This paper considers the entropy based feature extraction method for the fault diagnosis of rolling bearings in automobile production line, where the fault information is difficult to identify due to the strong nonlinear and non-stationary characteristics of the fault vibration signals. In our work, a novel entropy based method called generalized composite multiscale diversity entropy (GCMDiEn) is developed. This method can effectively track the inside pattern changes of the time series by the description of cosine similarity between adjacent orbits. Unlike most of the existing entropy based results which concentrate on the static orderliness, we analyze the dynamic complexity characteristics of the arbitrary time series. Moreover, compared with the multiscale diversity entropy, GCMDiEn calculates all coarse-grained time series entropy value with the same scale and extends the first-order moment to second-order moment to mitigate the shortcomings of the short time series instability and losing part of the frequency region information. This paper proposed a new rolling bearing fault diagnosis framework which combined the GCMDiEn with the empirical wavelet transform (EWT), Laplacian score (LS), and particle swarm optimization-based support vector machine (PSO-SVM). Finally, the simulation results show the superiority of the GCMDiEn method over the multiscale diversity entropy method. The proposed framework has a higher fault recognition rate (99.38%) than the existing methods.
In order to reveal the effect laws of boosting velocity on forming quality of tube bending. A three dimensional (3D) elastic plastic finite element (FE) model of whole process of high-strength TA18 tubes in numerical control (NC) bending was established based on the FE code of ABAQUS, and its reliability was validated by using the experimental results in literature. Then, the effect laws of boosting velocity on deformation behaviors of high-strength TA18 tubes in NC bending were explored with respect to multiple defects such as wall thinning, wall thickening, cross section deformation and springback. The results show that wall thinning ratio decreases with the increasing of boosting velocity; wall thickening ratio increases with the increasing of boosting velocity; cross section deformation ratio decreases with the increasing of boosting velocity; springback decreases slightly with the increasing of boosting velocity.
Owing to the unique properties of high strength Ti-3Al-2.5V tube, wall thinning is prone to happen in tube bending. To accomplish tube precision bending forming, the wall thinning needs to be precisely predicted and effectively controlled. A theoretical model considering circumferential deformation was presented to reveal the inherent relationship for wall thickness distribution versus tube geometrical parameters, and its reliability was validated by published experimental results. Using finite element (FE) simulation combined with orthogonal test, the effect rules and significances of process parameters on wall thinning in numerical control (NC) bending for high strength Ti-3Al-2.5V tube were investigated. The results show that the significant factors sort from the largest to the smallest for wall thinning in tube bending as the clearance of tube versus mandrel Cm, axial feed of mandrel e, friction of tube versus mandrel fm, clearance of tube versus bending die Cb, clearance of tube versus wiper die Cw and push assistant speed of pressure die vp; the wall thinning decreases for the larger Cm and vp, while increases for the larger Cb, Cw, fm, and e. Furthermore, the prediction model of maximum wall thinning ratio was established based on the significant process parameters by multiple linear regression method. Compared with the results of orthogonal test, the relative error of that is less than 6.5%, which can be employed for rapidly predicting the wall thinning in tube bending.
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