Abnormal Control Chart Patterns Research Articles

Research on the whole process monitoring and management of coal digital production based on IOT technology

Abstract This paper firstly explores the method and monitoring process of coal SPC production quality monitoring based on IOT technology and simulates different control chart patterns using the Monte Carlo method. Then a control chart pattern recognition model based on PCA-PSO-SVM is established to downscale the collected data using the PCA method and quickly discriminate whether the control chart is abnormal or not based on the OC-SVM algorithm. By identifying abnormal control chart patterns, real-time monitoring of the coal production process is realized. Finally, comparative analysis and fault source analysis are conducted to verify the sensitivity and effectiveness of the monitoring method in this paper. The difference in methane concentration between the experimental group and the comparison group is less than 10ppm, CO concentration is less than 5ppm, and dust concentration is less than 0.05mg/m3. It shows that the method of this paper is very effective for coal production monitoring.

Application of integrated recurrent neural network with multivariate adaptive regression splines on SPC-EPC process

The integration of statistical process control and engineering process control has been reported as an effective way to monitor and control the autocorrelated process. However, because engineering process control compensates for the effects of underlying disturbances, the disturbance patterns become very hard to recognize, especially when various abnormal control chart patterns are mixed and co-existed in the engineering process. In this study, a new control chart pattern recognition model which integrates multivariate adaptive regression splines and recurrent neural network is proposed to not only address the problem of feature selection (i.e., lagged process measurements) but also improve the pattern recognition accuracy. The performance of the proposed method is evaluated by comparing the recognition results of multivariate adaptive regression splines and recurrent neural network with the results of four competing approaches (multivariate adaptive regression splines-extreme learning machine, multivariate adaptive regression splines-random forest, single recurrent neural network, and single random forest) on the simulated individual process data. The experimental study shows that the proposed multivariate adaptive regression splines and recurrent neural network approach can not only solve the problem of variable selection but also outperform other competing models. Moreover, according to the lagged process measurements selected by the proposed approach, lagged observations that exerted significant impact on the construction of the control chart pattern recognition model can be identified successfully. This study has significant implications for research and practice in production management and provides a valuable reference for manufacturing process managers to better understand and develop strategies for control chart pattern recognition.

A cost-sensitive convolution neural network learning for control chart pattern recognition

Abnormal control chart patterns are naturally infrequent in an industrial setting. However, such patterns may indicate manufacturing faults that, if not treated in a timely manner, can lead to significant internal and external failure costs, ultimately threatening the product reputation. Therefore, the detection of abnormalities, which is sought in the well-known control chart pattern recognition (CCPR) problem, is of utmost importance. Standard machine learning algorithms have been extensively applied to this problem. However, they often produce biased classifiers unless the inherent data imbalancedness, which originates from the scarcity of abnormal patterns, are carefully addressed. In this paper, we develop a cost-sensitive classification scheme within a deep convolutional neural network (CSCNN) for the imbalanced CCPR problem. We further investigate the performance of our algorithm on both simulated and real-world datasets to determine separable and non-separable common fault patterns in a manufacturing setting. As the contribution of this work, we particularly demonstrate that the cost weighting strategy is both robust and efficient for moderately- and severely-imbalanced cases. We further show that our method can either be fine-tuned to specific faults or trained to detect multiple faults while remaining efficient for large datasets. To the best of our knowledge, this is the first deep CSCNN designed for imbalanced CCPR problems, which presents great promise for other manufacturing applications in the presence of imbalanced datasets.

Improved statistical features-based control chart patterns recognition using ANFIS with fuzzy clustering

Various types of abnormal control chart patterns can be linked to certain assignable causes in industrial processes. Hence, control chart patterns recognition methods are crucial in identifying process malfunctioning and source of variations. Recently, the hybrid soft computing methods have been implemented to achieve high recognition accuracy. These hybrid methods are complicated, because they require optimizing algorithms. This paper investigates the design of efficient hybrid recognition method for widely investigated eight types of X-bar control chart patterns. The proposed method includes two main parts: the features selection and extraction part and the recognizer design part. In the features selection and extraction part, eight statistical features are proposed as an effective representation of the patterns. In the recognizer design part, an adaptive neuro-fuzzy inference system (ANFIS) along with fuzzy c-mean (FCM) is proposed. Results indicate that the proposed hybrid method (FCM-ANFIS) has a smaller set of features and compact recognizer design without the need of optimizing algorithm. Furthermore, computational results have achieved 99.82% recognition accuracy which is comparable to published results in the literature.

Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic

Locating the assignable causes by use of the abnormal patterns of control chart is a widely used technology for manufacturing quality control. If there are uncertainties about the occurrence degree of abnormal patterns, the diagnosis process is impossible to be carried out. Considering four common abnormal control chart patterns, this paper proposed a characteristic numbers based recognition method point by point to quantify the occurrence degree of abnormal patterns under uncertain conditions and a fuzzy inference system based on fuzzy logic to calculate the contribution degree of assignable causes with fuzzy abnormal patterns. Application case results show that the proposed approach can give a ranked causes list under fuzzy control chart abnormal patterns and support the abnormity eliminating.

Intelligent Recognition of Mixture Control Chart Pattern Based on Quadratic Feature Extraction and SVM with AMPSO

Zhang, M.; Cheng, W., and Guo, P., 2015. Intelligent recognition of mixture control chart pattern based on quadratic feature extraction and SVM with AMPSO.Take control charts as the principal statistical process control tools. The recognition accuracy of abnormal control chart patterns (CCPs) directly influences the quality control of production process. (Most of the existing studies focus on the basic recognition of abnormal CCPs, but the observed process data could be of mixture patterns in real world, which consists of two or three basic patterns. This paper introduces a hybrid intelligent model for recognizing the mixture control chart pattern that includes three main aspects: feature extraction, classifier and parameter optimization. In feature extraction, statistical and sharp features of observation data are used as data input and principal component analysis as the second feature extraction to get the effective data of the classifier. A multi-class support vector machine (SVM) is applied for recognizing the mixture patterns. Finally, adaptive mutation particle swam optimization is used to optimize the SVM classifier by searching the best values of the parameters of SVM kernel function. The simulation results show the proposed algorithm with little features has better recognition accuracy compared with other methods.

Textile Processing Anomaly Detection Based on Time Domain Features and GA-SVM

Demands of automatic recognition of abnormal patterns in control charts have been increasing nowadays in manufacturing process. Control chart pattern recognition is an important statistical process control tool used to determine whether a process is run in its intended range or not and eliminate the potential attribution factors as far as possible according to the abnormal condition shown in the control chart. This paper uses the time domain features as input vector and genetic algorithm to obtain the optimal parameters of SVM in a self-adapted manner. Design anomaly detection model for dynamic process is made to realize control chart pattern recognition under the complex condition. The experimental results show that the proposed approach method has higher detection accuracy and stronger generalization ability than other methods, so it is more suitable for quality control in production field.

Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features

Control charts have been widely utilized for monitoring process variation in numerous applications. Abnormal patterns exhibited by control charts imply certain potentially assignable causes that may deteriorate the process performance. Most of the previous studies are concerned with the recognition of single abnormal control chart patterns (CCPs). This paper introduces an intelligent hybrid model for recognizing the mixture CCPs that includes three main aspects: feature extraction, classifier, and parameters optimization. In the feature extraction, statistical and shape features of observation data are used in the data input to get the effective data for the classifier. A multiclass support vector machine (MSVM) applies for recognizing the mixture CCPs. Finally, genetic algorithm (GA) is utilized to optimize the MSVM classifier by searching the best values of the parameters of MSVM and kernel function. The performance of the hybrid approach is evaluated by simulation experiments, and simulation results demonstrate that the proposed approach is able to effectively recognize mixture CCPs.

Control Chart Pattern Recognition Based on Wavelet Analysis

Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. In this paper, we have constructed the recognition model for control chart pattern using one-dimensional discrete wavelet transform and BP neural network. Then, using Monte-Carlo method to generate the data, we have compared between the performances of the model using raw data and the model based on wavelet transform. In order to select the optimal wavelet, we have compared the recognition accuracy of different wavelets (DbN, SymN and CoifN) on different levels. The simulation results show that model based on wavelet transform in this paper have higher recognition accuracy and the recognition accuracy using the wavelet of Coif4 on the level 3 is 97.64% and relatively stable for various patterns.

Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine

Since abnormal control chart patterns (CCPs) are indicators of production processes being out-of-control, it is a critical task to recognize these patterns effectively based on process measurements. Most methods on CCP recognition assume that the process data only suffers from single type of unnatural pattern. In reality, the observed process data could be the combination of several basic patterns, which leads to severe performance degradations in these methods. To address this problem, some independent component analysis (ICA) based schemes have been proposed. However, some limitations are observed in these algorithms, such as lacking of the capability of monitoring univariate processes with only one key measurement, misclassifications caused by the inherent permutation and scaling ambiguities, and inconsistent solution. This paper proposes a novel hybrid approach based on singular spectrum analysis (SSA) and support vector machine (SVM) to identify concurrent CCPs. In the proposed method, the observed data is first separated by SSA into multiple basic components, and then these separated components are classified by SVM for pattern recognition. The scheme is suitable for univariate concurrent CCPs identification, and the results are stable since it does not have shortcomings found in the ICA-based schemes. Furthermore, it has good generalization performance of dealing with the small samples. Superior performance of the proposed algorithm is achieved in simulations.

Recognition of control chart patterns using an intelligent technique

Control chart patterns (CCPs) are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in the manufacturing processes. This paper presents a novel hybrid intelligent method for recognition of common types of CCP. The proposed method includes three main modules: the feature extraction module, the classifier module and optimization module. In the feature extraction module, a proper set of the shape features and statistical features is proposed as the efficient characteristic of the patterns. In the classifier module multilayer perceptron neural network and support vector machine (SVM) are investigated. In support vector machine training, the hyper-parameters have very important roles for its recognition accuracy. Therefore, in the optimization module, improved bees algorithm is proposed for selecting of appropriate parameters of the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy.

Control chart pattern recognition using K-MICA clustering and neural networks

Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved.

Recognition of control chart patterns using swarm intelligence and neural networks based on the statistical and shape features

AbstractAutomatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in the manufacturing processes. In this paper, a hybrid intelligent system is proposed for the recognition of control chart patterns. In this system, we have used a proper set of shape features and statistical features as the efficient characteristics of the patterns. Then we proposed a hybrid heuristic recognition system based on particle swarm optimization to improve the generalization performance of the radial basis function neural network classifier. For this purpose, we have optimized the classifier design by searching for the best value of the parameters that tune its discriminate function. The obtained results show that the proposed technique has high recognition accuracy in comparison with other techniques. This recognition accuracy is achieved with fewer training samples. IEEJ Trans 2010 DOI: 10.1002/tee.20610

Control chart pattern recognition using a novel hybrid intelligent method

Control chart patterns (CCPs) are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in the manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of common types of CCP. The proposed method includes three main modules: a feature extraction module, a classifier module and an optimization module. In the feature extraction module, the multi-resolution wavelets (MRW) are proposed as the effective features for representation of CCPs. These features are novel in this area. In the classifier module, because of the promising generalization capability of support vector machines, a multi-class SVM (SVM) based classifier is proposed. In support vector machine training, the hyper-parameters have very important roles for its recognition accuracy. Therefore, in the optimization module, an efficient genetic algorithm is proposed for selecting of appropriate parameters of the classifier. Simulation results confirm that the proposed system outperforms other methods and shows high recognition accuracy about 99.37%.

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.

Improved recognition of control chart patterns using artificial neural networks

Recognition of abnormal patterns in control charts provides clues to reveal potential quality problems in the manufacturing processes. One potentially popular approach for recognizing different control chart patterns (CCPs) is to develop heuristics based on various shape features of the patterns. The advantage of this approach is that the users can easily understand how a particular pattern is identified. However, consistency in the recognition performance is found to be considerably poor in the heuristics approach. Since shape features represent the main characteristics of the patterns in a condensed form, artificial neural network (ANN) with features extracted from the process data as input vector representation can facilitate efficient pattern recognition with a smaller network size. In this paper, a set of seven shape features is selected, whose magnitudes are independent of the process mean and standard deviation under a special representation of the sampling interval in the control chart plot. Based on these features, the CCPs are recognized using a multilayered perceptron neural network trained by back-propagation algorithm. The recognizer can recognize all the eight commonly observed CCPs. Extensive performance evaluation of this recognizer is carried out using simulated pattern data. Numerical results indicate that the developed ANN recognizer can perform well in real time process control applications with respect to both recognition accuracy and consistency.

A neural network based model for abnormal pattern recognition of control charts

In the past years, artificial neural networks were used for pattern recognition of control charts with an emphasis on recognizing specific abnormal patterns of control charts. This paper proposes an artificial neural network based model, which contains several back propagation networks, to both recognize the abnormal control chart patterns and estimate the parameters of abnormal patterns such as shift magnitude, trend slope, cycle amplitude and cycle length, so that the manufacturing process can be improved. Numerical results show that the proposed model also has a good recognition performance for mixed abnormal control chart patterns (e.g. a pattern with trend and cycle characteristics).