A new fault diagnosis method based on convolutional neural network and compressive sensing

Ma, Yunfei; Jia, Xisheng; Bai, Huajun; Liu, Guozeng; Wang, Guang-Long; Guo, Chiming; Wang, Shuangchuan

doi:10.1007/s12206-019-1007-5

Cited by 25 publications

(11 citation statements)

References 24 publications

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“…As an effective deep learning method, CNN has been successfully utilized in various applications involving time series data, especially in control systems [38], [46]- [49]. Based on the GToMFI database, the CNN diagnosis model is designed including data processing, cross-entropy calculation, key neural layers such as convolutional layer, pooling layer, and fully connected layer, etc.…”

Section: A Cnn Diagnosis Modelmentioning

confidence: 99%

CNN-Based Intelligent Fault-Tolerant Control Design for Turbofan Engines With Actuator Faults

Cheng

Liu

2021

IEEE Access

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In this paper, the problem of fault-tolerant control is investigated for turbofan engines with actuator faults. The controller involvement has repressed the effects of actuator faults on the controlled outputs of turbofan engines, making fault-tolerant control difficult. To solve this problem, the internal gaspath data of turbofan engines is introduced to provide conducive fault information. Besides, the useful property of the convolution neural network (CNN) is explored and utilized in fault-tolerant control. Based on the analysis of actuator faults, by using the Lyapunov stability and L 2-gain like theorems, a novel CNN-based intelligent fault-tolerant control system for turbofan engines is proposed, including a CNNbased fault diagnosis module and a nonlinear fault-tolerant controller with corresponding reconfiguration unit. The CNN-based intelligent fault-tolerant control system has the advantages of reducing the accuracy requirements of the mathematical description of turbofan engines. Furthermore, the proposed system can diagnose actuator faults and reduce the adverse effects of actuator faults on turbofan engines. Finally, simulation results are presented to demonstrate the efficiency of the designed method. INDEX TERMS CNN-based intelligent fault-tolerant control, actuator faults, gas-path data, fault diagnosis, nonlinear fault-tolerant controller, turbofan engines.

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Section: A Cnn Diagnosis Modelmentioning

confidence: 99%

CNN-Based Intelligent Fault-Tolerant Control Design for Turbofan Engines With Actuator Faults

Cheng

Liu

2021

IEEE Access

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“…In order to ensure the accuracy, this article does not simplify the formula, but complete the subsequent formula derivation based on the expression formula (10). Therefore, the trace of the covariance matrix of the weight x 0 is:…”

Section: Optimization Goal: Trace Of the Covariance Matrix Of The Weightmentioning

confidence: 99%

“…The equipment vibration signals under negative conditions and the signals under normal operating conditions often have different characteristics. Therefore, vibration signals analysis for power equipment can distinguish different operating conditions of equipment, thereby helping people to diagnose faults and find the cause [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Different types of sound signals are closely related to the characteristics of different space or objects.…”

Section: Introductionmentioning

confidence: 99%

Signals classification based on IA-optimal CNN

Zhang

et al. 2021

Neural Comput & Applic

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The versatility of the existing A-optimal-based CNN for solving multiple types of signals classification problems has not been verified by different signals datasets. Moreover, the existing A-optimal-based CNN uses a simplified approximate function as the optimization objective function instead of precise analytical function, which affects the signals classification accuracy to a certain extent. In this paper, a classification method called IA-optimal CNN is proposed. To improve the stability of the classifier, the trace of the covariance matrix of the weights of the fully connected layer is used as the optimization objective function, and the parameter optimization model is established without any simplification of the optimization objective function. In addition, to avoid the difficulty of not being able to obtain the analytical expression formula of the partial derivative of the inverse matrix with regard to the networks parameters, a novel dual function is introduced to transform the optimization problem into an equivalent binary function optimization problem. Furthermore, based on the above analytical solution results, the parameters are updated using the alternate iterative optimization method and the accurate weight update formula is deduced in detail. Five signals datasets are used to test the universality of the IAoptimal CNN in signals classification fields. The performance of IA-optimal CNN is showed, and the experimental results are compared with the existing A-optimal-based classification algorithm. Lastly, the following conclusion is proved theoretically: For the A-optimal-based CNN, the trace of the covariance matrix will continue to decrease and approach a convergence value in the iterative process, but it is impossible for the networks to strictly reach the A-optimal state.

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“…Deep learning can extract and learn the representative patterns of the signals effectively compared to conventional feature extraction and selection methods. For example, the convolutional neural network method shows excellent performance for pattern recognition that was also applied for fault diagnosis [24,25]. Janssens et al developed an architecture of the CNN model for detecting rotary machinery faults with vibration spectrum features [26].…”

Section: Introductionmentioning

confidence: 99%

Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment

et al. 2021

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In recent years, the internet of things (IoT) represents the main core of Industry 4.0 for cyberphysic systems (CPS) in order to improve the industrial environment. Accordingly, the application of IoT and CPS has been expanded in applied electrical systems and machines. However, cybersecurity represents the main challenge of the implementation of IoT against cyber-attacks. In this regard, this paper proposes a new IoT architecture based on utilizing machine learning techniques to suppress cyber-attacks for providing reliable and secure online monitoring for the induction motor status. In particular, advanced machine learning techniques are utilized here to detect cyber-attacks and motor status with high accuracy. The proposed infrastructure validates the motor status via communication channels and the internet connection with economical cost and less effort on connecting various networks. For this purpose, the CONTACT Element platform for IoT is adopted to visualize the processed data based on machine learning techniques through a graphical dashboard. Once the cyber-attacks signal has been detected, the proposed IoT platform based on machine learning will be visualized automatically as fake data on the dashboard of the IoT platform. Different experimental scenarios with data acquisition are carried out to emphasize the performance of the suggested IoT topology. The results confirm that the proposed IoT architecture based on the machine learning technique can effectively visualize all faults of the motor status as well as the cyber-attacks on the networks. Moreover, all faults of the motor status and the fake data, due to the cyber-attacks, are successfully recognized and visualized on the dashboard of the proposed IoT platform with high accuracy and more clarified visualization, thereby contributing to enhancing the decision-making about the motor status. Furthermore, the introduced IoT architecture with Random Forest algorithm provides an effective detection for the faults on motor due to the vibration under industrial conditions with excellent accuracy of 99.03% that is significantly greater than the other machine learning algorithms. Besides, the proposed IoT has low latency to recognize the motor faults and cyber-attacks to present them in the main dashboard of the IoT platform.

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A new fault diagnosis method based on convolutional neural network and compressive sensing

Cited by 25 publications

References 24 publications

CNN-Based Intelligent Fault-Tolerant Control Design for Turbofan Engines With Actuator Faults

CNN-Based Intelligent Fault-Tolerant Control Design for Turbofan Engines With Actuator Faults

Signals classification based on IA-optimal CNN

Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment

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