Journal ArticleDOI
Advanced bearing diagnostics: A comparative study of two powerful approaches
TLDR
This paper investigates and compares two emerging approaches to vibration-based fault detection based on a cyclostationary modeling of the bearing signal and addresses the extension of these approaches to the nonstationary operating regime.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2019-01-01. It has received 101 citations till now. The article focuses on the topics: Fault detection and isolation & Cyclostationary process.read more
Citations
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Journal ArticleDOI
Deep digital twins for detection, diagnostics and prognostics
TL;DR: An overview of the Deep Digital Twin framework is presented and it is demonstrated that the DDT is able to detect incipient faults, track asset degradation and differentiate between failure modes in both stationary and non-stationary operating conditions when trained on only healthy operating data.
Journal ArticleDOI
Series2Graph: graph-based subsequence anomaly detection for time series
Paul Boniol,Themis Palpanas +1 more
TL;DR: The experimental results demonstrate that the proposed approach correctly identifies single and recurrent anomalies without any prior knowledge of their characteristics, outperforming by a large margin several competing approaches in accuracy, while being up to orders of magnitude faster.
Journal ArticleDOI
Transfer learning for remaining useful life prediction of multi-conditions bearings based on bidirectional-GRU network
TL;DR: A new transfer learning method based on bidirectional Gated Recurrent Unit (TBiGRU) is proposed to accurately predict the RUL of bearings under different working conditions and can adaptively recognize different running states of bearings and obtain corresponding training labels, and realize better RUL prediction performance under differentWorking conditions.
Journal ArticleDOI
A physics-informed deep learning approach for bearing fault detection
Sheng Shen,Hao Lu,Mohammadkazem Sadoughi,Chao Hu,Venkat P. Nemani,Adam Thelen,Keith E. Webster,Matthew J. Darr,Jeff Sidon,Shawn Kenny +9 more
TL;DR: In this article, a physics-informed deep learning approach was proposed for bearing condition monitoring and fault detection, which consists of a simple threshold model and a deep convolutional neural network (CNN) model.
Proceedings ArticleDOI
Automated Anomaly Detection in Large Sequences
TL;DR: NorM is a novel approach, suitable for domain-agnostic anomaly detection, based on a new data series primitive, which permits to detect anomalies based on their (dis)similarity to a model that represents normal behavior.
References
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Journal ArticleDOI
Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter
H. Endo,Robert B. Randall +1 more
TL;DR: In this paper, the authors proposed the use of the minimum entropy deconvolution (MED) technique to enhance the ability of the existing autoregressive (AR) model based filtering technique to detect localised faults in gears.
Journal ArticleDOI
Cyclic spectral analysis of rolling-element bearing signals : Facts and fictions
TL;DR: In this paper, the cyclic spectral tools should be considered for diagnostics of rolling-element bearing vibrations, which can not only indicate the presence of a fault in high levels of background noise, but can also return a relative measure of its severity.
Journal ArticleDOI
Differential Diagnosis of Gear and Bearing Faults
Jérôme Antoni,Robert B. Randall +1 more
TL;DR: In this article, a vibration-based diagnosis of rolling element bearings in the presence of strong interfering gear signals, such as is typical of helicopter gearboxes, is presented. But bearing signals experience some randomness and are close to cyclostationary, i.e. with a periodic bivariate autocorrelation function.
Journal ArticleDOI
An enhanced Kurtogram method for fault diagnosis of rolling element bearings
TL;DR: In this paper, the authors proposed an enhanced Kurtogram based on the power spectrum of the envelope of the signals extracted from wavelet packet nodes at different depths, which measured the protrusion of the sparse representation.
Journal ArticleDOI
Cyclic spectral analysis in practice
TL;DR: In this article, it is shown that non-parametric cyclic spectral estimators can all be derived from a general quadratic form, which yields as particular cases cyclic versions of the smoothed, averaged, and multitaper periodograms.
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