Control Chart Patterns Research Articles

On-line recognition of mixture control chart patterns using hybrid CNN and LSTM for auto-correlated processes

Statistical process control (SPC), designed to detect and identify process disturbances, is an effective quality control method for ensuring process stability. Owing to the growing automation of the industry, the manufacturing process can be autocorrelated. Engineer process control (EPC) is typically used to address autocorrelation. However, process disturbances can be offset by feedback compensation, making control chart patterns (CCPs) difficult to identify. Most studies on control chart pattern recognition (CCPR) techniques are based on a single abnormal control chart pattern (CCP). However, a mixture of CCPs can occur during real-world manufacturing processes. With the development of intelligent manufacturing systems, early detection of abnormal concurrent CCPs has become an important issue. In this study, a hybrid model combining a convolutional neural network (CNN) and long short-term memory (LSTM) in an online detection system was used to recognize the concurrent CCPR problem in SPC-EPC processes. The results showed that the average accuracy of the deep learning CNN-LSTM method was 99.83%, which was significantly better than that of the machine learning method. In addition, the running time of the CNN-LSTM model was shortened. In a comparison of online monitoring between the deep learning method and the machine learning method, the CNN-LSTM model for an online monitoring system identified abnormal concurrent patterns faster. Therefore, the proposed CNN-LSTM online monitoring scheme can be applied confidently and successfully to identify mixture CCPs in an SPC-EPC process.

Concurrent control chart pattern recognition in manufacturing processes based on zero-shot learning

In real industrial settings, collecting and labeling concurrent abnormal control chart pattern (CCP) samples are challenging, thereby hindering the effectiveness of current CCP recognition (CCPR) methods. This paper introduces zero-shot learning into quality control, proposing an intelligent model for recognizing zero-shot concurrent CCPs (C-CCPs). A multiscale ordinal pattern (OP) feature considering data sequential relationship is proposed. Drawing from expert knowledge, an attribute description space (ADS) is established to infer from single CCPs to C-CCPs. An ADS is embedded between features and labels, and the attribute classifier associates the features and attributes of CCPs. Experimental results demonstrate an accuracy of 98.73% for 11 unseen C-CCPs and an overall accuracy of 98.89% for all 19 CCPs, without C-CCP samples in training. Compared with other features, the multiscale OP feature has the best recognition effect on unseen C-CCPs.

Pattern recognition of control charts based on data feature enhancement and ensemble learning of classifiers for dimensional accuracy of products

Product quality in manufacturing is directly related to the reliability, durability and safety of the product. Product quality can be controlled by building a product control chart pattern recognition model. The complexity and temporal sequence of product quality data make it difficult to extract effective control chart features, and it is difficult for common classifiers to play an effective role for each control chart pattern category, thus affecting the accuracy of pattern recognition. Therefore, this paper proposes a data feature enhancement processing method that combines Smote algorithm and smooth shift processing. This method reveals data features by performing feature enhancement processing on raw data and extracts statistical and shape features for dimensionality reduction to improve recognition accuracy and efficiency. Based on this approach, this paper also proposes an ensemble learning model based on the Stacking method, which incorporates BP, ELM, PNN and SVM classifiers for control chart pattern recognition. Through extensive experimental validation, the data feature enhancement method improves the accuracy of the weak classifier normal pattern recognition by 30%, and effectively reduces the misclassification in the quality control process. The ensemble learning model is stronger than the weak classifier, and its recognition accuracy can reach 97.31%.

Control chart pattern recognition for small samples based on Siamese Neural Network

Quality control for small samples is growing in popularity due to its broad application prospects and high research value, and one of the commonly used methods for quality control nowadays is control chart pattern recognition (CCPR). Deep learning is an effective way for CCPR, but its recognition rate of control chart patterns for small samples is low and the quality control capability is limited. Therefore, to solve these problems, this paper first proposes a Perceptron-Convolutional Siamese Neural Network (PCSNN) model to improve the pattern recognition rate of small sample control chart. And then, three pattern recognition models (Convolutional Neural Network, Perceptron-Convolutional Neural Network, and PCSNN) are compared to verify the validity of the proposed model. After that, simulation experiments are conducted, and results show that the proposed PCSNN model has a high recognition rate of control chart patterns for small samples and performs well when the sample quality parameters change within a certain range. Finally, the proposed model is applied to the manufacturing process of automatic boring of enterprise gearbox shell, and its validity is verified.

