Multi-machine earliness and tardiness scheduling problem: an interconnected neural network approach

This paper addresses the problem of scheduling a set of independent jobs with sequence-dependent setups and distinct due dates on non-uniform multi-machines to minimize the total weighted earliness and tardiness, and explores the use of artificial neural networks as a valid alternative to the traditional scheduling approaches. The objective is to propose a dynamical gradient neural network, which employs a penalty function approach with time varying coefficients for the solution of the problem which is known to be NP-hard. After the appropriate energy function was constructed, the dynamics are defined by steepest gradient descent on the energy function. The proposed neural network system is composed of two maximum neural networks, three piecewise linear and one log-sigmoid network all of which interact with each other. The motivation for using maximum networks is to reduce the network complexity and to obtain a simplified energy function. To overcome the tradeoff problem encountered in using the penalty function approach, a time varying penalty coefficient methodology is proposed to be used during simulation experiments. Simulation results of the proposed approach on a scheduling problem indicate that the proposed coupled network yields an optimal solution which makes it attractive for applications of larger sized problems.     

Machine fault classification using an ART 2 neural network

Machine monitoring and diagnostics has been considered to be an integral part of the manufacturing process in recent years. It has played an important role in increasing productivity and reducing costs. This paper presents a methodology which is built upon parametric modelling and neural network technology for automatic detection and identification of machine faults. An adaptive resonance theory (ART) neural network architecture is used to identify machine faults from the parameters of a parametric model of the vibration signal. The experimental results indicate that the ART 2 neural network is capable of classifying a fault correctly and rapidly by using the parameters of the parametric model of process signals.     

人工神经网络在机器人学中的应用探讨

简要介绍常用于机器人学中的基本网络模型,并着重探讨了人工神经网络在机器人运动学和运动控制中的应用。     

滚动轴承故障诊断的神经网络方法

总结分析了轴承的故障形式及原因,给出了振动频率,阐述了Bp网络的结构及算法,并对实例建立BP神经网络。     

A neural network model based predictive control approach: application to a semi-batch reactor

Neural networks can be considered to be new modelling tools in process control and especially in non-linear dynamical systems cases. Their ability to approximate non-linear functions has been very often demonstrated and tested by simulation and experimental studies. In this paper, a predictive control strategy of a semi-batch reactor based on neural network models is proposed. Results of a non-linear control of the reactant temperature of a semi-batch reactor are presented. The process identification is composed of an off-line phase that consists in training the network, and of an on-line phase that corresponds to the neural model adaptation so that it fits any modification of the process dynamics. Experimental results when using this method to control a semi-batch reactor are reported and show the great potential of this strategy in controlling non-linear processes.     

Control chart pattern recognition using an optimized neural network and efficient features

Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This study investigates the design of an accurate system for control chart pattern (CCP) recognition from two aspects. First, an efficient system is introduced that includes two main modules: the feature extraction module and the classifier module. The feature extraction module uses the entropies of the wavelet packets. These are applied for the first time in this area. In the classifier module several neural networks, such as the multilayer perceptron and radial basis function, are investigated. Using an experimental study, we choose the best classifier in order to recognize the CCPs. Second, we propose a hybrid heuristic recognition system based on particle swarm optimization to improve the generalization performance of the classifier. The results obtained clearly confirm that further improvements in terms of recognition accuracy can be achieved by the proposed recognition system.     

神经网络自适应控制的研究进展及展望

关于人工神经网络与自适应结合的研究，近年来已成为智能控制学科的热点之一。自适应具有强鲁棒性，神经网络则具有自学习功能和良好的容错能力，神经网络自适应控制由于较好地结合了二者的优点而具有强大的优势。本文系统地综述了神经网络自适应控制的进展，讨论了神经网络自适应的主要模型和算法，并就其存在的一些问题、应用与发展趋势进行了探讨。     

Application of artificial neural networks instead of the orifice plate discharge coefficient

Differential pressure flowmeters are very often used in many industries. Therefore, the improvement of this method of flow measurement is an important task of flow measurement and instrumentation. One of the important characteristics of differential pressure flowmeters is the discharge coefficient of the flow transducers. A large number of studies and publications were devoted to modeling this coefficient. Therefore, in the framework of this research, this coefficient is simulated using artificial neural networks. The neural representation of this characteristic is made in the form of a multilayer perceptron. In this paper, we replace the traditional equation for the discharge coefficient with an artificial neural network. The advantages and disadvantages of such application of neural networks as discharge coefficients are discussed. The analysis of the results of gas flow measurement, where the neural network is used instead of the traditional equation, is presented. The estimation of flow rate measurement errors with such an approach is made; the error of calculation of the discharge coefficient is estimated.     

