Rolling Bearing Fault Diagnosis Based On Convolutional Capsule Network

Fault diagnosis technology has been widely applied and is an important part of ensuring the safe operation of mechanical equipment.In response to the problem of frequent faults in rolling bearings,this paper designs a rolling bearing fault diagnosis method based on convolutional capsule network(CCN).More specifically,the original vibration signal is converted into a two-dimensional time–frequency image using continuous wavelet transform(CWT),and the feature extraction is performed on the two-dimensional time–frequency image using the convolution layer at the front end of the network,and the extracted features are input into the capsule network.The capsule network converts the extracted features into vector neurons,and the dynamic routing algorithm is used to achieve feature transfer and output the results of fault diagnosis.Two different datasets are used to compare with other traditional deep learning models to verify the fault diagnosis capability of the method.The results show that the CCN has good diagnostic capability under different working conditions,even in the presence of noise and insufficient samples,compared to other models.This method contributes to the safe and reliable operation of mechanical equipment and is suitable for other rotating scenarios.

Optimization of Fermentation Conditions of Lepista sordida Mycelium

[ Objective] This study aimed to optimize the fermentation condition of Lepista sordida mycelium. [ Method ] The effects of carbon sources, nitrogen sources ,pH and incubation time on the dry weight of mycelium in L. sordida fermentation were investigated. [ Result] The descending order of the influences on L. sordida mycelium growth was ： carbon sources 〉 incubation time 〉 pH 〉 nitrogen sources. Based on orthogonal experiment, the optimal fermentation condition of L. sordida mycelium was determined as ： fermentation medium of 3% corn powder and 0.2% yeast extract, pH of 6.0, and incubation time of 8 d. [ Conclusion ] This study provides theoretical reference and experimental basis for the development and utilization of L. sordida biological resources.

Mechanisms of autophagy function and regulation in plant growth,development,and response to abiotic stress

Autophagy is an evolutionarily conserved degradation pathway of lysosomes(in mammals)and vacuoles(in yeasts and plants)from lower yeasts to higher mammals.It wraps unwanted organelles and damaged proteins in a double-membrane structure to transport them to vacuoles for degradation and recycling.In plants,autophagy functions in adaptation to the environment and maintenance of growth and development.This review systematically describes the autophagy process,biological functions,and regulatory mechanisms occurring during plant growth and development and in response to abiotic stresses.It provides a basis for further theoretical research and guidance of agricultural production.

Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging

Rotating machinery is widely applied in industrial applications.Fault diagnosis of rotating machinery is vital in manufacturing system,which can prevent catastrophic failure and reduce financial losses.Recently,Deep Learning(DL)-based fault diagnosis method becomes a hot topic.Convolutional Neural Network(CNN)is an effective DL method to extract the features of raw data automatically.This paper develops a fault diagnosis method using CNN for InfRared Thermal(IRT)image.First,IRT technique is utilized to capture the IRT images of rotating machinery.Second,the CNN is applied to extract fault features from the IRT images.In the end,the obtained features are fed into the Softmax Regression(SR)classifier for fault pattern identification.The effectiveness of the proposed method is validated using two different experimental data.Results show that the proposed method has a superior performance in identification various faults on rotor and bearings comparing with other deep learning models and traditional vibration-based method.

Synergistic optimization of thermoelectric performance in p-type Ag2Te through Cu substitution

Superionic conductors,which exhibit liquid-like phonon transport but crystal-like carrier transport,have attracted great attention and broad research interest in the thermoelectric community.Ag_(2)Te is a superionic conductor;however,its small band gap and large Ag vacancy formation energy impede its application as a prominent p-type thermoelectric material.In this work,synergistic optimization of the thermoelectric performance of Ag_(2)Te through Cu substitution is realized through a combination of experimental and theoretical efforts.For the electrical transport,Cu substitution systematically increases the band gap of Ag_(2)Te and reduces the cation vacancy formation energy.These two beneficial effects simultaneously increase the electrical conductivity and suppress the bipolar effect,thereby greatly enhancing the p-type electrical transport properties of Ag_(2)Te.For the thermal transport,alloying Cu_(2)Te with Ag_(2)Te significantly reduces the thermal conductivity through not only point defect scattering but also softening of the interatomic interactions.The latter is attributed to the relatively small Cu atoms vibrating in the oversized 8c sites.This two-fold optimization results in maximum thermoelectric figure of merit zT values of over 1.3 at 773 K for both Ag_(1.2)Cu_(0.8)Te and AgCuTe,demonstrating the great potential of Ag_(2-x)Cu_(x)Te as a promising p-type thermoelectric material system.