A fuzzy-soft learning vector quantization for control chart pattern recognition

This paper presents a supervised competitive learning network approach, called a fuzzy-soft learning vector quantization, for control chart pattern recognition. Unnatural patterns in control charts mean that there are some unnatural causes for variations in statistical process control (SPC). Hence, control chart pattern recognition becomes more important in SPC. In order to detect effectively the patterns for the six main types of control charts, Pham and Oztemel described a class of pattern recognizers for control charts based on the learning vector quantization (LVQ) such as LVQ, LVQ2 and LVQ-X etc. In this paper, we propose a new supervised LVQ for control charts based on a fuzzy-soft competitive learning network. The proposed fuzzy-soft LVQ (FS-LVQ) uses a fuzzy relaxation technique and simultaneously updates all neurons. It can increase correct recognition accuracy and also decrease the learning time. Comparisons between LVQ, LVQ-X and FS-LVQ are made. Numerical results show that the proposed FS-LVQ has better accuracy and less learning epochs for all neurons being completely learned than LVQ and LVQ-X. Overall, FS-LVQ is highly recommended to be used as a control chart pattern recognizer.     

An integrated approach for process monitoring using wavelet analysis and competitive neural network

A novel framework involving both a detection module and a classification module is proposed for the recognition of the six main types of process signals. In particular, a multi-scale wavelet filter is used for denoising and its performance is compared with that of single-scale linear filters. Moreover, two kinds of competitive neural networks, based on learning vector quantization (LVQ) and adaptive resonance theory (ART), are adopted for the task of pattern classification and benchmarking. Our results show that denoising through a wavelet filter is best for pattern classification, and the classification accuracy with respect to six predefined categories using a LVQ-X network is a little better than using an ART network. However, when an unexpected novel pattern occurs within the process, LVQ will force the novel pattern to be classified into one of those predefined categories that is most similar to the novel pattern. On the contrary, ART will automatically construct a new class when the similarity measured between the novel pattern and the most similar category is too small to be incorporated. Therefore, under the consideration of the stability–plasticity dilemma, our simplified ART network based on multi-scale wavelet denoising provides a more promising way to adapt unexpected novel patterns.     

Recognition and classification of single and concurrent unnatural patterns in control charts via neural networks and fitted line of samples

The correct, prompt recognition and analysis of unnatural and significant patterns in Schewhart’s control charts are very important since they remind out-of-control conditions. In fact, pattern extraction increases the sensitivity of charts when identifying out of control conditions. Artificial neural networks have been used to identify unnatural patterns in many research studies due to their high efficiency in pattern recognition. In most of such studies, there is a significant risk of misclassification of highly sensitive patterns. To put it more clearly, the proposed models offered for the recognition of patterns with low parametric coefficients are mistaken. This study, offers a model for the recognition and analysis of basic patterns in process control charts using LVQ and MLP networks along with a fitted line analysis. In this model, not only does risk of misclassification at different levels of sensitivity decrease remarkably, but there will also be the possibility for recognition and analysis when basic pattern occur simultaneously. The efficiency and effectiveness of the model are shown by conducting tests based on simulation.     

Control chart pattern recognition using back propagation artificial neural networks

In this paper, control chart pattern recognition using artificial neural networks is presented. An important motivation of this research is the growing interest in intelligent manufacturing systems, specifically in the area of Statistical Process Control (SPC). Online automated process analysis is an important area of research since it allows the interfacing of process control with Computer Integrated Manufacturing (CIM) techniques. Two back-propagation artificial neural networks are used to model traditional Shewhart SPC charts and identify out-of-control situations as specified by the Western Electric Statistical Quality Control Handbook , including instability patterns, trends, cycles, mixtures and systematic variation. Using back propagation, patterns are presented to the network, and training results in a suitable model for the process. The implication of this research is that out-of-control situations can be detected automatically and corrected within a closed-loop environment. This research is the first step in an automated process monitoring and control system based on control chart methods. Results indicate that the performance of the back propagation neural networks is very accurate in identifying control chart patterns.     

