Vibration Signal Fault Diagnosis Based on Generalized Fractal Dimension
نویسندگان
چکیده
To process the vibration signal fault diagnosis automatically and effectively, a method to calculate the generalized fractal dimension (Dq) based on the traditional G-P correlation dimension algorithm is presented. First, the vibration signal, a kind of random fractal that meets the statistical self-similarity, is reconstructed according to the phase space reconstruction theory. Second, for various weight factor values (q), calculation of the qth-order correlation integral, followed by the Dq, is processed. Range of the weight factor is discussed. With the generalized fractal dimension, the dynamic characteristics of the system are then reproduced more entirely and accurately. The effectiveness of the proposed method is verified by calculating the generalized fractal dimension for various measured vibration signals. The generalized fractal dimension distinguishes the state of vibration signal unambiguously. The analysis also indicates that the fluctuation amplitude of Dq is related to the frequency distribution of vibration signal. This would be helpful for the vibration signal fault diagnosis.
منابع مشابه
Research on Fractal Method for Soft Fault Diagnosis of Nonlinear Analog Circuits
The soft fault diagnosis of nonlinear analog circuits is an important guarantee of the stable and reliable operation of electronic products. In view of the low accuracy and heavy computation load of current soft fault diagnosis methods for nonlinear analog circuits, this paper presents a soft fault diagnosis method for nonlinear analog circuits based on fractal theory. Analyzing the single-frac...
متن کاملFault Classification of Rolling Bearing Based on Time-Frequency Generalized Dimension of Vibration Signal and ANFIS
Research shows that multi-fractal can not only exhibit the singular probability distribution form of the fractal signal completely, but also increase the fine level of signal geometrical characteristics and local scaling behavior. Based on multi fractal dimension calculation of time-frequency matrix of vibration signal of rolling bearing in this paper, energy distribution characteristics of tim...
متن کاملA Fault Diagnosis Method for Automaton based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition
In the fault diagnosis of automaton, the vibration signal presents non-stationary and non-periodic, which make it difficult to extract the fault features. To solve this problem, an automaton fault diagnosis method based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD) was proposed. Based on the advantages of the morphological component analysis method i...
متن کاملA Fault Diagnosis Method for Automaton Based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition
In the fault diagnosis of automaton, the vibration signal presents non-stationary and non-periodic, which make it difficult to extract the fault features. To solve this problem, an automaton fault diagnosis method based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD) was proposed. Based on the advantages of the morphological component analysis method i...
متن کاملA Rolling Element Bearing Fault Diagnosis Approach Based on Multifractal Theory and Gray Relation Theory
Bearing failure is one of the dominant causes of failure and breakdowns in rotating machinery, leading to huge economic loss. Aiming at the nonstationary and nonlinear characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a novel rolling element bearing fault diagnosis method based on multifractal theory and gray ...
متن کامل