A semi-supervised Support Vector Data Description-based fault detection method for rolling element bearings based on cyclic spectral analysis
Chenyu Liu,Konstantinos Gryllias +1 more
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TLDR
A novel semi-supervised Support Vector Data Description with negative samples (NSVDD) fault detection approach is proposed and shows superior characteristics in anomaly detection compared to three classification methodologies, i.e. the Back-Propagation Neural Network, the Random Forest and the K-Nearest Neighbour.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2020-06-01 and is currently open access. It has received 70 citations till now. The article focuses on the topics: Fault detection and isolation & Anomaly detection.read more
Citations
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Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery
TL;DR: Several main techniques applied in CNN-based intelligent diagnosis, principally including the fast Fourier transform, wavelet transform, data augmentation, S-transform, and cyclic spectral analysis are discussed.
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Oversampling adversarial network for class-imbalanced fault diagnosis
TL;DR: A new adversarial network for simultaneous classification and fault detection is proposed and the discriminator of this model is designed to handle the generated faulty samples to prevent outlier and overfitting.
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Semi-Supervised Bearing Fault Diagnosis and Classification Using Variational Autoencoder-Based Deep Generative Models
TL;DR: A semi-supervised learning scheme for bearing fault diagnosis using variational autoencoder (VAE)-based deep generative models, which can effectively leverage a dataset when only a small subset of data have labels is proposed.
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A novel fault diagnosis procedure based on improved symplectic geometry mode decomposition and optimized SVM
TL;DR: A novel fault diagnosis procedure based on improved symplectic geometry mode decomposition (SGMD) and optimized SVM and Harris hawks optimization algorithm (HHO) is presented, demonstrating its effectiveness and robustness for rotating machineries fault diagnosis.
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Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review
TL;DR: In this article, the authors provide a systematic overview of the current development, common technologies, open source datasets, codes, and challenges of AI-enabled PHM methods from three aspects of monitoring, diagnosis, and prognosis.
References
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Support Vector Data Description
TL;DR: The Support Vector Data Description (SVDD) is presented which obtains a spherically shaped boundary around a dataset and analogous to the Support Vector Classifier it can be made flexible by using other kernel functions.
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Rolling element bearing diagnostics—A tutorial
Robert B. Randall,Jérôme Antoni +1 more
TL;DR: This tutorial is intended to guide the reader in the diagnostic analysis of acceleration signals from rolling element bearings, in particular in the presence of strong masking signals from other machine components such as gears.
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Support vector domain description
TL;DR: This paper shows the use of a data domain description method, inspired by the support vector machine by Vapnik, called the support vectors domain description (SVDD), which can be used for novelty or outlier detection and is compared with other outlier Detection methods on real data.
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Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics
TL;DR: In this paper, the performance of wavelet decomposition-based de-noising and wavelet filter based denoising methods are compared based on signals from mechanical defects, and the comparison result reveals that wavelet filters are more suitable and reliable to detect a weak signature of mechanical impulse-like defect signals, whereas the wavelet transform has a better performance on smooth signal detection.
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The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines
Jérôme Antoni,Robert B. Randall +1 more
TL;DR: In this article, the spectral kurtosis (SK) was used to detect and characterize nonstationary signals in the presence of strong masking noise and to detect incipient faults in rotating machines.