A data-driven approach for estimating the time-frequency binary mask

Empirical mode decomposition (EMD) provides an adaptive, data-driven approach to time–frequency analysis, yielding components from which local amplitude, phase, and frequency content can be derived. Since its initial introduction to electroencephalographic (EEG) data analysis, EMD has been extended to enable phase synchrony analysis and multivariate data processing. EMD has been integrated into a wide range of applications, with emphasis on denoising and classification. We review the methodological developments, providing an overview of the diverse implementations, ranging from artifact removal to seizure detection and brain–computer interfaces. Finally, we discuss limitations, challenges, and opportunities associated with EMD for EEG analysis.

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A data-driven approach for seismic damage detection of shear-type building structures using the fractal dimension of time-frequency features

Structural Control and Health Monitoring ◽

10.1002/stc.1528 ◽

2012 ◽

Vol 20 (9) ◽

pp. 1191-1210 ◽

Cited By ~ 15

Author(s):

Hui Li ◽

Dongwang Tao ◽

Yong Huang ◽

Yuequan Bao

Keyword(s):

Fractal Dimension ◽

Damage Detection ◽

Seismic Damage ◽

Data Driven ◽

Building Structures ◽

Time Frequency ◽

Data Driven Approach ◽

Shear Type ◽

Frequency Features

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A Data-Driven Approach for the Diagnosis of Mechanical Systems Using Trained Subtracted Signal Spectrograms

Sensors ◽

10.3390/s19051055 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1055 ◽

Cited By ~ 2

Author(s):

Jiung Huh ◽

Huan Pham Van ◽

Soonyoung Han ◽

Hae-Jin Choi ◽

Seung-Kyum Choi

Keyword(s):

Frequency Conversion ◽

Time Synchronization ◽

Expert Knowledge ◽

Health Management ◽

Industrial Robot ◽

Mechanical Systems ◽

Data Driven ◽

Time Frequency ◽

Data Driven Approach ◽

The Difference

Toward the prognostic and health management of mechanical systems, we propose and validate a novel effective, data-driven fault diagnosis method. In this method, we develop a trained subtracted spectrogram, the so called critical information map (CIM), identifying the difference between the signal spectrograms of normal and abnormal status. We believe this diagnosis process may be implemented in an autonomous manner so that an engineer employs it without expert knowledge in signal processing or mechanical analyses. Firstly, the CIM method applies sequential and autonomous procedures of time-synchronization, time frequency conversion, and spectral subtraction on raw signal. Secondly, the subtracted spectrogram is then trained to be a CIM for a specific mechanical system failure by finding out the optimal parameters and abstracted information of the spectrogram. Finally, the status of a system health can be monitored accurately by comparing the CIM with an acquired signal map in an automated and timely manner. The effectiveness of the proposed method is successfully validated by employing a diagnosis problem of six-degree-of-freedom industrial robot, which is the diagnosis of a non-stationary system with a small amount of training datasets.

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