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CNN-LSTM based on attention mechanism for brake pad remaining life prediction

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Mechanical Engineering Science 2022年第2期4卷 30-38,I0005页

作者： Shuo WANG zhenliang yu Guangchen XU Sisi CHEN Yingkou Institute of Technology School of Mechanical and Power Engineering YingkouChina

In order to predict the remaining service life of brake pads accurately and efficiently,and to achieve intelligent warning,this paper proposes a CNN-LSTM brake pad remaining life prediction model based on an attention *** model constructs a non-linear relationship between brake pad features such as brake temperature,brake oil pressure and brake speed and brake pad wear data through convolutional neural network(CNN)and long and short term memory network(LSTM),as well as capturing the time dependence that exists in the brake pad wear *** attention mechanism is also introduced to assign different weight values to the features output from multiple historical moments,highlighting the features with high saliency and avoiding the influence of invalid features,so as to improve the prediction effect of the remaining brake pad *** results show that the proposed CNN-LSTM-Attention model can effectively predict the remaining life of brake pads,with the mean absolute error MAE value of 0.0048,root mean square error RMSE value of 0.0059 and coefficient of determination R2 value of 0.9636;and compared with the BP model,CNN model,LSTM model and CNN-LSTM model,the coefficient of determination R2 values are closest to 1,with an improvement of 8.26%,5.25%,3.99%and 1.85%respectively,enabling more effective monitoring and intelligent warning of the remaining brake pad life.

关键词： attention mechanism CNN-LSTM brake pads life prediction

Tool wear condition monitoring method of five-axis machining center based on PSO-CNN

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Mechanical Engineering Science 2022年第2期4卷 11-20,I0006页

作者： Shuo WANG zhenliang yu Changguo LU Jingbo WANG Yingkou Institute of Technology School of Mechanical and Power EngineeringYingkouChina

The effective monitoring of tool wear status in the milling process of a five-axis machining center is important for improving product quality and efficiency,so this paper proposes a CNN convolutional neural network model based on the optimization of PSO algorithm to monitor the tool wear ***,the cutting vibration signals and spindle current signals during the milling process of the five-axis machining center are collected using sensor technology,and the features related to the tool wear status are extracted in the time domain,frequency domain and time-frequency domain to form a feature sample matrix;secondly,the tool wear values corresponding to the above features are measured using an electron microscope and classified into three types:slight wear,normal wear and sharp wear to construct a target Finally,the tool wear sample data set is constructed by using multi-source information fusion technology and input to PSO-CNN model to complete the prediction of tool wear *** results show that the proposed method can effectively predict the tool wear state with an accuracy of 98.27%;and compared with BP model,CNN model and SVM model,the accuracy indexes are improved by 9.48%,3.44%and 1.72%respectively,which indicates that the PSO-CNN model proposed in this paper has obvious advantages in the field of tool wear state identification.

关键词： five-axis machining center tool wear PSO-CNN intelligent monitoring

Multi-objective reliability optimization design of high-speed heavy-duty gears based on APCK-SORA model

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Mechanical Engineering Science 2022年第2期4卷 49-56,I0006页

作者： zhenliang yu Shuo WANG Fengqin ZHAO Chenyuan LI School of Mechanical and Power Engineering Yingkou Institute of TechnologyYingkouChina

For high-speed heavy-duty gears in operation is prone to high tooth surface temperature rise and thus produce tooth surface gluing leading to transmission failure and other adverse effects,but in the gear optimization design and little consideration of thermal transmission errors and thermal resonance and other factors,while the conventional multi-objective optimization design methods are difficult to achieve the optimum of each *** on this,the paper proposes a gear multi-objective reliability optimisation design method based on the APCK-SORA *** PC-Kriging model and the adaptive k-means clustering method are combined to construct an adaptive reliability analysis method(APCK for short),which is then integrated with the SORA optimisation *** objective function is the lightweight of gear pair,the maximum overlap degree and the maximum anti-glue strength;the basic parameters of the gear and the sensitivity parameters affecting the thermal deformation and thermal resonance of the gear are used as design variables;the amount of thermal deformation and thermal resonance,as well as the contact strength of the tooth face and the bending strength of the tooth root are used as constraints;the optimisation results show that:the mass of the gear is reduced by 0.13kg,the degree of overlap is increased by 0.016 and the coefficient of safety against galling Compared with other methods,the proposed method is more efficient than the other methods in meeting the multi-objective reliability design requirements of lightweighting,ensuring smoothness and anti-galling capability of high-speed heavy-duty gears.

