A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to generate fuzzy memberships.In the algorithm,sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC.Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm,the penalty factor and kernel parameter of which are optimized by GA.Finally,the model is executed for multi-class fault diagnosis of rolling element bearings.The results show that the presented model achieves high performances both in identifying fault types and fault degrees.The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization. 相似文献
A pore-array intensified tube-in-tube microchannel (PA-TMC), which is characterized by high throughput and low pressure drop, was developed as a gas–liquid contactor. The sulfite oxidation method was used to determine the oxygen efficiency (φ) and volumetric mass transfer coefficient (kLa) of PA-TMC, and the mass transfer amount per unit energy (ε) was calculated by using the pressure drop. The effects of structural and operating parameters were investigated systematically, and the two-phase flow behavior was monitored by using a charge-coupled device imaging system. The results indicated that the gas absorption efficiency and mass transfer performance of the PA-TMC were improved with increasing pore number, flow rate, and number of helical coil turns and decreasing pore size, row number, annular size, annular length, and surface tension. The φ, ε and kLa of PA-TMC could reach 31.3%, 1.73 × 10−4 mol/J, and 7.0 s−1, respectively. The Sherwood number was correlated with the investigated parameters to guide the design of PA-TMC in gas absorption and mass transfer processes.