检索结果-内蒙古大学图书馆

Research on modelling and simulation of a new variable displacement oil pump

JOURNAL OF engineering-JOE 2019年第13期2019卷 350-354页

作者： Zhang, Wei Wang, Bin Zhang, Yanhuan Peng, Lele Liu, Mohan Lv, Yancheng Yanshan Univ Hebei Prov Key Lab Heavy Machinery Fluid Power Tr Qinhuangdao Peoples R China

A new variable displacement oil pump has been modelled and simulated by using AMESim. Through the comparison and analysis of the simulation curve and the test data of this pump, it is found that the volume efficiency of it is unsatisfactory when the rotational speed is too high. The mechanical structure model of the pump was established by using ADAMS to confirm that the cause of the problem is redundant force on the variable device. On this basis, the redundant force acting on the variable device is theoretically deduced to prepare for the improvement of the pump.

关键词： ADAMS variable displacement oil pump lubricating oils mechanical engineering computing hydraulic systems mechanical structure model redundant force test data AMESim variable device simulation curve volume efficiency digital simulation pumps

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Method of detecting pipeline leakage location based on multiresolution analysis

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JOURNAL OF engineering-JOE 2019年第15期2019卷 552-554页

作者： Zhang, QingChun Liu, Bei Qiu, Yu Zhang, Xi Huaiyin Inst Technol Huaian Jiangsu Peoples R China

Aiming at the problem of low precision of traditional pipeline leakage detection method, a high-precision pipeline leakage detection method based on acoustic signal is proposed, which combines multiresolution analysis of wavelet transform and cross-correlation algorithm of signal processing. As of the large amount of background noise in the pipeline, it is necessary to filter the high-frequency noise by wavelet transform. In order to improve the accuracy of localisation, the noise is checked at different scales and the noise is filtered at lower scales to retain more details of the acoustic signal leakage. Combined with cross-correlation function, the location of pipeline leakage location is repeatedly positioned to improve the accuracy of positioning.

关键词： wavelet transforms correlation methods leak detection pipelines acoustic signal processing condition monitoring mechanical engineering computing high-precision pipeline leakage detection method cross-correlation algorithm acoustic signal processing wavelet transform noise filter

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Solder joint image adaptive block compressive sensing with convex optimisation and Gini index

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JOURNAL OF engineering-JOE 2019年第23期2019卷 8923-8926页

作者： Zhao, Huihuang Wang, Yaonan Zheng, Jinhua Qiao, Zhijun Zhang, Yun Hengyang Normal Univ Dept Comp Sci & Technol Coll Comp Sci & Technol Hengyang Hunan Peoples R China Hunan Prov Key Lab Intelligent Informat Proc & Ap Hengyang Peoples R China Hunan Univ Dept Elect & Engn Coll Elect & Informat Engn Changsha Peoples R China Univ Texas Sch Math & Stat Sci Rio Grande Valley TX USA Commun Univ Zhejiang Inst Radio & TV Technol Hangzhou 310018 Peoples R China

This study aims to improve the performance in solder joint image compression and reconstruction. A novel adaptive block compressive sensing with convex optimisation and Gini index (Ad_BCSGB_Gini) methodology for solder joint image compression and reconstruction is proposed. At first, the image is split into square blocks and each block is resized into a row which consists of a new image. Then, the new image is transformed into a sparse signal by an orthogonal basis matrix, and the image reconstruction is handled as a convex optimisation problem. Moreover, a gradient-based method which has fast computational speed is used to reconstruct image. There is a control factor which controls a norm l(1) in the optimisation problem. To achieve the best performance, at last, the proposed method adaptively selects the best result by comparing Gini index of the reconstruction results based on different control factor values. Experimental results with different methods indicate that the Ad_BCSGB_Gini method is able to achieve the best performance in quantisation comparison than several classical algorithms, and Ad_BCSGB_Gini has a good robustness.

关键词： matrix algebra iterative methods data compression optimisation gradient methods surface mount technology image reconstruction compressed sensing solders image coding mechanical engineering computing Ad_BCSGB_Gini method solder joint image reconstruction performance comparing Gini index convex optimisation problem square blocks novel adaptive block compressive sensing solder joint image compression surface mount technology components solder joint image adaptive block compressive sensing

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Position-pose measurement of crane sway based on monocular vision

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JOURNAL OF engineering-JOE 2019年第22期2019卷 8330-8334页

作者： Fu Xing-yu Niu Dan Li Qi Liu Jin-bo Southeast Univ Sch Automat Minist Educ Key Lab Measurement & Control CSE Nanjing 210096 Jiangsu Peoples R China Nanjing Sciyon Automat Grp Co Ltd Wenwang Dept Nanjing 211102 Jiangsu Peoples R China

