Search

A novel bearing fault diagnosis method combining feature extraction based on wavelet packets quantifiers and pattern recognition method based on improved initialization method of Discriminative K-SVD (D-KSVD) algorithm is proposed. In D-KSVD algorithm, the representational power of dictionary and discriminative ability of classifier are seriously affected by their initialization values. Therefore, the improved initialization method of D-KSVD is presented and employed for bearing fault diagnosis. The improvement is that during the initialization of training stage, subdictionaries corresponding to each category are trained by K-SVD separately and then the initial dictionary is constructed by cascading the subdictionaries, which can completely represent the characteristics of all categories, and as for the initialization of linear classifier, naive Bayesian classifier is utilized. The experimental results show that under the same parameters the improved D-KSVD has better classification ability compared with traditional D-KSVD and some other classification methods. [ABSTRACT FROM AUTHOR]

Spectral kurtosis (SK) is an algorithm that gives an indication of how kurtosis varies with frequency. A frequency band that contains abundant information, especially the impact signal, can be tracked by calculating SK. In the present article, SK combined with Autoregressive AR model, was applied into the fault diagnosis and condition monitoring of bearings. Accelerated life test of rolling bearings in Hangzhou Bearing Test & Research Center (HBRC) was performed to collect vibration data over their entire lifetime (normal-fault-failure). The result shows that SK can detect early incipient fault by eliminating some other interfering frequency components. In addition, it can detect fault 5 min earlier than root mean value (RMS). This fault detection in advance is significant for condition monitoring. [ABSTRACT FROM AUTHOR]

finite element model is used for flexural vibration analysis of a static (non-rotating) rotor with open cracks; the stiffness matrices of the cracked elements are obtained using transfer matrix analysis and local flexibility theorem. Through numerical simulation, the effects of the slenderness ratio and the crack depth on the mode shapes and the changes in the eigenfrequencies of the cracked rotor are investigated; the variations of the changes in eigenfrequencies with crack location are studied; and the ratios of the changes in the first two eigenfrequencies are discussed for rotors with two cracks. Then, crack identification algorithms for one unknown crack case and for two unknown cracks case are proposed, and illustrative examples are demonstrated to verify the validity of the proposed algorithms. [ABSTRACT FROM AUTHOR]