Real-time recognition of control chart patterns in autocorrelated processes using a learning vector quantization network-based approach

Researchers have been investigating the use of artificial neural networks (NNs) in the application of control chart pattern (CCP) recognition with encouraging results in recent years. Most of the NN models in this field are designed to be used in uncorrelated processes where the process data are independent. Unfortunately, the prerequisite of data independence is not even approximately satisfied in many manufacturing processes. To the best of the author's knowledge, no research results have been published to date on the application of NNs for CCP recognition in autocorrelated processes. This work first shows that autocorrelation in process data greatly affects the performance of NN-based CCP recognizers developed with independent data and then presents a learning vector quantization network-based system that can effectively recognize CCPs in real-time for processes with various levels of autocorrelation. The system performance is evaluated in terms of the classification rate and the average run length. An empirical comparison using simulation indicates that the proposed learning-based system performs better than the traditional control chart methods in detecting shifts when the process data are positively correlated, while it also offers pattern classification. A demonstration example is provided using real data.     

基于数据融合的红外目标识别

为了获得良好的红外目标识别性能,综合应用了图像处理、模式识别和数据融合领域内的新技术.采用了神经网络和证据理论集成的数据融合方法进行目标识别的数据融合.根据LVQ神经网络在目标识别领域内应用特点,构造了基于证据理论的基本概率赋值函数.对此目标识别技术进行了测试,结果表明,采用此技术后的识别的可信度得到了较大提高.     

Detecting process non-randomness through a fast and cumulative learning ART-based pattern recognizer

An adaptive resonance theory (ART) based, general-purpose control chart pattern recognizer (CCPR) which is capable of fast and cumulative learning is presented. The implementation of this ART-based CCPR was made possible by introducing two key alternatives, that is, incorporating a synthesis layer in addition to the existing two-layer architecture and adopting a quasi-supervised training strategy. i A detailed algorithm with the training and the testing modes was presented. Extensive simulations and performance evaluations were conducted and proved that this ART-based CCPR indeed possesses the capability of fast and cumulative learning. When compared with a back-propagation pattern recognizer (BPPR), the ART-based CCPR is superior on cyclic patterns, inferior on mixture patterns, and comparable on other patterns. Furthermore, an ART-based CCPR is easier to develop since it needs fewer training templates and takes less time to learn. This study not only provides a basis for understanding the capabilities of ART-based neural networks on control chart pattern recognition but re-confirms the applicability of the neural network approach.     

基于神经网络的质量控制图模式识别技术的研究

提出了一种用于质量控制图模式识别的新的神经网络模型，它与以往的神经网络模型相比，具有较强的识别能力和较短的训练时间。     

基于随机森林的统计控制图模式识别研究

引入随机森林方法进行统计控制图模式识别的研究。提取了控制图的统计特征和形状特征,设计了5种不同的特征组合方法,利用蒙特卡洛仿真方法产生训练数据集和测试数据集,选取了常用的3种模式识别方法（支持向量机方法、人工神经网络方法、决策树方法）进行对比。实验结果表明,随机森林方法相比其他3种分类器方法,在分类准确率和消耗时间两个维度上都有明显优势,可以应用于统计过程控制图模式识别。     

Artificial neural networks for pattern recognition

This tutorial article deals with the basics of artificial neural networks (ANN) and their applications in pattern recognition. ANN can be viewed as computing models inspired by the structure and function of the biological neural network. These models are expected to deal with problem solving in a manner different from conventional computing. A distinction is made between pattern and data to emphasize the need for developing pattern processing systems to address pattern recognition tasks. After introducing the basic principles of ANN, some fundamental networks are examined in detail for their ability to solve simple pattern recognition tasks. These fundamental networks together with the principles of ANN will lead to the development of architectures for complex pattern recognition tasks. A few popular architectures are described to illustrate the need to develop an architecture specific to a given pattern recognition problem. Finally several issues that still need to be addressed to solve practical problems using ANN approach are discussed. This paper is mostly a consolidation of work reported by several researchers in the literature, some of which is cited in the references. The author has borrowed several ideas and illustrations from the references quoted in this paper.     