An imbalance-aware BiLSTM for control chart patterns early detection

Digital twins-based predictive models find their roots in smart manufacturing. However, their potential applications to control chart pattern recognition (CCPR) algorithms, which lie at the heart of advanced fault detection systems, remain underexplored. A key challenge in CCPR models arises from the intrinsic imbalance between classes, which can compromise the model’s performance if left untreated. Further, existing CCPR models are often trained over simulated control chart data in which abnormal patterns are generated separately from abnormal signals; the resulting classifiers, however, perform poorly in the early detection of abnormalities in real-time production environments. To address these challenges, we develop a cost-sensitive, bi-directional long short-term memory neural network for data sequences with mixed normal and abnormal signals. We further introduce a novel adaptive weighting strategy for data generation by enforcing the rates of abnormal signals within mini-batch distributions. Our model benefits from a new bi-objective early stopping technique, which optimally balances loss minimization and G-mean maximization for model training. Finally, we introduce a novel rolling window-based metric for evaluating CCPR classifier stability. We conduct a comprehensive experimental study of our model using both simulated data and two real-world datasets collected from biomanufacturing and wafer industries. The results of our study consistently demonstrate the superiority of our proposed stopping technique over traditional methods. Our experiments further show the effectiveness of our proposed model in maintaining the classifier stability and specifying optimal process monitoring window length within the datasets.

Diesel engine quality abnormal patterns recognition based on feature fusion and adaptive decision fusion

The current assembly process of marine diesel engines is low in intelligence and the control chart pattern classifier with unstable performance, which makes it difficult to control and identify the quality control chart pattern. This paper proposes a new assembly quality control diagram recognition method based on an adaptive decision model to address these problems. Through characteristics and changes of the diesel engine assembly process analyses, the triangular norm is used to fuse the extracted shape features and statistical features to reduce the influence of data fluctuation and imbalance on pattern recognition. An adaptive decision fusion model of the assembly process is established by defining multiple weights with considering the complexity and uncontrollability of the diesel engine assembly process. Based on these, the fusion coefficients within the adaptive decision model are optimized by the Ant Lion Optimization algorithm (ALO) to improve the decision efficiency and classification precision. To validate the proposed model, diesel engine exhaust pressure is selected as a case for abnormal pattern recognition, and the ability of the model is discussed in terms of recognition accuracy and stability.

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.

A Novel Scheme of Control Chart Patterns Recognition in Autocorrelated Processes

Control chart pattern recognition (CCPR) can quickly recognize anomalies in charts, making it an important tool for narrowing the search scope of abnormal causes. Most studies assume that the observations are normal, independent and identically distributed (NIID), while the assumption of independence cannot always be satisfied under continuous manufacturing processes. Recent research has considered the existence of autocorrelation, but the recognition rate is overestimated. In this paper, a novel scheme is proposed to recognize control chart patterns (CCPs) in which the inherent noise is autocorrelated. By assuming that the inherent noise follows a first-order autoregressive (AR (1)) process, the one-dimensional convolutional neural network (1DCNN) is applied for extracting features in the proposed scheme, while the grey-wolf-optimizer-based support vector machine (GWOSVM) is employed as a classifier. The simulation results reveal that the proposed scheme can effectively identify seven types of CCPs. The overall accuracy is 89.02% for all the autoregressive coefficients, and the highest accuracy is 99.43% when the autoregressive coefficient is on the interval (−0.3, 0]. Comparative experiments indicate that the proposed scheme has great potential to identify CCPs in autocorrelated processes.

A novel autonomous machine learning technique for recognizing control chart patterns

A number of strategies have been developed for controlling and monitoring the production process since the quality of the product has emerged as one of the key concerns in today's manufacturing sector. Control charts are the best tools for monitoring and adjusting products and processes. This research proposes a novel automatic method for the recognition of nine control chart patterns (CCPs) based on novel autonomous machine learning. The classification portion and the tuning portion make up the two main components of this procedure. Support Vector Machine (SVM) have demonstrated outstanding performance over the past few years on a variety of applications, including signal dispensation, speech detection, and image processing. SVM is consequently employed as the intelligent classifier for the recognition of CCPs in the classification phase. One key challenge with SVM is that it requires a high level of expertise to choose appropriate parameters, such as the quantity of kernel and their spatial diameters, knowledge rate, etc. It is difficult to fine-tune the SVM parameters because of their domestic dependence. These problems led to the employment of the Harmony Search (HS) Algorithm for the best tuning of SVM parameters in the tuning section of the proposed technique. Instead of depending on any feature engineering procedures, the suggested method, in contrast to the popular CCPs recognition methods, takes raw data and runs it through many hidden layers to obtain the best feature representation. The quantitative and simulation results demonstrate the suggested method's performance advantage over the earlier methods.