The method of spatial positioning and pose measurement using computer vision have been widely used in satellite docking, motion control systems, navigation and guidance areas. A monocular vision measurement method of the telescope's spatial pose based on infrared target was proposed for the crane sway during industrial driving. An experimental system for monocular vision measurement of the crane spreader was established. Through the corresponding relationship between the geometric position of the infrared light spots and the imaging projection, the non-linear optimisation iterative method was used to solve the spatial pose parameters that were converted into four spreader model parameters. The system was measured under static conditions and dynamic pendulum for accuracy and real-time analysis. The experimental results show that the system can meet the real time and accuracy requirements of the spatial pose measurement of the crane sway.

关键词： cranes position measurement computer vision iterative methods computerised instrumentation infrared imaging nonlinear programming pendulums mechanical engineering computing monocular vision measurement method infrared target crane sway industrial driving crane spreader geometric position infrared light spots nonlinear optimisation iterative method spatial pose parameters spreader model parameters position-pose measurement spatial positioning computer vision

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Method of extracting gear fault feature based on stacked autoencoder

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JOURNAL OF engineering-JOE 2019年第23期2019卷 8765-8769页

作者： Liu, Shuo Liu, Yulong Gu, Yuhai Xu, Xiaoli Beijing Informat Sci & Technol Univ Key Lab Electromech Measurement & Control Beijing Peoples R China

Gear and its transmission are widely used in different transmission systems, and its complicated and changeable condition brings a series of problems to the fault feature extraction and diagnosis. In recent years, deep learning techniques have been gradually applied to feature extraction and pattern recognition, and the features of feature extraction and fault diagnosis in complex working environments have shown certain advantages. This study is based on stacked autoencoder under deep learning model, and improve training network performance by modified activation function. Through the network training before and after the experiment done, and to extract the fault feature data comparison in testing, improving network after activation function to extract fault features showed a greater advantage, can be a very good application in practical fault feature extraction.

关键词： mechanical engineering computing fault diagnosis gears feature extraction neural nets learning (artificial intelligence) practical fault feature extraction fault features network training modified activation function training network performance deep learning model complex working environments pattern recognition deep learning techniques changeable condition complicated condition different transmission systems stacked autoencoder gear fault feature

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Design and optimisation of cushioning structure of hydraulic free-piston engine

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JOURNAL OF engineering-JOE 2019年第13期2019卷 54-59页

作者： Chen, Qihuai Ren, Haoling Lin, Tianliang Miao, Cheng Fu, Shengjie Huaqiao Univ Coll Mech Engn & Automat Xiamen Peoples R China

The velocity of the free-piston assembly (FPA) of the hydraulic free-piston engine (HFPE) is very high, which is up to 10 m/s. In order to avoid the collision of the FPA to the cylinder head when the FPA moves towards the dead centres, the cushioning device must be involved. A cushioning structure which combines the sidestep-shape cushion and flute-shape cushion is proposed according to the working principle of the HFPE. The mathematical model of the cushioning is set up, and key parameters are obtained. AMESim is employed to analyse the cushioning process and the influence of the key parameters on the cushioning process. The optimised parameters are obtained according to the simulation. The test rig is established to verify the effectiveness of the cushioning structure according to the optimised parameters. The experiment results show that the proposed cushioning structure is reliable. The maximum pressure during the cushioning process is 19 MPa, and cushioning time is 12 ms. The cushioning structure has little influence on the starting process of HFPE.

关键词： hydraulic systems pistons mechanical engineering computing time 12.0 ms pressure 19.0 MPa velocity 10.0 m/s AMESim hydraulic free-piston engine sidestep-shape cushion cushioning device FPA free-piston assembly cushioning structure optimised parameters cushioning process HFPE flute-shape cushion

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Fault diagnosis of rolling bearing based on kurtosis criterion VMD and modulo square threshold

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JOURNAL OF engineering-JOE 2019年第23期2019卷 8685-8690页

作者： Zhang, Xueying Luan, Zhongquan Liu, Xiuli Beijing Informat Sci & Technol Univ Minist Educ Key Lab Modern Measurement & Control Technol Beijing Peoples R China

Aiming at the problem that fault signals of rolling bearing are easily submerged by the strong background noise, which makes it difficult to extract fault information, so a method based on kurtosis criterion variational mode decomposition (VMD) and modulo square threshold is proposed and applied to fault diagnosis of rolling bearing. First, the vibration signals of rolling bearing are processed by VMD. Second, the signals are reconstructed by selecting the intrinsic mode function (IMF) components with the largest kurtosis and the second largest kurtosis. Finally, the reconstruction signals are de-noised by the modulo square threshold. Through the test of the inner ring and outer ring of the rolling bearing, the feasibility and effectiveness of the proposed method are verified.

