Case studies in process modelling and condition monitoring using artificial neural networks

Increasingly artificial neural networks are finding applications in a process engineering environment. Recently the Department of Trade and Industry in the UK has supported the transfer of neural technology to industry with a £5.7M campaign. As part of the campaign, the University of Newcastle and EDS Advanced Technologies Group have set up a Process Monitoring and Control Club.This paper presents two case studies from the work of the Club. Firstly, the ability of neural networks to provide enhanced modelling performance over traditional linear techniques is demonstrated on real process data. Secondly, the ability of neural networks to capture non-linear system characteristics is exploited in a novel way in a condition monitoring exercise. The process studied in both applications is the melter stage of the BNFL Vitrification Process. The process involves the encapsulation of highly active liquid waste in glass blocks to provide a safe and convenient method of storage.     

Process monitoring using auto-associative,feed-forward artificial neural networks

The potential of using artificially simulated neural networks as intelligent, adaptive process-monitoring devices is discussed. The investigation is considered as a method for automatic, intelligent exception reporting for quality control applications. The technique is also compared with the conventional statistical approaches of principal component analysis and Kohonen's feature map. The applications of the technique in aerospace and manufacturing environments are presented and a possible extension of the method to incorporate a diagnostic function is discussed.Seconded from Cheltenham and Gloucester College of Higher Education as a Royal Society/SERC Research Fellow at Smith's Industries Aerospace and Defence Systems, Bishop's Cleeve, Cheltenham, UK.     

Comparison of optical performance monitoring techniques using artificial neural networks

In this paper, we make an overview of three techniques that have used artificial neural networks (ANNs) to model impairments in optical fiber. A comparison between a linear partial least squares regression algorithm and ANN is also shown. We demonstrate that nonlinear modeling is required for multi-impairment monitoring in optical fiber when using Parametric Asynchronous Eye Diagram (PAED). Results demonstrating the accuracy of PAED are also shown. A comparison between PAED and Synchronous Eye Diagrams is also demonstrated, for NRZ, RZ and QPSK modulated signals. We show that PAED can provide comprehensible diagrams for QPSK modulated signals, under a certain range of chromatic dispersion.     

应用人工神经元网络进行pH中和过程故障诊断

应用NeurOn-Line神经元网络应用系统开发技术和G2实时智能专家系统开发技术,开发了一套pH中和过程的故障诊断系统。先简单描述了该pH中和过程及其建模,然后详细论述了该故障诊断系统在NeurOn-Line和G2软件平台上的设计和编程开发情况。共进行了pH中和过程的正常运行模式,pH传感器测量值偏高、pH传感器测量值偏低和碱液浓度变稀三种故障模式的仿真和诊断。仿真结果表明该故障诊断系统能快速准确诊断出pH中和过程的正常运行和故障模式。     

Simultaneous optimization of artificial neural networks for financial forecasting

Artificial neural networks (ANNs) have been popularly applied for stock market prediction, since they offer superlative learning ability. However, they often result in inconsistent and unpredictable performance in the prediction of noisy financial data due to the problems of determining factors involved in design. Prior studies have suggested genetic algorithm (GA) to mitigate the problems, but most of them are designed to optimize only one or two architectural factors of ANN. With this background, the paper presents a global optimization approach of ANN to predict the stock price index. In this study, GA optimizes multiple architectural factors and feature transformations of ANN to relieve the limitations of the conventional backpropagation algorithm synergistically. Experiments show our proposed model outperforms conventional approaches in the prediction of the stock price index.     

On line detection of mean and variance shift using neural networks and support vector machine in multivariate processes

The effective recognition of unnatural control chart patterns (CCPs) is one of the most important tools to identify process problems. In multivariate process control, the main problem of multivariate quality control charts is that they can detect an out of control event but do not directly determine which variable or group of variables has caused the out of control signal and how much is the magnitude of out of control. Recently machine learning techniques, such as artificial neural networks (ANNs), have been widely used in the research field of CCP recognition. This study presents a modular model for on-line analysis of out of control signals in multivariate processes. This model consists of two modules. In the first module using a support vector machine (SVM)-classifier, mean shift and variance shift can be recognized. Then in the second module, using two special neural networks for mean and variance, it can be recognized magnitude of shift for each variable simultaneously. Through evaluation and comparison, our research results show that the proposed modular performs substantially better than the traditional corresponding control charts. The main contributions of this work are recognizing the type of unnatural pattern and classifying the magnitude of shift for mean and variance in each variable simultaneously.     

