Journal ArticleDOI
EDM-Fuzzy: An Euclidean Distance Based Multiscale Fuzzy Entropy Technology for Diagnosing Faults of Industrial Systems
TLDR
A Euclidean distance based multiscale fuzzy entropy (EDM-Fuzzy), which measures the similarity of two vectors with continuous values from zero to one based on the Euclideans distance of the two vectors, and obtains higher accuracy in detecting the bearing faults than the state-of-the-art entropy technologies.Abstract:
Sample entropy (SampEn) technologies have been widely applied in diagnosing the faults of industrial systems. However, there are two disadvantages of these technologies. First, all of these technologies measure the distance of two vectors solely based on the maximum distance between the corresponding elements in the two vectors, which is not able to fully reflect the distance of the two vectors. Second, these methodologies measure the similarity of two vectors with either zero or one, which may cause sudden changes in entropy values. Therefore, we proposed a Euclidean distance based multiscale fuzzy entropy (EDM-Fuzzy), which measures the similarity of two vectors with continuous values from zero to one based on the Euclidean distance of the two vectors. The results from the synthetic and real signals demonstrated that EDM-Fuzzy has higher accuracy in measuring the complexity of signals. As a result, EDM-Fuzzy obtains a higher accuracy in detecting the bearing faults than the state-of-the-art entropy technologies.read more
Citations
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Journal ArticleDOI
Hierarchical Amplitude-Aware Permutation Entropy-Based Fault Feature Extraction Method for Rolling Bearings
TL;DR: Huang et al. as mentioned in this paper proposed a hierarchical amplitude-aware permutation entropy (HAAPE) to solve complex time series in a new dynamic change analysis, and a fault feature selection strategy based on HAAPE was put forward to select the bearing fault characteristics after the application of the least common multiple in singular value decomposition (LCM-SVD) method to the fault vibration signal.
Journal ArticleDOI
Cross-Domain Fault Diagnosis Based on Improved Multi-Scale Fuzzy Measure Entropy and Enhanced Joint Distribution Adaptation
TL;DR: Experimental results demonstrate that improved multi-scale fuzzy measure entropies have better distinguishing ability and transferable ability than several existing entropy methods, and the enhanced joint distribution adaptation is more generalized to transfer scenarios with complex data distributions.
Journal ArticleDOI
An Improved Approach of Incomplete Information Fusion and Its Application in Sensor Data-Based Fault Diagnosis
Yutong Chen,Yongchuan Tang +1 more
TL;DR: This paper proposes a new method framework to process uncertain information and fuse incomplete data based on an extension to the Deng entropy in the open world assumption, negation of basic probability assignment (BPA), and the generalized combination rule.
Journal ArticleDOI
Transition Permutation Entropy and Transition Dissimilarity Measure: Efficient Tools for Fault Detection of Railway Vehicle Systems
TL;DR: In this article , a transition permutation entropy (TPE) and a transition dissimilarity measure (TDM) through the transition matrix are proposed to evaluate the complexity of systems.
Journal ArticleDOI
A novel approach of dependence measure for complex signals
Du Shang,Pengjian Shang +1 more
TL;DR: This work proposes the dependence measure (DM) based on the RDC and the phase space reconstruction theory, aiming to capture linear and nonlinear dynamical features from various kinds of complex signals with higher accuracy, and combines the DM and the CSE to construct the DM-CSE plane.
References
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Journal ArticleDOI
A mathematical theory of communication
TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
Journal ArticleDOI
Training feedforward networks with the Marquardt algorithm
TL;DR: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks and is found to be much more efficient than either of the other techniques when the network contains no more than a few hundred weights.
Journal ArticleDOI
Physiological time-series analysis using approximate entropy and sample entropy
TL;DR: A new and related complexity measure is developed, sample entropy (SampEn), and a comparison of ApEn and SampEn is compared by using them to analyze sets of random numbers with known probabilistic character, finding SampEn agreed with theory much more closely than ApEn over a broad range of conditions.
Journal ArticleDOI
Approximate entropy as a measure of system complexity.
TL;DR: Analysis of a recently developed family of formulas and statistics, approximate entropy (ApEn), suggests that ApEn can classify complex systems, given at least 1000 data values in diverse settings that include both deterministic chaotic and stochastic processes.
Journal ArticleDOI
Three approaches to the quantitative definition of information
TL;DR: In this article, three approaches to the quantitative definition of information are presented: information-based, information-aware and information-neutral approaches to quantifying information in the context of information retrieval.