关键词： signal processing vibrations fault diagnosis mechanical engineering computing rolling bearings fault diagnosis rolling bearing kurtosis criterion VMD modulo square threshold fault signals fault information kurtosis criterion variational mode decomposition largest kurtosis

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Prediction of wind turbine generator bearing failure through analysis of high-frequency vibration data and the application of support vector machine algorithms

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JOURNAL OF engineering-JOE 2019年第18期2019卷 4965-4969页

作者： Turnbull, Alan Cerroll, James Koukoura, Sofia McDonald, Alasdair Univ Strathclyde Elect & Elect Engn Glasgow Lanark Scotland

Innovations which help facilitate predictive maintenance strategies have the potential to greatly reduce costs associated with wind turbine O&M by driving efficiency and increasing wind turbine availability. This study uses multiple examples of the same generator bearing failure to provide insight into how condition monitoring systems can be used in to train machine learning algorithms with the ultimate goal of predicting failure and remaining useful life. Results show that by analysing high-frequency vibration data and extracting key features to train support vector machine algorithms, an accuracy of 67% can be achieved in successfully predicting failure 1-2 months before occurrence. This study reflects on the limitations surrounding a generalised training approach, taking advantage of all available data, showing that if too many different examples are considered of different wind turbines and operating conditions, the overall accuracy can be diminished.

关键词： vibrations remaining life assessment power generation faults wind turbines mechanical engineering computing turbogenerators condition monitoring support vector machines learning (artificial intelligence) fault diagnosis machine bearings wind turbine generator high-frequency vibration data support vector machine algorithms wind turbine downtime asset management onshore developments offshore developments multiple examples generator bearing failure condition monitoring systems remaining useful life generalised training approach different wind turbines time 1 0 month to 2 0 month

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Review on online inductive wear debris monitoring technology

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JOURNAL OF engineering-JOE 2019年第23期2019卷 8518-8521页

作者： Chen, Tao Wang, Liyong Gu, Yuhai Zuo, Yunbo Beijing Informat & Sci Technol Univ Lab Modern Measurement & Control Technol Minist Educ Beijing Peoples R China

Online oil monitoring technique is now an important development means to monitor wear condition and diagnose wear fault real time. Inductive wear debris monitoring is the research hotspot in the current online oil monitoring for its unique characteristics. Based on the current domestic and international online inductive wear debris monitoring, the relevant comments to the online inductive wear debris monitoring technology were given. Then, the positive and inverse problems of numerical analysis of inductive wear debris monitoring are analysed. Based on the above, the difficulties and shortages of online inductive wear debris monitoring are studied and the development trend and research focus of online inductive wear debris monitoring are discussion to provide for the oil online technology research.

关键词： mechanical engineering computing wear fault diagnosis condition monitoring oils lubricating oils online inductive wear debris monitoring technology online oil monitoring technique wear condition diagnose wear fault real time current online oil monitoring current domestic wear debris monitoring international online inductive wear debris monitoring

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Hydraulic system fault diagnosis method based on a multi-feature fusion support vector machine

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JOURNAL OF engineering-JOE 2019年第13期2019卷 215-218页

作者： Wang, Lihua Wu, Xiao-qiang Zhang, Chunyou Shi, Hongyan Inner Mongolia Univ Nationalities Coll Mech Engn Tongliao 028043 Inner Mongolla Peoples R China

Real-time fault prevention and diagnosis in hydraulic systems is one of the challenges in engineering applications. In order to solve the problems of feature extraction and pattern recognition of different types of fault signals such as leakage, blockage, and cavitation in the hydraulic system, this paper presents a fault diagnosis method based on a multi-feature fusion support vector machine. This method first extracts the typical features of the fault signal from the time domain, frequency domain, and time-frequency domain. Then use the information entropy to calculate the weight of each feature, select the feature with the larger weight to participate in the stock index diagnosis. Finally, a multi-classifier based on the support vector machine is used to realise the fault diagnosis of the hydraulic system. The experimental results show that this method can achieve high accuracy in the hydraulic system fault diagnosis when the Gaussian radial basis function is selected. Also, compared with the commonly used improved back-projection neural network fault diagnosis method, this method has more excellent diagnostic performance.

关键词： support vector machines fault diagnosis radial basis function networks hydraulic systems entropy feature extraction pattern recognition signal processing mechanical engineering computing hydraulic system fault diagnosis method multifeature fusion support vector machine feature extraction pattern recognition fault signal stock index diagnosis back-projection neural network fault diagnosis method time-frequency domain

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