An adaptive Bayesian scheme for joint monitoring of process mean and variance

This paper presents a new model for the economic optimization of a process operation where two assignable causes may occur, one affecting the mean and the other the variance. The process may thus operate in statistical control, under the effect of either one of the assignable causes or under the effect of both assignable causes. The model employed uses the Bayes theorem to determine the probabilities of operating under the effect of each assignable cause. Based on these probabilities, and following an economic optimization criterion, decisions are made on the necessary actions (stop the process for investigation or not) as well as on the time of the next sampling instance and the size of the next sample. The superiority of the proposed model is estimated by comparing its economic outcome against the outcome of simpler approaches such as Fp (Fixed-parameter) and adaptive Vp (Variable-parameter) Shewhart charts for a number of cases. The numerical investigation indicates that the economic improvement of the new model may be significant.     

Contingency evaluation and monitorization using artificial neural networks

In this paper, different neural network-based solutions to the contingency analysis problem are presented. Contingency analysis is examined from two perspectives: as a functional approximation problem obtaining a numerical evaluation and ranking contingencies; and as a graphical monitoring problem, obtaining an easy visualization system of the relative severity of the contingencies. For the functional evaluation problem, we analyze the use of different supervised feed-forward artificial neural networks (multilayer perceptron and radial basis function networks). The proposed systems produce a very accurate evaluation and ranking, and so present a high applicability. For the graphical monitoring problem, unsupervised artificial neural networks such as self-organizing maps by Kohonen have been used. This solution allows both a rapid, easy and simultaneous visualization of the severity level of the complete contingency set. The proposed solutions avoid the main drawbacks of previous neural network approaches to this problem, which are explicitly analyzed here.     

Modelling deforestation using GIS and artificial neural networks

This study aims to predict the spatial distribution of tropical deforestation. Landsat images dated 1974, 1986 and 1991 were classified in order to generate digital deforestation maps which locate deforestation and forest persistence areas. The deforestation maps were overlaid with various spatial variables such as the proximity to roads and to settlements, forest fragmentation, elevation, slope and soil type to determine the relationship between deforestation and these explanatory variables. A multi-layer perceptron was trained in order to estimate the propensity to deforestation as a function of the explanatory variables and was used to develop deforestation risk assessment maps. The comparison of risk assessment map and actual deforestation indicates that the model was able to classify correctly 69% of the grid cells, for two categories: forest persistence versus deforestation. Artificial neural networks approach was found to have a great potential to predict land cover changes because it permits to develop complex, non-linear models.     

Predicting grinding burn using artificial neural networks

This paper introduces a method for predicting grinding burn using artificial neural networks (ANN). First, the way to model grinding burn via ANN is presented. Then, as an example, the prediction of grinding burn of ultra-strength steel 300M via ANN is given. Very promising results were obtained.     

Gradientless shape optimization using artificial neural networks

In this paper a new zero order method of structural shape optimization, in which material shrinks or grows perpendicular to the design boundary, has been proposed in order to satisfy fully stressed design criteria. To avoid mesh distortion that results in undesirable shape, design element concept and for nodal movement and convergence checking, fuzzy set theory have been used. To accelerate the convergence, artificial neural networks are employed. The proposed approach, named as GSN technique, has been incorporated in a FORTRAN software GSOANN. Using this software shape optimization of four structures are carried out. It is demonstrated that proposed technique overcomes most of the shortcomings of mundane zero order methods.     

Microstrip antenna design using artificial neural networks

Neural‐network computational modules have recently gained recognition as an unconventional and useful tool for RF and microwave modeling and design. Neural networks can be trained to learn the behavior of passive/active components/circuits. This work describes the fundamental concepts in this emerging area aimed at teaching RF/microwave engineers what neural networks are, why they are useful, when they can be used, and how to use them to model microstrip patch antenna. This work studies in‐depth different designs and analysis methods of microstrip patch antenna using artificial neural‐network and different network structure are also described from the RF/microwave designer's perspective. This article also illustrates two examples of microstrip antenna design and validating the utility of ANN in the area of microstrip antenna design. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

Design of artificial neural networks for tool wear monitoring

An on-line scheme for tool wear monitoring using artificial neural networks (ANNs) has been proposed. Cutting velocity, feed, cutting force and machining time are given as inputs to the ANN, and the flank wear is estimated using the ANN. Different ANN structures are designed and investigated to estimate the tool wear accurately. An existing analytical model is used to obtain the data for various cutting conditions in order to eliminate the huge cost and time associated with generation of training and evaluation data. Motivated by the fact that the tool wear at a given instance of time depends on the tool wear value at a previous instance of time, memory is included in the ANN. ANNs without memory, with one-phase memory, and with two-phase memory are investigated in this study. The effect of various training parameters, such as learning coefficient, momentum, temperature, and number of hidden neurons, on these architectures is studied. The findings and experience obtained should facilitate the design and implementation of reliable and economical real-time systems for tool wear monitoring and identification in intelligent manufacturing.     

