scholarly journals A Multirate Sensor Information Fusion Strategy for Multitask Fault Diagnosis Based on Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Xiangkai Ma ◽  
Pei Wang ◽  
Bozhou Zhang ◽  
Ming Sun
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Information Fusion ◽  
Mechanical Systems ◽  
General Purpose ◽  
Fusion Strategy ◽  
Time Frequency ◽  
Sensor Information

In complicated mechanical systems, fault diagnosis, especially regarding feature extraction from multiple sensors, remains a challenge. Most existing methods for feature extraction tend to assume that all sensors have uniform sampling rates. However, complex mechanical systems use multirate sensors. These methods use upsampling for data preprocessing to ensure that all signals at the same scale can cause certain time-frequency features to vanish. To address these issues, this paper proposes a Multirate Sensor Information Fusion Strategy (MRSIFS) for multitask fault diagnosis. The proposed method is based on multidimensional convolution blocks incorporating multisource information fusion into the convolutional neural network (CNN) architecture. Features with different sampling rates from the raw signals are run through a multichannel parallel fault feature extraction framework for fault diagnosis. Additionally, time-frequency analysis technology is used to reveal fault information in the association between time and frequency domains. The simulation platform’s experimental results show that the proposed multitask model achieves higher diagnosis accuracy than the existing methods. Furthermore, manual feature selection for each task becomes unnecessary in MRSIFS, which has the potential toward a general-purpose framework.

scholarly journals Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 23717-23725
Author(s):  
Jiaxing Wang ◽  
Dazhi Wang ◽  
Sihan Wang ◽  
Wenhui Li ◽  
Keling Song
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Information Fusion ◽  
Sensor Information

scholarly journals Bearing Intelligent Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Junfeng Guo ◽  
Xingyu Liu ◽  
Shuangxue Li ◽  
Zhiming Wang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Rolling Bearing ◽  
Mechanical Equipment ◽  
Time Frequency ◽  
The Impact

As one of the important parts of modern mechanical equipment, the accurate real-time diagnosis of rolling bearing is particularly important. Traditional fault diagnosis methods have some disadvantages, such as low diagnostic accuracy and difficult fault feature extraction. In this paper, a method combining Wavelet transform (WT) and Deformable Convolutional Neural Network (D-CNN) is proposed to realize accurate real-time fault diagnosis of end-to-end rolling bearing. The vibration signal of rolling bearing is taken as the monitoring target. Firstly, the Orthogonal Matching Pursuit (OMP) algorithm is used to remove the harmonic signal and retain the impact signal and noise. Secondly, the time-frequency map of the signal is obtained by time-frequency transform using Wavelet analysis. Finally, the D-CNN is used for feature extraction and classification. The experimental results show that the accuracy of the method can reach 99.9% under various fault modes, and it can accurately identify the fault of rolling bearing.

An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image

Measurement ◽  
2020 ◽  
Vol 157 ◽  
pp. 107667 ◽  
Author(s):  
Ying Zhang ◽  
Kangshuo Xing ◽  
Ruxue Bai ◽  
Dengyun Sun ◽  
Zong Meng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

An adaptive data fusion strategy for fault diagnosis based on the convolutional neural network

Measurement ◽  
2020 ◽  
Vol 165 ◽  
pp. 108122 ◽  
Author(s):  
Shi Li ◽  
Huaqing Wang ◽  
Liuyang Song ◽  
Pengxin Wang ◽  
Lingli Cui ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Data Fusion ◽  
Convolutional Neural Network ◽  
Fusion Strategy

scholarly journals Mechanical Fault Diagnosis Using Color Image Recognition of Vibration Spectrogram Based on Quaternion Invariable Moment

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Liang Hua ◽  
Yujian Qiang ◽  
Juping Gu ◽  
Ling Chen ◽  
Xinsong Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Learning Algorithm ◽  
Probabilistic Neural Network ◽  
Invariant Moment ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Neural Network Algorithm

Automatic extraction of time-frequency spectral image of mechanical faults can be achieved and faults can be identified consequently when rotating machinery spectral image processing technology is applied to fault diagnosis, which is an advantage. Acquired mechanical vibration signals can be converted into color time-frequency spectrum images by the processing of pseudo Wigner-Ville distribution. Then a feature extraction method based on quaternion invariant moment was proposed, combining image processing technology and multiweight neural network technology. The paper adopted quaternion invariant moment feature extraction method and gray level-gradient cooccurrence matrix feature extraction method and combined them with geometric learning algorithm and probabilistic neural network algorithm, respectively, and compared the recognition rates of rolling bearing faults. The experimental results show that the recognition rates of quaternion invariant moment are higher than gray level-gradient cooccurrence matrix in the same recognition method. The recognition rates of geometric learning algorithm are higher than probabilistic neural network algorithm in the same feature extraction method. So the method based on quaternion invariant moment geometric learning and multiweight neural network is superior. What is more, this algorithm has preferable generalization performance under the condition of fewer samples, and it has practical value and acceptation on the field of fault diagnosis for rotating machinery as well.

scholarly journals Multitask Convolutional Neural Network for Rolling Element Bearing Fault Identification

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Mingxing Jia ◽  
Yuemei Xu ◽  
Maoyi Hong ◽  
Xiyu Hu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rolling Bearing ◽  
Rolling Element Bearing ◽  
One Dimensional ◽  
Generalization Ability ◽  
Bearing Fault ◽  
Damage Degree

As one of the most vital parts of rotating equipment, it is an essential work to diagnose rolling bearing failure. The traditional signal processing-based rolling bearing fault diagnosis algorithms rely on artificial feature extraction and expert knowledge. The working condition of rolling bearings is complex and changeable, so the traditional algorithm is slightly lacking adaptability. The damage degree also plays a crucial role in fault monitoring. Different damage degrees may take different remedial measures, but traditional fault-diagnosis algorithms roughly divide the damage degree into several categories, which do not correspond to the continuous value of the damage degree. To solve the abovementioned two problems, this paper proposes a fault-diagnosis algorithm based on “end-to-end” one-dimensional convolutional neural network. The one-dimensional convolution kernel and the pooling layer are directly applied to the original time domain signal. Feature extraction and classifier are merged together, and the extracted features are used to judge the damage degree at the same time. Then, the generalization ability of the model is studied under a variety of conditions. Experiments show that the algorithm can achieve more than 99% accuracy and can accurately give the damage degree of the bearing. It has good performance under different speeds, different types of motors, and different sampling frequencies, and so it has good generalization ability.

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