A hydraulic fault diagnosis method based on sliding-window spectrum feature and deep belief network

The vibration signal of hydraulic system contains abundant state information, so vibration testing technology is an effective way to realize the fault diagnosis of hydraulic system. However, the mapping relation between signal characteristic and system state is complex and the expression meaning of characteristic is obscure, which brings a great challenge to the hydraulic fault diagnosis. The DBN, a newly proposed deep learning model, has an advantage of autonomously learning and reasoning. And it is good at studying the concealed representation of data and highlighting the feature expression. So, it is contributive to deal with the problems of large capacity data like high dimension, redundancy, and nonlinear etc. Therefore, DBN is chosen as the fault diagnosis method in this paper. Meanwhile, given that the difficulty in feature extraction of hydraulic vibration signal and the important influence of input feature vector to the diagnosing of DBN, a fast and effectively feature extraction method based on sliding-window spectrum feature (SWSF) is proposed. It is effective in remaining the integrity of feature, avoiding the risking of relative shifting of characteristic spectrum, and decreasing the dimensions of feature vector. The experimental results demonstrate that the combination of SWSF and DBN is a fast and effective approach to realize the fault diagnosis of hydraulic system

Unique normal forms for Hopf-zero vector fields

We consider normal forms of Hopf-zero vector fields in R-3. Unique normal forms under conjugacy and orbital equivalence for the generic case are given.MathematicsSCI(E)4ARTICLE4345-34833

Effect of Alkali Treatment of Wheat Straw on Adsorption of Cu(II) under Acidic Condition

The convenient and feasible pretreatment method of alkali treatment is very common in the degradation process of wheat straw. However, its utilization in the pretreatment of wheat straw as alternative adsorbents for aqueous heavy metals remediation is rarely reported. The present study investigated the removal efficiency of Cu(II) ions using wheat straw with alkali pretreatment. The condition of alkali treatment on wheat straw was optimized with the adsorption capacity of Cu(II) as indicator using single-factor experiments. The influences of wheat straw dosages, pH values, contact time, and temperatures on adsorption performance for both untreated wheat straw (UWS) and alkali-treated wheat straw (AWS) were investigated. Results showed that the relatively large removal rate of Cu(II) could be obtained, and chemical behavior occurred during the adsorption process. Characteristic analysis found that the major function of alkali treatment to wheat straw was to introduce the hydroxy group, which resulted in the increase of -C-O- group. Although the adsorption capacity is not as high as the one of ligands supported adsorbents, the method is easy to operate and has a wide range of application; at the same time, it could realize both purposes of treating heavy metal pollution and solid wastes