Application of artificial neural networks in linear profile monitoring

In many quality control applications the quality of process or product is characterized and summarized by a relation (profile) between a response variable and one or more explanatory variables. Such profiles can be modeled using linear or nonlinear regression models. In this paper we use artificial neural networks to detect and classify the shifts in linear profiles. Three monitoring methods based on artificial neural networks are developed to monitor linear profiles. Their efficacies are assessed using average run length criterion.     

Continuous process control using neural networks

We present some adaptive control strategies based on neural networks that can be used for designing controllers for continuous process control problems. Specifically, a learning algorithm has been formulated based on reinforcement learning, a weakly supervised learning technique, to solve set-point control and control scheduling for continuous processes where the process cannot be modeled easily. It is shown how reinforcement learning can be used to learn the control strategy adaptively based on exploration of the control space without making assumptions about the process model. A new learning scheme, handicapped learning, was developed to learn a control schedule that specifies a schedule of set points. Applications studied include the control of a nonisothermal continuously stirred tank reactor at its unstable state and the learning of the daily time-temperature schedule for an environment controller. Experimental results demonstrate good learning performance, indicating that the learning algorithm can be used for solving transient startup and boundary value control problems.     

Using neural networks to detect the bivariate process variance shifts pattern

Most of the research in statistical process control has been focused on monitoring the process mean. Typically, it is also important to detect variance changes as well. This paper presents a neural network-based approach for detecting bivariate process variance shifts. Some important implementation issues of neural networks are investigated, including analysis window size, number of training examples, sample size, training algorithm, etc. The performance of the neural network, in terms of the ARL and run length distribution, is compared with that of traditional multivariate control charts. Through rigorous evaluation and comparison, our research results show that the proposed neural network performs substantially better than the traditional generalized variance chart and might perform better than the adaptive sizes control charts in the case that the out-of-control covariance matrix is not known in advance.     

最佳拟合与神经网络相结合实现传感器特性线性化

提出了一种传感器特性线性化的方法.该方法把传感器特性分为线性和非线性段,用一种改进的BP神经网络映射传感器特性非线性段的反函数作为校正环节,用最佳拟合方法得到线性段的直线方程,从而实现传感器特性的线性化.经过仿真试用表明,这种方法可使传感器的非线性误差减小近十倍.最后,给出了一些仿真实验和仿真结果.     

A hybrid recommender system using artificial neural networks

In the context of recommendation systems, metadata information from reviews written for businesses has rarely been considered in traditional systems developed using content-based and collaborative filtering approaches. Collaborative filtering and content-based filtering are popular memory-based methods for recommending new products to the users but suffer from some limitations and fail to provide effective recommendations in many situations. In this paper, we present a deep learning neural network framework that utilizes reviews in addition to content-based features to generate model based predictions for the business-user combinations. We show that a set of content and collaborative features allows for the development of a neural network model with the goal of minimizing logloss and rating misclassification error using stochastic gradient descent optimization algorithm. We empirically show that the hybrid approach is a very promising solution when compared to standalone memory-based collaborative filtering method.     

Robust radar target classifier using artificial neural networks

In this paper an artificial neural network (ANN) based radar target classifier is presented, and its performance is compared with that of a conventional minimum distance classifier. Radar returns from realistic aircraft are synthesized using a thin wire time domain electromagnetic code. The time varying backscattered electric field from each target is processed using both a conventional scheme and an ANN-based scheme for classification purposes. It is found that a multilayer feedforward ANN, trained using a backpropagation learning algorithm, provides a higher percentage of successful classification than the conventional scheme. The performance of the ANN is found to be particularly attractive in an environment of low signal-to-noise ratio. The performance of both methods are also compared when a preemphasis filter is used to enhance the contributions from the high frequency poles in the target response.