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Multiscale symbolic fuzzy entropy: An entropy denoising method for weak feature extraction of rotating machinery
- Source :
- Mechanical Systems and Signal Processing. 162:108052
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
Abstract
- The entropy-based method has been demonstrated to be an effective approach to extract the fault features by estimating the complexity of signals, but how to remove the strong background noises in analyzing early weak impulsive signal remains unexplored. To solve this problem, this paper proposes symbolic fuzzy entropy (SFE) based on symbolic dynamic filtering and fuzzy entropy to eliminate the noises and improve the calculation efficiency. The main idea of SFE is to use symbolic dynamic filtering to remove the noise-related fluctuations while significantly simplifying the circulation calculation, thereby, generating better performance in resisting the background noises and high computation efficiency. The superiority of SFE is verified via two simulated signals and other three entropy methods. For comprehensive feature description, we further extend SFE into multiscale analysis by incorporating with the coarse gaining process, called MSFE. Experimental results demonstrate that the proposed MSFE method has the best performance in extracting weak fault characteristics compared with three existing MSE, MFE, and MPE methods.
- Subjects :
- 0209 industrial biotechnology
Computer science
Mechanical Engineering
Computation
Noise reduction
Feature extraction
Process (computing)
Aerospace Engineering
02 engineering and technology
Fault (power engineering)
01 natural sciences
Signal
Computer Science Applications
020901 industrial engineering & automation
Fuzzy entropy
Control and Systems Engineering
0103 physical sciences
Signal Processing
Entropy (energy dispersal)
010301 acoustics
Algorithm
Civil and Structural Engineering
Subjects
Details
- ISSN :
- 08883270
- Volume :
- 162
- Database :
- OpenAIRE
- Journal :
- Mechanical Systems and Signal Processing
- Accession number :
- edsair.doi...........2f4d6697150fb604ed05b9a5b88655d2