Deep Learning-Based Gear Pitting Severity Assessment using Acoustic Emission, Vibration and Currents signals

A method for gearbox pitting faults severity classification using Deep Learning techniques is reported. The signals are preprocessed for obtaining a 2D time-frequency representation corresponding to the Mel Frequency Cepstral Coefficients. This bi-dimensional representation is the feature space used for classification. A Long Short Term Memory network (LSTM) is used for classifying nine levels of pitting in spur gears. Each signals dataset is used for training and validating a LSTM network. Classification accuracies up to 100 % are obtained during cross-validation with the analyzed signals dataset.MOST Science and Technology Partnership Program [KY201802006]Universidad Politecnica Salesiana through the research group GIDTE

Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram

Local faults of rotating machinery usually result in repetitive transients whose impulsiveness or cyclostationarity can be employed as faulty signatures. However, to simultaneously accommodate the impulsiveness and the cyclostationarity is a challenging task for rotating machinery diagnostics. Inspired by recently-reported infogram that is sensitive to either the impulsiveness or the cyclostationarity using spectral negentropy defined in time domain or frequency domain, a multiscale clustering grey infogram (MCGI) is proposed by combining both negentropies in a grey fashion using multiscale clustering. Fourier spectrum of the vibration signal is decomposed into multiple scales with different initial resolutions. In each scale, fine segments are grouped using hierarchical clustering. Meanwhile, both time-domain and frequency-domain spectral negentropies are taken into account to guide the clustering through grey evaluation of both negentropies. Numerical simulations and experimental tests are carried out for validating the proposed MCGI. For comparison, peer methods are applied to challenge different noises and interferences. The results show that, thanks to the multiscale clustering of the spectrum and the grey evaluation of both negentropies, the present MCGI is robust in extracting the repetitive transients for the rotating machinery diagnosis. (C) 2016 Elsevier Ltd. All rights reserved

Automatic feature extraction of time-series applied to fault severity assessment of helical gearbox in stationary and non-stationary speed operation

Signals captured in rotating machines to obtain the status of their components can be considered as a source of massive information. In current methods based on artificial intelligence to fault severity assessment, features are first generated by advanced signal processing techniques. Then feature selection takes place, often requiring human expertise. This approach, besides time-consuming, is highly dependent on the machinery configuration as in general the results obtained for a mechanical system cannot be reused by other systems. Moreover, the information about time events is often lost along the process, preventing the discovery of faulty state patterns in machines operating under time-varying conditions. In this paper a novel method for automatic feature extraction and estimation of fault severity is proposed to overcome the drawbacks of classical techniques. The proposed method employs a Deep Convolutional Neural Network pre-trained by a Stacked Convolutional Autoencoder. The robustness and accuracy of this new method are validated using a dataset with different severity conditions on failure mode in a helical gearbox, working in both constant and variable speed of operation. The results show that the proposed unsupervised feature extraction method is effective for the estimation of fault severity in helical gearbox, and it has a consistently better performance in comparison with other reported feature extraction methods. (C) 2017 Elsevier B.V. All rights reserved.R&D projects Ministeriode Economia yCompet-itividad of Cobierno de Espana [TH12012-37434, T1N2013-41086-P]European FEDER fundsGIDTEC [002-002-2016-03-03]Universidad Politecnica Salesians sede CuencaSecretariat for Higher Education, Science.Technology and Innovation (SENESCVT) of the Republic of Ecuadorinfo:eu-repo/semantics/publishedVersio

Fusing convolutional generative adversarial encoders for 3D printer fault detection with only normal condition signals

Collecting data from mechanical systems in abnormal conditions is expensive and time consuming. Consequently, fault detection approaches based on classical supervised learning working with both normal and abnormal data are not applicable in some condition-based maintenance tasks. To address this problem, this paper proposes Fusing Convolutional Generative Adversarial Encoders (fCGAE) method to create fault detection models from only normal data. Firstly, to obtain an adequate deep feature space, encoder models based on 1D convolutional neural networks are created. Then, these encoders are optimized in an unsupervised way through Bidirectional Generative Adversarial Networks. Finally, the multi-channel features collected from the system are merged with One-Class Support Vector Machine. fCGAE is applied to fault detection in 3D printers, where experimental results in two fault detection cases show excellent generalization capabilities and better performance compared to peer methods. (C) 2020 Elsevier Ltd. All rights reserved.GIDTEC Research Group of Universidad Politecnica Salesiana; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [51775112, 71801046]; National Key RD Program [2016YFE0132200]; MoST Science and Technology Partnership Program [KY201802006]; Chongqing Natural Science FoundationNatural Science Foundation of Chongqing [cstc2019jcyjzdxmX0013]; CTBU Project [KFJJ2018107, KFJJ2018075