Journal ArticleDOI
Modified Stacked Auto-encoder Using Adaptive Morlet Wavelet for Intelligent Fault Diagnosis of Rotating Machinery
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A modified stacked auto-encoder that uses adaptive Morlet wavelet is proposed to automatically diagnose various fault types and severities of rotating machinery and experimental results show that the proposed method is superior to other state-of-the-art methods.Abstract:
Intelligent fault diagnosis techniques play an important role in improving the abilities of automated monitoring, inference, and decision-making for the repair and maintenance of machinery and processes In this paper, a modified stacked auto-encoder (MSAE) that uses adaptive Morlet wavelet is proposed to automatically diagnose various fault types and severities of rotating machinery Firstly, the Morlet wavelet activation function is utilized to construct an MSAE to establish an accurate nonlinear mapping between the raw nonstationary vibration data and different fault states Then, the nonnegative constraint is applied to enhance the cost function to improve sparsity performance and reconstruction quality Finally, the fruit fly optimization algorithm (FOA) is used to determine the adjustable parameters of the Morlet wavelet to flexibly match the characteristics of the analyzed data The proposed method is used to analyze the raw vibration data collected from a sun gear unit and a roller bearing unit Experimental results show that the proposed method is superior to other state-of-the-art methodsread more
Citations
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Novel Joint Transfer Network for Unsupervised Bearing Fault Diagnosis From Simulation Domain to Experimental Domain
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Novel Joint Transfer Network for Unsupervised Bearing Fault Diagnosis From Simulation Domain to Experimental Domain
TL;DR: Wang et al. as discussed by the authors proposed a joint transfer network for unsupervised bearing fault diagnosis from the simulation domain to the experimental domain to reduce the dependence on the resources of laboratory test rigs.
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TL;DR: A case study on FEMTO-ST datasets shows that the fine-tuned model is competent for incipient fault detection, outperforming other state-of-the-art methods.
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Semi-supervised graph convolutional network and its application in intelligent fault diagnosis of rotating machinery
TL;DR: Experimental results indicate that the proposed method can adaptively extract the available fault features from the raw vibration signals and can obtain an average accuracy of more than 98%.
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Bearing fault diagnosis using transfer learning and optimized deep belief network
TL;DR: The experimental results show that the proposed JACADN method can effectively improve the fault diagnosis accuracy of rolling bearings under variable operating conditions.