A Comparative Analysis of Signal Decomposition Techniques for Structural Health Monitoring on an Experimental Benchmark [PDF]
Sensors, 2021 Signal Processing is, arguably, the fundamental enabling technology for vibration-based Structural Health Monitoring (SHM), which includes damage detection and more advanced tasks.
Cecilia Surace, Marco Civera
exaly +3 more sources
Resonance-Based Sparse Signal Decomposition and its Application in Mechanical Fault Diagnosis: A Review [PDF]
Sensors, 2017 Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals ...
exaly +3 more sources
Data-driven nonstationary signal decomposition approaches: a comparative analysis. [PDF]
Sci Rep, 2023 Signal decomposition (SD) approaches aim to decompose non-stationary signals into their constituent amplitude- and frequency-modulated components. This represents an important preprocessing step in many practical signal processing pipelines, providing ...
Eriksen T, Rehman NU.
europepmc +2 more sources
Difference mode decomposition for adaptive signal decomposition
Mechanical Systems and Signal Processing, 2023 Tangbin Xia +2 more
exaly +3 more sources
Temporal Graph Signal Decomposition [PDF]
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021 Temporal graph signals are multivariate time series with individual components associated with nodes of a fixed graph structure. Data of this kind arises in many domains including activity of social network users, sensor network readings over time, and ...
M. McNeil, Lin Zhang, P. Bogdanov
semanticscholar +6 more sources
Connecting EEG signal decomposition and response selection processes using the theory of event coding framework. [PDF]
Hum Brain Mapp, 2020 The neurophysiological mechanisms underlying the integration of perception and action are an important topic in cognitive neuroscience. Yet, connections between neurophysiology and cognitive theoretical frameworks have rarely been established. The theory
Takacs A +5 more
europepmc +2 more sources
IEEE Access, 2017 Effective signal processing methods are essential for machinery fault diagnosis. Most conventional signal processing methods lack adaptability, thus being unable to well extract the embedded meaningful information.
Zhipeng Feng, Dong Zhang, Ming J. Zuo
doaj +3 more sources
EM-DeepSD: A Deep Neural Network Model Based on Cell-Free DNA End-Motif Signal Decomposition for Cancer Diagnosis [PDF]
DiagnosticsBackground and Objectives: The accurate discrimination between patients with and without cancer using their cell-free DNA (cfDNA) is crucial for early cancer diagnosis.
Zhi-Yang Zhao +6 more
doaj +2 more sources
Incipient Fault Diagnosis Method via Joint Adaptive Signal Decomposition
IEEE Sensors JournalIdentification and diagnosis of the incipient fault in electromechanical systems is a challenging task due to its weakness and concealment of magnitude.
De Zhu, Dawei Zhao, Dong Sun
exaly +2 more sources
Nonstationary Signal Decomposition for Dummies [PDF]
Advances in Mechanics and Mathematics, 2017 How can I decompose a nonstationary signal? What are the advantages of using the most recent methods available in the literature versus using classical methods like (short time) Fourier transform or wavelet transform?
A. Cicone
semanticscholar +4 more sources