A neural network approach to the classification of 3D prismatic parts

This paper presents a neural network approach to the classification of 3D prismatic parts based on their global shape information modelling. In this approach, a 3D part is modelled by the contours of its three projected views, which are approximately represented by three rectilinear polygons. The global shape information of each polygon is modelled by its simplified skeleton, which originally is of a tree structure and can be represented by several vectors by a conversion method. These vectors are the input to a polygon classifier which is constructed on the basis of the back-propagation neural network model. The classification results of polygons can be used to group the 3D prismatic parts into families in a hierarchical manner, by setting different levels of similarity criteria. The proposed method for classifying 3D workpieces can be used to enhance the productivity of design and manufacturing processes. By retrieving and reviewing similar parts from the part families, the designers or process planners could be greatly assisted in performing a new task. That is, they can avoid the reinvention of an existing design and can create a new design by modifying existing ones.     

Condition monitoring of planetary gearbox by hardware implementation of artificial neural networks

The hazards of planetary gearboxes’ failures are the most crucial in the machinery which directly influence human safety like aircrafts. But also in an industry their damages can cause the large economic losses. Planetary gearboxes are used in wind turbines which operate in non-stationary conditions and are exposed to extreme events. Also bucket-wheel excavators are equipped with high-power gearboxes that are exposed to shocks. Continuous monitoring of their condition is crucial in view of early failures, and to ensure safety of exploitation. Artificial neural networks allow for a quick and effective association of the symptoms with the condition of the machine. Extensive research shows that neural networks can be successfully used to recognize gearboxes’ failures; they allow for detection of new failures which were not known at the time of training and can be applied for identification of failures in variable-speed applications. In a majority of the studies conducted so far neural networks were implemented in the software, but for dedicated engineering applications the hardware implementation is being used increasingly, due to high efficiency, flexibility and resistant to harsh environmental conditions. In this paper, a hardware implementation of an artificial neural network designed for condition monitoring of a planetary gearbox is presented. The implementation was done on a Field Programmable Gate Array (FPGA). It is characterized by much higher efficiency and stability than the software one. To assess condition of a gearbox working in non-stationary conditions and for chosen failure modes, a signal pre-processing algorithm based on filtration and estimation of statistics from the vibration signal was used. Additionally, the rewards-punishments training process was improved for a selected neural network, which is based on a Learning Vector Quantization (LVQ) algorithm. Presented classifier can be used as an independent diagnostic system or can be combined with traditional data acquisition systems using FPGAs.     

神经网络理论在数控机床热误差建模中的应用

使用神经网络理论对建立数控机床热误差数学模型进行研究分析,并将其与传统的最小二乘线性建模所得热误差数学模型进行综合对比。通过应用实例分析比较表明:神经网络模型与传统的最小二乘线性模型相比具有更好的拟合性和预测能力,并对温度传感器布点的鲁棒性优于传统的最小二乘法建模。     

A neural network model for the assessment of partners’ performance in virtual enterprises

In response to increasing international competition, enterprises have been investigating new ways of cooperating with each other to cope with today’s unpredictable market behaviour. Advanced developments in information & communication technology (ICT) enabled reliable and fast cooperation to support real-time alliances. In this context, the virtual enterprise (VE) represents an appropriate cooperation alternative and competitive advantage for the enterprises. VE is a temporary network of independent companies or enterprises that can quickly bring together a set of core competencies to take advantage of market opportunity. In this emerging business model of VE, the key to enhancing the quality of decision making in the partner companies’ performance evaluation function is to take advantage of the powerful computer-related concepts, tools and technique that have become available in the last few years. This paper attempts to introduce a neural network model, which is able to contribute to the extrapolation of the probable outcomes based on available pattern of events in a virtual enterprise. Quality, delivery and progress were selected as determinant factors effecting the performance assessment. Considering the features of partner performance assessment and neural network models, a back-propagation neural network that includes a two hidden layers was used to evaluate the partner performance.     