关键词： APCK-SORA model high-speed heavy-duty gears multi-objective reliability optimization design k-means clustering method

Fault monitoring and diagnosis of motorized spindle in five-axis Machining Center based on CNN-SVM-PSO

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Mechanical Engineering Science 2022年第2期4卷 21-29,I0005页

作者： Shuo WANG zhenliang yu Xu LIU Zhipeng Lyu School of Mechanical and Power Engineering Yingkou Institute of TechnologyYingkouChina Yingkou Dingsheng Heavy Industry Machinery Co.LTD YingkouChina School of Mechanical and Power Engineering Shenyang University of Chemical TechnologyShenyangChina

A spindle fault diagnosis method based on CNN-SVM optimized by particle swarm algorithm(PSO)is proposed to address the problems of high failure rate of electric spindles of high precision CNC machine tools,while manual fault diagnosis is a tedious task and low *** model uses a convolutional neural network(CNN)model as a deep feature miner and a support vector machine(SVM)as a fault state *** the electric spindle of a five-axis machining centre as the experimental research object,the model classifies and predicts four labelled states:normal state of the electric spindle,loose state of the rotating shaft and coupling,eccentric state of the motor air gap and damaged state of the bearing and rolling body,while introducing a particle swarm algorithm(PSO)is introduced to optimize the hyperparameters in the model to improve the prediction *** results show that the proposed hybrid PSO-CNN-SVM model is able to monitor and diagnose the electric spindle failure of a 5-axis machining centre with an accuracy of 99.33%.In comparison with the BP model,SVM model,CNN model and CNN-SVM model,the accuracy of the model increased by 10%,6%,4%and 2%respectively,which shows that the fault diagnosis model proposed in the paper can monitor the operation status of the electric spindle more effectively and diagnose the type of electric spindle fault,so as to improve the maintenance strategy.

关键词： five-axis machining centres CNN-SVM spindle vibration fault diagnosis

A CNN-LSTM-PSO tool wear prediction method based on multi-channel feature fusion

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Mechanical Engineering Science 2022年第2期4卷 39-48,I0006页

作者： Shuo WANG zhenliang yu Yongqi GUO Xu LIU School of Mechanical and Power Engineering Yingkou Institute of TechnologyYingkouChina Yingkou Dingsheng Heavy Industry Machinery Co.LTD YingkouChina

In order to achieve predictive maintenance of CNC machining tools and to be able to change tools intelligently before tool wear is at a critical threshold,a CNN-LSTM tool wear prediction model based on particle swarm algorithm(PSO)optimization with multi-channel feature fusion is ***,the raw signals of seven channels of the machining process are collected using sensor technology and processed for noise reduction;secondly,the time-domain,frequency-domain and time-frequency-domain features of each channel signal are extracted,and a sample data set of spatio-temporal correlation of traffic flow is constructed by dimensionality reduction processing and information fusion of the above features;finally,the data set is input to the CNN-LSTM-PSO model for training and *** results show that the CNN-LSTM-PSO model can effectively predict tool wear with an average absolute error MAE value of 0.5848,a root mean square error RMSE value of 0.7281,and a coefficient of determination R2 value of 0.9964;and compared with the BP model,CNN model,LSTM model and CNN-LSTM model,its tool wear prediction accuracy improved by 7.56%,2.60%,2.98%,and 1.63%,respectively.

