ArticleSensors (Basel, Switzerland)2018
Improved Dynamic Mode Decomposition and Its Application to Fault Diagnosis of Rolling Bearing.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Rapid Method to Rank Sound Sources in a Reverberant Environment.Sensors (Basel, Switzerland) · 2026Article
- CNN-based framework for Alzheimer's disease detection from EEG via dynamic mode decomposition.Frontiers in neuroinformatics · 2025Article
- A Novel Study on a Generalized Model Based on Self-Supervised Learning and Sparse Filtering for Intelligent Bearing Fault Diagnosis.Sensors (Basel, Switzerland) · 2023Article
- Sensor Fault Diagnostics Using Physics-Informed Transfer Learning Framework.Sensors (Basel, Switzerland) · 2022Article
- A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing.Sensors (Basel, Switzerland) · 2019Article
- Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection.Sensors (Basel, Switzerland) · 2018Article
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4 authors.
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Abstract
To solve the intractable problems of optimal rank truncation threshold and dominant modes selection strategy of the standard dynamic mode decomposition (DMD), an improved DMD algorithm is introduced in this paper. Distinct from the conventional methods, a convex optimization framework is introduced by applying a parameterized non-convex penalty function to obtain the optimal rank truncation number. This method is inspirited by the performance that it is more perfectible than other rank truncation methods in inhibiting noise disturbance. A hierarchical and multiresolution application similar to the process of wavelet packet decomposition in modes selection is presented so as to improve the algorithm's performance. With the modes selection strategy, the frequency spectrum of the reconstruction signal is more readable and interference-free. The improved DMD algorithm successfully extracts the fault characteristics of rolling bearing fault signals when it is utilized for mechanical signal feature extraction. Results demonstrated that the proposed method has good application prospects in denoising and fault feature extraction for mechanical signals.
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