Design of new sound metric and its application for quantification of an axle gear whine sound by utilizing artificial neural network

The gear whine sound of an axle system is one of the most important sound qualities in a sport utility vehicle (SUV). Previous work has shown that, because of masking effects, it is difficult to evaluate the gear whine sound objectively by using only the A-weighted sound pressure level. In this paper, a new objective evaluation method for this sound was developed by using new sound metrics, which are developed based on the increment of signal to noise ration and the psychoacoustic parameters in the paper, and the artificial neural network (ANN) used for the modeling of the correlation between objective and subjective evaluation. This model developed by using ANN was applied to the objective evaluation of the axle-gear whine sound for real SUVs and the output of the model was compared with subjective evaluation. The results indicate a good correlation of over 90 percent between the subjective and objective evaluations. This paper was recommended for publication in revised form by Associate Editor Yeon June Kang Professor Sang-Kwon Lee received a Ph.D. degree in ISVR (Institute of Sound and Vibration Research) from Southampton University in 1998. He joined Hyundai Motor Research Center in Korea, working with the Automotive Noise and Vibration Control Group from 1985 to 1994. He has been the Professor at the Department of Mechanical Engineering, Inha University, Inchon, Korea, since March 1999. His research interests are the digital signal processing, NVH (noise vibration harahness), condition monitoring, product sound quality design and active control.     

Adaptive clustering algorithm for recycling cell formation: An Application of fuzzy ART neural networks

The recycling cell formation problem means that disposal products are classified into recycling part families using group technology in their end-of-life phase. Disposal products have the uncertainties of product status by usage influences during product use phase, and recycling cells are formed design, process and usage attributes. In order to deal with the uncertainties, fuzzy set theory and fuzzy logic-based neural network model are applied to recycling cell formation problem for disposal products. Fuzzy C-mean algorithm and a heuristic approach based on fuzzyART neural network is suggested. Especially, the modified FuzzyART neural network is shown that it has a good clustering results and gives an extension for systematically generating alternative solutions in the recycling cell formation problem. Disposal refrigerators are shown as examples.     

把神经网络应用于丝杠磨削过程的建模与控制

提出了利用两个人工神经网络对丝杠的磨削过程进行建模与预测控制的思想.其中,网络1用于复映传动链、热变形和力变形等误差源与工件螺距误差的关系,即建模;网络2根据网络1的输出和工件螺距误差的仿真值而预报输出下一采样周期的综合补偿控制量.通过一系列试验研究,证明此控制策略能减少工件螺距误差80%以上,有效提高了试件丝杠的磨削精度.     

人工神经网络在降水预报中的应用

在用经验统计方法和降水判别函数进行24 h和12 h晴雨预报的基础上,再用B-P人工神经网络建立降水量级预报模型。经试用,预报准确率较高,有一定的应用价值,12 h预报准确率高于24 h预报准确率。     

人工神经网络在机械设计中的应用

本文通过对机械设计专家系统和人工神经网络的讨论，研究了人工神经网络和专家系统技术在机械设计智能系统中的综合应用问题，并提出了人工神经网络在机械设计中的总体应用方案，为进一步研究打下了基础。     

Feature signature prediction of a boring process using neural network modeling with confidence bounds

Prediction of machine tool failure has been very important in modern metal cutting operations in order to meet the growing demand for product quality and cost reduction. This paper presents the study of building a neural network model for predicting the behavior of a boring process during its full life cycle. This prediction is achieved by the fusion of the predictions of three principal components extracted as features from the joint time–frequency distributions of energy of the spindle loads observed during the boring process. Furthermore, prediction uncertainty is assessed using nonlinear regression in order to quantify the errors associated with the prediction. The results show that the implemented Elman recurrent neural network is a viable method for the prediction of the feature behavior of the boring process, and that the constructed confidence bounds provide information crucial for subsequent maintenance decision making based on the predicted cutting tool degradation.NSF Industry/University Cooperative Research Center (NSF I/UCRC) forIntelligent Maintenance Systems(IMS).     

人工神经网络在智能机械设计中的应用

介绍了智能ＣＡＤ的概念和发展,分析了人工神经网络（ＡＮＮ）的特点,针对目前机械设计专家系统存在的问题,提出将ＡＮＮ应用到专家系统的设计中,是进行智能ＣＡＤ的一条有效途径。介绍了ＡＮＮ在概念设计、设计过程中形象思维的模拟、知识的获取和表示、回溯问题的模拟等方面的应用。     

PID神经网络控制器的设计及仿真研究

PID神经网络(PID-NN)是一种新型的前向神经元网络,该隐含层单元分别为比例(P)、积分(I)、微分(D)单元,各层神经元个数、连接方式、连接权初值是按PID控制规律的基本原则确定的。PID神经网络控制器是将神经网络和PID控制规律融为一体,既具有常规PID控制器结构简单、参数物理意义明确之优点,同时又具有神经网络自学习、自适应的功能。本文给出了PID-NN控制器的结构形式,计算公式,从理论上证明了PID-NN的收敛性和稳定性,最后对二阶对象下的系统进行了仿真,证明了PID-NN控制器具有较好的自学习和自适应性。