Gaussian mixture models-based control chart pattern recognition

Abnormal patterns exhibited in control charts can be associated with certain assignable causes for process variation. Hence, accurate and fast control chart pattern recognition (CCPR) is essential for significantly narrowing down the scope of possible causes that must be investigated, and speeds up the troubleshooting process. This study proposes a Gaussian mixture models (GMM)-based CCPR model that employs a collection of several GMMs constructed for CCPR. By using statistical features and wavelet energy features as the input features, the proposed CCPR model provides a more simple and effective training procedure and better generalisation performance than using a single CCPR recogniser, and hence is easier to be used by quality engineers and operators. Furthermore, the proposed model is capable of adapting novel control chart patterns (CCPs) by applying a dynamic modelling scheme. The experimental results indicate that the GMM-based CCPR model shows good detection and recognition performance for current CCPs and adapts further novel CCPs effectively. Moreover, the proposed model provides a promising way for the on-line recognition of CCPs because of its efficient computation and good pattern recognition performance. Analysis from this study provides guidelines for developing GMM-based statistical process control (SPC) recognition systems.     

基于模糊c均值聚类算法的控制图模式识别

控制图模式识别能够区分制造过程中的一般因素与异常因素,提高制造过程中的产品质量,减少成本,提高效益。利用蒙特卡洛方法产生样本;采用一维离散小波变换处理原始数据;利用模糊c均值聚类算法进行控制图模式识别。识别准确率99.43%,其标准差为0.002 8。这表明基于该方法的控制图模式识别准确率高,稳定性好,较现有的控制图模式识别方法具有简易、高效等特点。     

基于神经网络的桥梁损伤位置识别

损伤位置识别是对大型桥梁结构进行损伤检测的重要一步。以汲水门斜拉桥为背景,对应用神经网络的模式分类技术识别桥面结构损伤位置的方法进行了研究。使用了两种类型的网络,简称动态网络和GA网络,探讨了这一方法的可行性。动态网络的网络结构是根据训练进程而动态地确定的。GA网络是在训练中引进了遗传算法。比较了两种网络对损伤位置的识别效果。结果表明,应用神经网络的模式分类技术对桥梁桥面结构损伤位置识别的方法是可行的。只需要较少的输入数据,两种网络均可产生较好的识别结果。     

Recognition of control chart concurrent patterns using a neural network approach

Pattern recognition is an important issue in Statistical Process Control, as unnatural patterns exhibited by control charts can be associated with specific assignable causes adversely affecting the process. Neural network approaches to recognition of control chart patterns have been developed by several researchers in recent years, but to date these have been focused on recognition and analysis of single patterns such as sudden shifts, linear trends or cyclic patterns. This paper investigates the detection of concurrent patterns where more than one pattern exists simultaneously. The topology and training of a Back-Propagation Network (BPN) system is described. Extensive performance evaluation has been carried out using simulated data to develop a range of average run length-related performance indices, including new performance indices that are proposed to describe concurrent patterns recognition performance. Two evaluation scenarios were evaluated: in the first, unnatural patterns are already present; while in the second, patterns may appear progressively at any time. Numerical results are provided that indicate that the pattern recognizer can perform very well in the first scenario, while it performs effectively but not without deficiencies for some specific pattern combinations in the second evaluation approach. Limitations and potential improvements in the concurrent pattern recognition scheme are also discussed.     

