The Time-Frequency Filtering (TFF) Method Used in Early Detection of Gear Faults in Variable Load and Dimensions Defect

2018 ◽  
pp. 56-67
Author(s):  
Hafida Mahgoun ◽  
Fakher Chaari ◽  
Ahmed Felkaoui ◽  
Mohamed Haddar
Keyword(s):  
Early Detection ◽  
Variable Load ◽  
Frequency Filtering ◽  
Time Frequency

Towards Early Detection and Burden Estimation of Atrial Fibrillation in an Ambulatory Free-living Environment

2021 ◽  
Vol 5 (2) ◽  
pp. 1-19
Author(s):  
Hanbin Zhang ◽  
Li Zhu ◽  
Viswam Nathan ◽  
Jilong Kuang ◽  
Jacob Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Early Detection ◽  
Daily Living ◽  
Research Study ◽  
Heart Rhythm ◽  
Living Environment ◽  
User Engagement ◽  
Free Living ◽  
Time Frequency

Early detection and accurate burden estimation of atrial fibrillation (AFib) can provide the foundation for effective physician treatment. New approaches to accomplish this have attracted tremendous attention in recent years. In this paper, we develop a novel passive smartwatch-based system to detect AFib episodes and estimate the AFib burden in an ambulatory free-living environment without user engagement. Our system leverages a built-in PPG sensor to collect heart rhythm without user engagement. Then, a data preprocessor module includes time-frequency (TF) analysis to augment features in both the time and frequency domain. Finally, a lightweight multi-view convolutional neural network consisting of 19 layers achieves the AFib detection. To validate our system, we carry out a research study that enrolls 53 participants across three months, where we collect and annotate more than 27,622 hours of data. Our system achieves an average of 91.6% accuracy, 93.0% specificity, and 90.8% sensitivity without dropping any data. Moreover, our system takes 0.51 million parameters and costs 5.18 ms per inference. These results reveal that our proposed system can provide a clinical assessment of AFib in daily living.

scholarly journals New State Identification Method for Rotating Machinery under Variable Load Conditions Based on Hybrid Entropy Features and Joint Distribution Adaptation

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Xiaoming Xue ◽  
Nan Zhang ◽  
Suqun Cao ◽  
Wei Jiang ◽  
Jianzhong Zhou ◽  
...  
Keyword(s):  
Joint Distribution ◽  
Operating Conditions ◽  
Variable Load ◽  
Rolling Element Bearings ◽  
Time Frequency ◽  
Identification Method ◽  
State Identification ◽  
Rolling Element ◽  
Load Conditions ◽  
Testing Set

Fault identification under variable operating conditions is a task of great importance and challenge for equipment health management. However, when dealing with this kind of issue, traditional fault diagnosis methods based on the assumption of the distribution coherence of the training and testing set are no longer applicable. In this paper, a novel state identification method integrated by time-frequency decomposition, multi-information entropies, and joint distribution adaptation is proposed for rolling element bearings. At first, fast ensemble empirical mode decomposition was employed to decompose the vibration signals into a collection of intrinsic mode functions, aiming at obtaining the multiscale description of the original signals. Then, hybrid entropy features that can characterize the dynamic and complexity of time series in the local space, global space, and frequency domain were extracted from each intrinsic mode function. As for the training and testing set under different load conditions, all data was mapped into a reproducing space by joint distribution adaptation to reduce the distribution discrepancies between datasets, where the pseudolabels of the testing set and the final diagnostic results were obtained by the k-nearest neighbor algorithm. Finally, five cases with the training and testing set under variable load conditions were used to demonstrate the performance of the proposed method, and comparisons with some other diagnosis models combined with the same features and other dimensionality reduction methods were also discussed. The analysis results show that the proposed method can effectively recognize the multifaults of rolling element bearings under variable load conditions with higher accuracies and has sound practicability.

Seismic ground roll time–frequency filtering using the gaussian wavelet transform

2003 ◽  
Vol 318 (3-4) ◽  
pp. 551-561 ◽  
Author(s):  
G. Corso ◽  
P.S. Kuhn ◽  
L.S. Lucena ◽  
Z.D. Thomé
Keyword(s):  
Wavelet Transform ◽  
Frequency Filtering ◽  
Time Frequency ◽  
Ground Roll
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