Electromyography (EMG) Data-Driven Load Classification using Empirical Mode Decomposition and Feature Analysis

Generally, two or more faults occur simultaneously in the bearings. These Compound Faults (CF) in bearing, are most difficult type of faults to detect, by any data-driven method including machine learning. Hence, it is a primary requirement to decompose the fault vibration signals logically, so that frequencies can be grouped in parts. Empirical Mode Decomposition (EMD) is one of the simplest techniques of decomposition of signals. In this paper we have used Ensemble Empirical Mode Decomposition (EEMD) technique for compound fault detection/identification. Ensembled Empirical Mode Decomposition is found useful, where a white noise helps to detect the bearing frequencies. The graphs show clearly the capability of EEMD to detect the multiple faults in rolling bearings.

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Empirical Mode Decomposition and its Extensions Applied to EEG Analysis: A Review

Advances in Data Science and Adaptive Analysis ◽

10.1142/s2424922x18400016 ◽

2018 ◽

Vol 10 (02) ◽

pp. 1840001 ◽

Cited By ~ 6

Author(s):

Catherine M. Sweeney-Reed ◽

Slawomir J. Nasuto ◽

Marcus F. Vieira ◽

Adriano O. Andrade

Keyword(s):

Empirical Mode Decomposition ◽

Data Driven ◽

Eeg Analysis ◽

Time Frequency ◽

Computer Interfaces ◽

Mode Decomposition ◽

Eeg Data ◽

Wide Range ◽

Challenges And Opportunities ◽

Data Driven Approach

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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Application of EMD to Integrated Signal Trend Extraction

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.591-593.2072 ◽

2012 ◽

Vol 591-593 ◽

pp. 2072-2076 ◽

Cited By ~ 1

Author(s):

Ye Qu Chen ◽

Wen Zheng ◽

Xie Ben Wei

Keyword(s):

Empirical Mode Decomposition ◽

Low Frequency ◽

Data Driven ◽

Pass Filter ◽

Effective Implementation ◽

Trend Extraction ◽

Mode Decomposition ◽

Calculation Results ◽

High Pass ◽

Emd Method

Huang’s data-driven technique of Empirical Mode Decomposition (EMD) is presented, and issues related to its effective implementation are discussed. Integrating signal directly will produce a trend, it will cause distortion and interfere with the calculation results. This paper discusses the reasons that cause the integrated signal trend, compares the different methods for extracting trend. The traditional steps use the linear fitting and a high-pass filter to remove low frequency signal to extract trend. This paper uses Empirical Mode Decomposition (EMD) method to extract integrated signals trend, discussed the advantages of Empirical Mode Decomposition (EMD) method in this case, proves that Empirical Mode Decomposition (EMD) has a good application in integrated signal trend extraction.

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