Local Entropy Optimization–Adaptive Demodulation Reassignment Transform for Advanced Analysis of Non-Stationary Mechanical Signals

This research proposes a new method for time–frequency analysis, termed the Local Entropy Optimization–Adaptive Demodulation Reassignment Transform (LEOADRT), which is specifically designed to efficiently analyze complex, non-stationary mechanical vibration signals that exhibit multiple instantaneou...

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Main Authors: Yuli Niu, Zhongchao Liang, Hengshan Wu, Jianxin Tan, Tianyang Wang, Fulei Chu
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Entropy
Subjects:
Online Access:https://www.mdpi.com/1099-4300/27/7/660
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author Yuli Niu
Zhongchao Liang
Hengshan Wu
Jianxin Tan
Tianyang Wang
Fulei Chu
author_facet Yuli Niu
Zhongchao Liang
Hengshan Wu
Jianxin Tan
Tianyang Wang
Fulei Chu
author_sort Yuli Niu
collection DOAJ
description This research proposes a new method for time–frequency analysis, termed the Local Entropy Optimization–Adaptive Demodulation Reassignment Transform (LEOADRT), which is specifically designed to efficiently analyze complex, non-stationary mechanical vibration signals that exhibit multiple instantaneous frequencies or where the instantaneous frequency ridges are in close proximity to each other. The method introduces a demodulation term to account for the signal’s dynamic behavior over time, converting each component into a stationary signal. Based on the local optimal theory of Rényi entropy, the demodulation parameters are precisely determined to optimize the time–frequency analysis. Then, the energy redistribution of the ridges already generated in the time–frequency map is performed using the maximum local energy criterion, significantly improving time–frequency resolution. Experimental results demonstrate that the performance of the LEOADRT algorithm is superior to existing methods such as SBCT, EMCT, VSLCT, and GLCT, especially in processing complex non-stationary signals with non-proportionality and closely spaced frequency intervals. This method provides strong support for mechanical fault diagnosis, condition monitoring, and predictive maintenance, making it particularly suitable for real-time analysis of multi-component and cross-frequency signals.
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institution Kabale University
issn 1099-4300
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publishDate 2025-06-01
publisher MDPI AG
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series Entropy
spelling doaj-art-8595934bea6d4427abcd416c0b88bb6c2025-08-20T03:32:26ZengMDPI AGEntropy1099-43002025-06-0127766010.3390/e27070660Local Entropy Optimization–Adaptive Demodulation Reassignment Transform for Advanced Analysis of Non-Stationary Mechanical SignalsYuli Niu0Zhongchao Liang1Hengshan Wu2Jianxin Tan3Tianyang Wang4Fulei Chu5Department of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaSchool of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100091, ChinaDepartment of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaThis research proposes a new method for time–frequency analysis, termed the Local Entropy Optimization–Adaptive Demodulation Reassignment Transform (LEOADRT), which is specifically designed to efficiently analyze complex, non-stationary mechanical vibration signals that exhibit multiple instantaneous frequencies or where the instantaneous frequency ridges are in close proximity to each other. The method introduces a demodulation term to account for the signal’s dynamic behavior over time, converting each component into a stationary signal. Based on the local optimal theory of Rényi entropy, the demodulation parameters are precisely determined to optimize the time–frequency analysis. Then, the energy redistribution of the ridges already generated in the time–frequency map is performed using the maximum local energy criterion, significantly improving time–frequency resolution. Experimental results demonstrate that the performance of the LEOADRT algorithm is superior to existing methods such as SBCT, EMCT, VSLCT, and GLCT, especially in processing complex non-stationary signals with non-proportionality and closely spaced frequency intervals. This method provides strong support for mechanical fault diagnosis, condition monitoring, and predictive maintenance, making it particularly suitable for real-time analysis of multi-component and cross-frequency signals.https://www.mdpi.com/1099-4300/27/7/660time–frequency analysisRényi entropynon-stationary signalsmechanical vibration signalsfault diagnosis
spellingShingle Yuli Niu
Zhongchao Liang
Hengshan Wu
Jianxin Tan
Tianyang Wang
Fulei Chu
Local Entropy Optimization–Adaptive Demodulation Reassignment Transform for Advanced Analysis of Non-Stationary Mechanical Signals
Entropy
time–frequency analysis
Rényi entropy
non-stationary signals
mechanical vibration signals
fault diagnosis
title Local Entropy Optimization–Adaptive Demodulation Reassignment Transform for Advanced Analysis of Non-Stationary Mechanical Signals
title_full Local Entropy Optimization–Adaptive Demodulation Reassignment Transform for Advanced Analysis of Non-Stationary Mechanical Signals
title_fullStr Local Entropy Optimization–Adaptive Demodulation Reassignment Transform for Advanced Analysis of Non-Stationary Mechanical Signals
title_full_unstemmed Local Entropy Optimization–Adaptive Demodulation Reassignment Transform for Advanced Analysis of Non-Stationary Mechanical Signals
title_short Local Entropy Optimization–Adaptive Demodulation Reassignment Transform for Advanced Analysis of Non-Stationary Mechanical Signals
title_sort local entropy optimization adaptive demodulation reassignment transform for advanced analysis of non stationary mechanical signals
topic time–frequency analysis
Rényi entropy
non-stationary signals
mechanical vibration signals
fault diagnosis
url https://www.mdpi.com/1099-4300/27/7/660
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AT zhongchaoliang localentropyoptimizationadaptivedemodulationreassignmenttransformforadvancedanalysisofnonstationarymechanicalsignals
AT hengshanwu localentropyoptimizationadaptivedemodulationreassignmenttransformforadvancedanalysisofnonstationarymechanicalsignals
AT jianxintan localentropyoptimizationadaptivedemodulationreassignmenttransformforadvancedanalysisofnonstationarymechanicalsignals
AT tianyangwang localentropyoptimizationadaptivedemodulationreassignmenttransformforadvancedanalysisofnonstationarymechanicalsignals
AT fuleichu localentropyoptimizationadaptivedemodulationreassignmenttransformforadvancedanalysisofnonstationarymechanicalsignals