Detection and Diagnosis of Gear Fault By the Single Gear Tooth Analysis Technique

This paper presents a procedure of sing le gear tooth analysis for early detection and diagnosis of gear faults. The objec tive of this procedure is to develop a method for more sensitive detection of th e incipient faults and locating the faults in the gear. The main idea of the sin gle gear tooth analysis is that the vibration signals collected with a high samp ling rate are divided into a number of segments with the same time interval. The number of signal segments is equal to that of the gear teeth. The analysis of i ndividual segments reveals more sensitively the changes of the vibration signals in both time and frequency domain caused by gear faults. In addition, the locat ion of a failed tooth can be indicated in terms of the position of the segment t hat deviates from the normal segments. An experimental investigation verified th e advantages of the single gear tooth analysis.

Influence of Temperature on Stacking Fault Energy and Creep Mechanism of a Single Crystal Nickel-based Superalloy

The influence of temperatures on the stacking fault energies and deformation mechanism of a Re- containing single crystal nickel-based superalloy during creep at elevated temperatures was investigated by means of calculating the stacking fault energy of alloy, measuring creep properties and performing contrast analysis of dislocation configuration. The results show that the alloy at 760 ℃ possesses lower stacking fault energy, and the stacking fault of alloy increases with increasing temperature. The defor- mation mechanism of alloy during creep at 760 ℃ is 7＇ phase sheared by 〈110〉 super-dislocations, which may be decomposed to form the configuration of Shockley partials plus super-lattice intrinsic stacking fault, while the deformation mechanism of alloy during creep at 1070 ℃ is the screw or edge super- dislocations shearing into the rafted 7＇ phase. But during creep at 7（50 and 980 ℃, some super- dislocations shearing into 7＇ phase may cross-slip from the {111} to {100} planes to form the K-W locks with non-plane core structure, which may restrain the dislocations slipping to enhance the creep resis- tance of alloy at high temperature. The interaction between the Re and other elements may decrease the diffusion rate of atoms to improve the microstructure stability, which is thought to be the main reason why the K-W locks are to be kept in the Re-containing superalloy during creep at 980 ℃.

Stacking fault energy in some single crystals

The stacking fault energy of single crystals has been reported using the peak shift method.Presently studied all single crystals are grown by using a direct vapor transport（DVT） technique in the laboratory.The structural characterizations of these crystals are made by XRD.Considerable variations are shown in deformation （α） and growth（β） probabilities in single crystals due to off-stoichiometry,which possesses the stacking fault in the single crystal.

Faulty Feeder Identification in Resonant Grounding Distribution Networks Based on Deep Learning and Transfer Learning

Identification of faulty feeders in resonant grounding distribution networks remains a significant challenge dueto the weak fault current and complicated working conditions.In this paper, we present a deep learning-based multi-labelclassification framework to reliably distinguish the faulty feeder.Three different neural networks (NNs) including the multilayerperceptron, one-dimensional convolutional neural network (1DCNN), and 2D CNN are built. However, the labeled data maybe difficult to obtain in the actual environment. We use thesimplified simulation model based on a full-scale test field (FSTF)to obtain sufficient labeled source data. Being different frommost learning-based methods, assuming that the distribution ofsource domain and target domain is identical, we propose asamples-based transfer learning method to improve the domainadaptation by using samples in the source domain with properweights. The TrAdaBoost algorithm is adopted to update theweights of each sample. The recorded data obtained in the FSTFare utilized to test the domain adaptability. According to ourvalidation and testing, the validation accuracies are high whenthere is sufficient labeled data for training the proposed NNs.The proposed 2D CNN has the best domain adaptability. TheTrAdaBoost algorithm can help the NNs to train an efficientclassifier that has better domain adaptation. It has been thereforeconcluded that the proposed method, especially the 2D CNN, issuitable for actual distribution networks.

Fault line selection in cooperation with multi-mode grounding control for the floating nuclear power plant grid

The Floating nuclear power plant grid is composed of power generation,in-station power supply and external power delivery.To ensure the safety of the nuclear island,the in-station system adopts a special power supply mode,while the external power supply needs to be adapted to different types of external systems.Because of frequent single phase-ground faults and various fault forms,the fault line selection protection should be accurate,sensitive and adaptive.This paper presents a fault line selection method in cooperation with multi-mode grounding control.Based on the maximum united energy entropy ratio(MUEER),the optimal wavelet basis function and decomposition scale are adaptively chosen,while the fault line is selected by wavelet transform modulus maxima(WTMM).For high-impedance faults(HIFs),to enlarge the fault feature,the system grounding mode can be switched by the multi-mode grounding control.Based on the characteristic of HIFs,the fault line can be selected by comparing phase differences of zero-sequence current mutation and fault phase voltage mutation before and after the fault.Simulation results using MATLAB/Simulink show the effectiveness of the proposed method in solving the protection problems.