Denoising convolutional autoencoder configuration for condition monitoring of rotating machines

Godói, Leonardo Franco de; Nóbrega, Eurípedes Guilherme de Oliveira

doi:10.1007/s40430-020-02776-7

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…Accordingly, among the state-of-the-art deep learning techniques, the unsupervised deep feature learning model based on SAE and other variants have shown an important role in some existing applications of machinery condition monitoring and fault diagnosis [ 27 ]. Godói utilized some variant of SAE in condition monitoring of rotating machines [ 28 ]. Huan Chen et al proposed a convolutional autoencoder-based method for energy disaggregation [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Deep Feature Learning Model Based on Parallel Convolutional Autoencoder for Intelligent Fault Diagnosis of Main Reducer

Liu

2021

Computational Intelligence and Neuroscience

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Traditional diagnostic framework consists of three parts: data acquisition, feature generation, and fault classification. However, manual feature extraction utilized signal processing technologies heavily depending on subjectivity and prior knowledge which affect the effectiveness and efficiency. To tackle these problems, an unsupervised deep feature learning model based on parallel convolutional autoencoder (PCAE) is proposed and applied in the stage of feature generation of diagnostic framework. Firstly, raw vibration signals are normalized and segmented into sample set by sliding window. Secondly, deep features are, respectively, extracted from reshaped form of raw sample set and spectrogram in time-frequency domain by two parallel unsupervised feature learning branches based on convolutional autoencoder (CAE). During the training process, dropout regularization and batch normalization are utilized to prevent over fitting. Finally, extracted representative features are feed into the classification model based on deep structure of neural network (DNN) with softmax. The effectiveness of the proposed approach is evaluated in fault diagnosis of automobile main reducer. The results produced in contrastive analysis demonstrate that the diagnostic framework based on parallel unsupervised feature learning and deep structure of classification can effectively enhance the robustness and enhance the identification accuracy of operation conditions by nearly 8%.

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Section: Introductionmentioning

confidence: 99%

An Unsupervised Deep Feature Learning Model Based on Parallel Convolutional Autoencoder for Intelligent Fault Diagnosis of Main Reducer

Liu

2021

Computational Intelligence and Neuroscience

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Applications of Artificial Intelligence for Fault Diagnosis of Rotating Machines: A Review

Kibrete

Woldemichael

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Denoising convolutional autoencoder configuration for condition monitoring of rotating machines

Cited by 2 publications

References 32 publications

An Unsupervised Deep Feature Learning Model Based on Parallel Convolutional Autoencoder for Intelligent Fault Diagnosis of Main Reducer

An Unsupervised Deep Feature Learning Model Based on Parallel Convolutional Autoencoder for Intelligent Fault Diagnosis of Main Reducer

Applications of Artificial Intelligence for Fault Diagnosis of Rotating Machines: A Review

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