基于自适应粒子群算法和支持向量机的控制图模式识别

针对目前多品种、复杂化的生产趋势,提出了一种基于自适应变异的粒子群算法（AMPSO）和支持向量机（SVM）的控制图失效模式识别的方法。利用SVM小样本学习能力,设计一对一的SVM多分类器进行控制图模式识别,并利用AMPSO算法优化SVM核函数的参数。通过对10种控制图模式(6种基本模式和4种混合模式)的20维特征仿真数据对该方法进行检验,并通过与BP、SVM、PSO SVM识别方法的对比分析。仿真试验表明该方法有效提高了控制图模式的识别精度,达到9814%,而BP仅有75%,为控制图在线实时识别提供了一种可行的途径。      

Modular artificial neural network for prediction of petrophysicalproperties from well log data

An application of Kohonen's self-organizing map (SOM), learning-vector quantization (LVQ) algorithms, and commonly used backpropagation neural network (BPNN) to predict petrophysical properties obtained from well-log data are presented. A modular, artificial neural network (ANN) comprising a complex network made up from a number of subnetworks is introduced. In this approach, the SOM algorithm is applied first to classify the well-log data into a predefined number of classes, This gives an indication of the lithology in the well. The classes obtained from SOM are then appended back to the training input logs for the training of supervised LVQ. After training, LVQ can be used to classify any unknown input logs. A set of BPNN that corresponds to different classes is then trained. Once the network is trained, it is then used as the classification and prediction model for subsequent input data. Results obtained from example studies using the proposed method have shown to be fast and accurate as compared to a single BPNN network     

Performance comparison between on-line sensors and control charts in manufacturing process monitoring

The rapid evolution of sensor technology, using techniques such as lasers, machine vision and pattern recognition, provides the potential to greatly improve the Statistical Process Control (SPC) method for monitoring manufacturing processes. This paper studies the method of using on-line sensors to monitor manufacturing processes and compares that method with the control chart method, a widely used SPC tool. Two separate economic models are formulated for using either a sensor or a control chart to monitor a manufacturing process. Then, the two models are compared in a sensitivity analysis with lespect to several process parameters.     

An attribute control chart for multivariate Poisson distribution using multiple dependent state repetitive sampling

In this paper, an attribute control chart for a multivariate Poisson distribution using multiple dependent state repetitive sampling (MDSRS) is presented. The evaluation of the proposed control chart is given through the average run length (ARL). The proposed control chart performs better than the existing control chart based on repetitive sampling and that using multiple dependent state sampling in terms of ARLs. A real example and a simulation study are added to explain the procedure and to demonstrate the power of the proposed control chart.     

基于径向基函数神经网络的滚动轴承故障模式的识别

径向基函数（RBF）神经网络是一种3层前馈性神经网络，它具有较强的函数逼近能力和分类能力。鉴于径向基函数神经网络的优点，在对滚动轴承振动信号特征分析的基础上，提出了采用时序方法对其建立AR模型，利用AR模型参数建立径向基函数神经网络，并用该网络对滚动轴承的故障模式进行了识别。理论和试验证明了该方法的有效性，且具有较高的识别精度。     

Identification of patterns in control charts for processes with statistically correlated noise

In real industrial scenarios, if the quality characteristics of a continuous or batch production process are monitored using Shewhart control charts, there could be a large number of false alarms about the process going out of control. This is because these control charts assume that the inherent noise of the monitored process is normally, independently and identically distributed, although the assumption of independence is not always correct for continuous and batch production processes. This paper presents three control chart pattern recognition systems where the inherent disturbance is assumed to be stationary. The systems use the first-order autoregressive (AR(1)), moving-average (MA(1)) and autoregressive moving-average (ARMA(1,1)) models. A special pattern generation scheme is adopted to ensure generality, randomness and comparability, as well as allowing the further categorisation of the studied patterns. Two different input representation techniques for the recognition systems were studied. These gave nearly the same performance for the MA(1) and ARMA(1,1) models, while the raw data yielded the highest accuracies when AR(1) was used. The effect of autocorrelation on the pattern recognition capabilities of the developed models was studied. It was observed that Normal and Upward Shift patterns were the most affected.