scholarly journals A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 671
Author(s):  
Daoguang Yang ◽  
Hamid Reza Karimi ◽  
Len Gelman
Keyword(s):  
Neural Networks ◽  
Fourier Transform ◽  
Fault Diagnosis ◽  
Convolutional Neural Networks ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Deep Convolutional Neural Networks ◽  
Signal Features ◽  
Fuzzy Fusion ◽  
Machinery Fault Diagnosis

Some artificial intelligence algorithms have gained much attention in the rotating machinery fault diagnosis due to their robust nonlinear regression properties. In addition, existing deep learning algorithms are usually dependent on single signal features, which would lead to the loss of some information or incomplete use of the information in the signal. To address this problem, three kinds of popular signal processing methods, including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT) and directly slicing one-dimensional data into the two-dimensional matrix, are used to create four different datasets from raw vibration signal as the input data of four enhancement Convolutional Neural Networks (CNN) models. Then, a fuzzy fusion strategy is used to fuse the output of four CNN models that could analyze the importance of each classifier and explore the interaction index between each classifier, which is different from conventional fusion strategies. To show the performance of the proposed model, an artificial fault bearing dataset and a real-world bearing dataset are used to test the feature extraction capability of the model. The good anti-noise and interpretation characteristics of the proposed method are demonstrated as well.

scholarly journals Signal Denoising Method Based on Adaptive Redundant Second-Generation Wavelet for Rotating Machinery Fault Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Na Lu ◽  
Guangtao Zhang ◽  
Yuanchu Cheng ◽  
Diyi Chen
Keyword(s):  
Fault Diagnosis ◽  
Second Generation ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Support Vector ◽  
Signal Denoising ◽  
Denoising Method ◽  
Machine Method ◽  
Second Generation Wavelet ◽  
Machinery Fault Diagnosis

Vibration signal of rotating machinery is often submerged in a large amount of noise, leading to the decrease of fault diagnosis accuracy. In order to improve the denoising effect of the vibration signal, an adaptive redundant second-generation wavelet (ARSGW) denoising method is proposed. In this method, a new index for denoising result evaluation (IDRE) is constructed first. Then, the maximum value of IDRE and the genetic algorithm are taken as the optimization objective and the optimization algorithm, respectively, to search for the optimal parameters of the ARSGW. The obtained optimal redundant second-generation wavelet (RSGW) is used for vibration signal denoising. After that, features are extracted from the denoised signal and then input into the support vector machine method for fault recognition. The application result indicates that the proposed ARSGW denoising method can effectively improve the accuracy of rotating machinery fault diagnosis.

scholarly journals Effects of Phase Shift Errors in Recurrence Plot for Rotating Machinery Fault Diagnosis

2021 ◽  
Vol 11 (2) ◽  
pp. 873
Author(s):  
Ignacio Torres-Contreras ◽  
Juan Carlos Jáuregui-Correa ◽  
Carlos Santiago López-Cajún ◽  
Salvador Echeverría-Villagómez
Keyword(s):  
Fault Diagnosis ◽  
Phase Shift ◽  
Phase Plane ◽  
Vibration Signal ◽  
Recurrence Plot ◽  
Rotating Machinery ◽  
Periodic Functions ◽  
Gaussian Peak ◽  
Phase Errors ◽  
Machinery Fault Diagnosis

For fault diagnosis and predictive maintenance of rotating machinery, the phase errors generated by the integration processing of a vibration signal are an essential investigation subject. Phase errors affect the solution of mechanical systems with multiple vibration sources and also the information transmitted through the vibration that is used for fault diagnosis. This work proposes the use of phase plane, recurrence plot (RP), and cross recurrence plot (CRP) to evaluate phase shift error effects on the solution of multiple asynchronous and simple periodic functions, and on the smoothing of a Gaussian peak with white noise. Noisy peaks were smoothed twice with the triangular method and with a different number of points. The analysis of the asynchronous periodic functions and the smoothing indicated that a small phase shift changes the phase plane and the RP pattern. These changes can affect not only the accuracy of machinery fault diagnosis but also prediction for the application of timely maintenance actions.

scholarly journals Application of Rotating Machinery Fault Diagnosis Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-30
Author(s):  
Wei Cui ◽  
Guoying Meng ◽  
Aiming Wang ◽  
Xinge Zhang ◽  
Jun Ding
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Normal Operation ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Safe Production ◽  
Continuous Progress ◽  
Machinery Fault Diagnosis

With the continuous progress of modern industry, rotating machinery is gradually developing toward complexity and intelligence. The fault diagnosis technology of rotating machinery is one of the key means to ensure the normal operation of equipment and safe production, which has very important significance. Deep learning is a useful tool for analyzing and processing big data, which has been widely used in various fields. After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault diagnosis of rotating machinery. Then, the commonly used evaluation indicators used to evaluate the performance of rotating machinery fault diagnosis methods are summarized. Finally, according to the current research status in the field of rotating machinery fault diagnosis, the current problems and possible future development and research trends are discussed.

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