Fault Classification of Rolling Bearing Based on Time-Frequency Generalized Dimension of Vibration Signal and ANFIS
نویسنده
چکیده
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 time-frequency domain of vibration signal could be extracted, then adaptive fuzzy neural network (ANFIS) was used in signal classification. Experiments showed that this method can realize fault classify of rolling bearing effectively, it is feasible in engineering application.
منابع مشابه
A DWT and SVM based method for rolling element bearing fault diagnosis and its comparison with Artificial Neural Networks
A classification technique using Support Vector Machine (SVM) classifier for detection of rolling element bearing fault is presented here. The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditio...
متن کاملRolling Bearing Fault Analysis by Interpolating Windowed DFT Algorithm
This paper focuses on the problem of accurate Fault Characteristic Frequency (FCF) estimation of rolling bearing. Teager-Kaiser Energy Operator (TKEO) demodulation has been applied widely to rolling bearing fault detection. FCF can be extracted from vibration signals, which is pre-treatment by TEKO demodulation method. However, because of strong noise background of fault vibration signal, it is...
متن کاملA Novel Intelligent Fault Diagnosis Approach for Critical Rotating Machinery in the Time-frequency Domain
The rotating machinery is a common class of machinery in the industry. The root cause of faults in the rotating machinery is often faulty rolling element bearings. This paper presents a novel technique using artificial neural network learning for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (harmmean and median), whic...
متن کاملWheelset Bearing Vibration Analysis Based on Nonlinear Dynamical Method
The wheelset bearing play an important role in train running safety. In this paper, we introduce the nonlinear dynamical method to study the wheelset bearing vibration signal. Based on the comparative studies of the phase graph and the correlation dimension of the vibration signals under normal condition, outer peeling fault condition and rolling element electric erosion fault condition, the fo...
متن کاملA Method of Bearing Fault Feature Extraction Based on Improved Wavelet Packet and Hilbert Analysis
In order to supply a gap of current resonance vibration and STFT demodulation method applied to rolling bearing fault feature extraction of city rail vehicle, a fault diagnosis method for rolling bearing is presented, which is based on the integration of improved wavelet packet, frequency energy analysis and Hilbert marginal spectrum. When faults occur in rolling bearing, the energy of the roll...
متن کامل