关键词： feature fusion CNN-LSTM tool wear life prediction

A multi-source information fusion method for tool life prediction based on CNN-SVM

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Mechanical Engineering Science 2022年第2期4卷 1-10,I0003,I0004页

作者： Shuo WANG zhenliang yu Peng LIU Man Tong WANG School of Mechanical and Power Engineering Yingkou Institute of TechnologyYingkouChina

For milling tool life prediction and health management,accurate extraction and dimensionality reduction of its tool wear features are the key to reduce prediction *** this paper,we adopt multi-source information fusion technology to extract and fuse the features of cutting vibration signal,cutting force signal and acoustic emission signal in time domain,frequency domain and time-frequency domain,and downscale the sample features by Pearson correlation coefficient to construct a sample data set;then we propose a tool life prediction model based on CNN-SVM optimized by genetic algorithm(GA),which uses CNN convolutional neural network as the feature learner and SVM support vector machine as the trainer for regression *** results show that the improved model in this paper can effectively predict the tool life with better generalization ability,faster network fitting,and 99.85%prediction *** compared with the BP model,CNN model,SVM model and CNN-SVM model,the performance of the coefficient of determination R2 metric improved by 4.88%,2.96%,2.53%and 1.34%,respectively.

关键词： CNN-SVM tool wear life prediction multi-source information fusion

林窗模型的几个基本问题的研究Ⅱ.模拟样地个数的效应

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生态学杂志 2000年第5期19卷 1-6,18页

作者：延晓冬于振良中国科学院大气物理研究所东亚研究中心北京100029 中国科学院地理与资源科学研究所北京100101

Plot number can deeply affect the result of forest gap model simulation, but which has been paid little attention and investigation. This study takes Broad-leaved Korean pine (Pinus koraiensis) forests, in Mt. Changbai of Northeastern China, for instance, and applies forest gap model NEWCOP to investigate the effects of different plot number on the result of forest gap model simulation. Our study shows that plot number can affect forest species composition, which indicates a decrease of forest community variability index with increasing simulation plot number and a trend to 0 for the index after the plot number reaching some value. Based on the result mentioned above, we conclude that the smallest area of stand for presenting the characteristics of broad-leaved korean pine forest community is 38400 ha.

关键词：森林群落林窗模型模拟样地数量斑块复合体

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关于科学研究项目同行评议的一些政策性分析

中国科学基金 2007年第1期21卷 42-46页

作者：冯锋于晟于振良国家自然科学基金委员会生命科学部北京100085 国家自然科学基金委员会地球科学部北京100085

政府支持的科学研究支撑着国家创新体系，对经济、社会的发展至关重要。公众关心科学研究能够用于为人类健康、安全和环境保护政策的制定提供科学依据。基础研究投资所产生的新知识将支撑未来经济的发展。科学研究经费分配有很多方法，... 详细信息

政府支持的科学研究支撑着国家创新体系，对经济、社会的发展至关重要。公众关心科学研究能够用于为人类健康、安全和环境保护政策的制定提供科学依据。基础研究投资所产生的新知识将支撑未来经济的发展。科学研究经费分配有很多方法，包括政府行政分配、通过同行专家评议分配、按照绩效表现分配，或采用以地区为基础的平均分配，每一种方法都有它的优点和缺点。在美国，一般来讲，约90％的联邦研究经费通过竞争性机制进行分配。

关键词：科学研究项目环境保护政策同行评议国家创新体系基础研究政府支持人类健康科学依据

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A Review on Basic Research of Desertification Combating in China

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Science Foundation in China 2003年第2期 35-37页

作者： yu zhenliang Gu Reisheng Chen yue Du Shengming Liu Shirong ( Department of Life Sciences, NSFC, Beijing 100085 Chinese academy of Forestry, Beijing 100094)

China is one of the countries seriously affected by desertification in the world. Nowadays, China has desertification-affected land area of about 2.67 million square kilometers, accounting for 27.9 percent of the entire territory in China, at an average rate of about 10400 square kilometers annually during the period of 1994 to 1999. At the same time, the area of desertification caused by wind erosion is expanding at an average rate of 3436 square kilometers per year. China' s gov-

关键词： in have NSFC on A Review on Basic Research of Desertification Combating in China of Basic