolutional neural network with attention is proposed for bearing fault diagnosis. The shallow multi-scale convolutional neural network structure can fully learn the feature information of input data without overfitting. For the first time, a feature attention mechanism is developed for fault diagnosis to adaptively select features for classification more effectively, where the pivotal feature was emphasized, and the redundant feature was weakened through an attention mechanism. The time frequency representations as the input of the model were obtained from the vibration time domain signals, which contain the complete time domain and frequency domain information of the vibration signals. Compared with the current popular diagnostic methods, the results show that the proposed diagnostic method has fairly high accuracy, and its performance is superior to the existing methods. The average recognition accuracy was 99.86%, and the weak recognition rate of I-07 and I-14 labels was improved.

scholarly journals Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 450
Author(s):  
Xudong Li ◽  
Jianhua Zheng ◽  
Mingtao Li ◽  
Wenzhen Ma ◽  
Yang Hu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Frequency Domain ◽  
Transfer Learning ◽  
Network Architecture ◽  
Domain Adaptation ◽  
Training Dataset ◽  
Testing Dataset ◽  
Feature Extractor

In recent years, transfer learning has been widely applied in fault diagnosis for solving the problem of inconsistent distribution of the original training dataset and the online-collecting testing dataset. In particular, the domain adaptation method can solve the problem of the unlabeled testing dataset in transfer learning. Moreover, Convolutional Neural Network (CNN) is the most widely used network among existing domain adaptation approaches due to its powerful feature extraction capability. However, network designing is too empirical, and there is no network designing principle from the frequency domain. In this paper, we propose a unified convolutional neural network architecture from a frequency domain perspective for a domain adaptation named Frequency-domain Fusing Convolutional Neural Network (FFCNN). The method of FFCNN contains two parts, frequency-domain fusing layer and feature extractor. The frequency-domain fusing layer uses convolution operations to filter signals at different frequency bands and combines them into new input signals. These signals are input to the feature extractor to extract features and make domain adaptation. We apply FFCNN for three domain adaptation methods, and the diagnosis accuracy is improved compared to the typical CNN.

scholarly journals A Novel 1-D CCANet for ECG Classification

2021 ◽  
Vol 11 (6) ◽  
pp. 2758
Author(s):  
Ian-Christopher Tanoh ◽  
Paolo Napoletano
Keyword(s):  
Neural Network ◽  
Computational Complexity ◽  
Convolutional Neural Network ◽  
Frequency Domain ◽  
Time Domain ◽  
Complex Structures ◽  
Autoregressive Modeling ◽  
One Dimensional ◽  
Electrocardiogram Ecg

This paper puts forward a 1-D convolutional neural network (CNN) that exploits a novel analysis of the correlation between the two leads of the noisy electrocardiogram (ECG) to classify heartbeats. The proposed method is one-dimensional, enabling complex structures while maintaining a reasonable computational complexity. It is based on the combination of elementary handcrafted time domain features, frequency domain features through spectrograms and the use of autoregressive modeling. On the MIT-BIH database, a 95.52% overall accuracy is obtained by classifying 15 types, whereas a 95.70% overall accuracy is reached when classifying 7 types from the INCART database.

Separating Multiple Moving Sources by Microphone Array Signals for Wayside Acoustic Fault Diagnosis

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Wei Xiong ◽  
Qingbo He ◽  
Zhike Peng
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Feature Matching ◽  
Matching Pursuit ◽  
Microphone Array ◽  
Time Frequency ◽  
Moving Sources ◽  
The Time Domain ◽  
Envelope Spectrum ◽  
Threshold Setting

Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

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