An improved features selection approach for control chart patterns recognition

<span lang="EN-US">Control chart patterns (CCPs) are an essential diagnostic tool for process monitoring using statistical process control (SPC). CCPs are widely used to improve production quality in many engineering applications. The principle is to recognize the state of a process, either a stable process or a deterioration to an unstable process. It is used to significantly narrow the set of possible assignable causes by shortening the diagnostic process to improve the quality. Machine learning techniques have been widely used in CCPs. Artificial neural networks with multilayer perceptron (ANN-MLP) are one of the standard tools used for this purpose. This paper proposes an improved features selection method to select the best features as input representation for control chart patterns recognition. The results demonstrate that the proposed approach can effectively recognize CCPs even for small patterns with a mean shift of less than 1.5 sigma. The dimensional reduction was achieved by employing Relief, correlation, and Fisher algorithms (RCF) for feature selection and (ANN-MLP) as a classifier (RCF-ANN). This study provides an experimental result that compares the performance before and after dimensional reduction.</span>

Multivariate Process Control Chart Pattern Classification Using Multi-Channel Deep Convolutional Neural Networks

Statistical process control (SPC) charts are commonly used to monitor quality characteristics in manufacturing processes. When monitoring two or more related quality characteristics simultaneously, multivariate T2 control charts are often employed. Like univariate control charts, control chart pattern recognition (CCPR) plays a crucial role in multivariate SPC. The presence of non-random patterns in T2 control charts indicates that a process is influenced by one or more assignable causes and that corrective actions should be taken. In this study, we developed a deep learning-based classification model for recognizing control chart patterns in multivariate processes. To address the problem of the insufficient representation of one-dimensional (1D) data, we explore the advantages of using two-dimensional (2D) image data obtained from a threshold-free recurrence plot. A multi-channel deep convolutional neural network (MCDCNN) model was developed to incorporate both 1D and 2D representations of control chart data. This model was tested on multivariate processes with different covariance matrices and compared with other traditional algorithms. Moreover, the effects of imbalanced datasets and dataset size on classification performance were analyzed. Simulation studies revealed that the developed MCDCNN model outperforms other techniques in identifying multivariate non-random patterns. For the most significant one, our proposed MCDCNN method achieved a 10% improvement over traditional methods. The overall results suggest that the developed MCDCNN model can be beneficial for intelligent SPC.

Control chart pattern recognition for imbalanced data based on multi-feature fusion using convolutional neural network

As the most practical quality control process monitoring tool, control chart patterns (CCPs) can determine abnormal conditions in the production process. Therefore, automatic and accurate recognition of CCP is critical for improving production efficiency and quality in the manufacturing process. In the actual production process, the CCPs data has mixture patterns as well as single patterns, more importantly, the control chart pattern data proportion is imbalanced. Manual recognition is a costly endeavor, and automatic recognition is simpler and more effective. By applying the deep learning method to control chart pattern recognition (CCPR), abnormal patterns can be recognized in real-time. In this paper, the SMOTE method was used to balance the imbalances in data set, then some features in the control chart patterns were extracted and fused with the data. A method of abnormal control chart pattern recognition based on imbalanced data was proposed by using convolutional neural network. A comparison was made between the performance of the convolutional neural network and that of other methods in recognizing imbalanced patterns in control charts. According to the simulation results, compared with the raw data and other classifiers, the proposed multi-feature fusion and convolutional neural network (MFF-CNN) method has a better recognition performance for abnormal CCPs.

A novel fault early warning method for mechanical equipment based on improved MSET and CCPR

Prompt fault early warning is a significant approach to improve the reliability of mechanical equipment. An abnormal state recognition method based on improved multivariate state estimation technology (MSET) and control chart pattern recognition (CCPR) is proposed. First, the health data band (HDB) of equipment is established through a k-nearest neighbor algorithm. Meanwhile, a dynamic memory matrix construction approach based on a clustering algorithm is presented to improve the calculation efficiency of MSET. The CCPR is integrated to reduce limitations such as artificial interference in the traditional threshold method of fault early warning. The proposed method is applied into two cases of rolling bearing and control moment gyro so as to compare its performance with other MSET modeling methods. According to the results, the proposed method can detect equipment abnormalities hours to weeks in advance, and significantly reduce the false alarm rate and calculation time.

Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns

Manufacturing processes have become highly accurate and precise in recent years, particularly in the chemical, aerospace, and electronics industries. This has attracted researchers to investigate improved procedures for monitoring and detection of small process variations to remain in line with such advances. Among these techniques, statistical process controls (SPC), in particular the control chart pattern (CCP), have become a popular choice for monitoring process variance, being utilized in numerous industrial and manufacturing applications. This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. Before advancing to the classification step, Nelson’s Rus Rules were utilized as a monitoring rule to distinguish between stable and unstable processes. The study’s findings indicate that the proposed method improves classification performance for patterns with mean changes of less than 1.5 sigma, and confirm that the performance of the ensemble classifier is superior to that of the individual classifier. The ensemble classifier can distinguish unstable pattern types with a classification accuracy of 99.55% and an ARL1 of 11.94.

PCA-WA Based Approach for Concurrent Control Chart Pattern Recognition

PCA-WA Based Approach for Concurrent Control Chart Pattern Recognition

A probabilistic LSTM-MC dropout model for Control Chart Pattern prediction

Control chart, a powerful tool of statistical process control, monitors efficiently the production quality. In practice, it is realized that each pattern in a control chart usually corresponds to a functional/failed to state of the production process. Most existing works in the literature focus on the identification of control chart patterns (CCPs). In this paper, we propose a prognostic model for CCPs forecasting. The model is constructed based on the probabilistic long-short-term memory (LSTM) network to capture the dependencies and uncertainties of the control chart data. The performance of the proposed model is then validated through a numerical study. The prediction results can be used as a basis for the predictive maintenance decision-making and helping to avoid unexpected failures of the production process.

Control chart pattern recognition using spectral clustering technique and support vector machine under gamma distribution

Most studies on control chart patterns (CCPs) recognition assume that the measured values of process characteristic follow a normal distribution. However, by collecting large-size on-line datasets, it could be found that this assumption may not be tenable. In the present study, a method to combine spectral clustering technique with support vector machine (SVM) for recognizing CCPs under a gamma distribution, which is typically employed to represent various probability distributions, is proposed. Spectral clustering is a useful tool for segmenting the observation window to obtain the correct feature set. Multiple SVM classifiers are applied to recognize CCPs. Recognition accuracy and average run length (ARL) are indicators that are used to validate the CCP recognition ability of our proposed method. Based on the comparative studies, our proposed method in the recognition efficiency of most CCP types generally outperforms the Shewhart X¯ chart and EWMA chart with run rules. It is also noted that a more skewed underlying distribution always leads to a higher false alarm rate for our proposed method. A real-world case study of the tire manufacturing process is given to illustrate how our proposed method can be practically applied to identify and recognize the CCPs.

Double Moving Average Control Chart for Autocorrelated Data

The assumption of normality and independence is necessary for statistical inference of control charts. Misleading results are obtained if the traditional control chart technique is applied on the auto-correlated data. When data is correlated, a time series model is employed to produce an optimum output. The objective is to create a new control chart methodology that takes the autocorrelation of observations into account. Charts of Moving Average, Exponentially Weighted and Cumulative Sum better perform in the existence of autocorrelation data for small and moderate changes. The proposed methodology is highly skilled and receptive to minor, moderate and major changes in the process. Propsed DMA chart increases efficiency of average run length (ARL) chart for moving average (MA) to detect the small to medium magnitude shifts in the mean. The simulation also demonstrates that the DMA chart with spans of w=10 and 15 generally performs well in terms of average run length (ARL) as compared to clasical MA. This research may be extended to a multivariate autocorrelated statistical process control, but it can also be used to recognise and categorise seven categories of traditional control chart patterns, such as Downward, Upward Shift, Normal Trend, Cyclic, Systematic patterns, Increasing and Decresing Trend. In order to identify and categorize a set of subclasses of abnormal patterns, this model (multivariate autocorrelated statistical process control chart) should employ a multilayer feed forward Artificial Neural Network (ANN) architecture controlled by a back-propagation learning rule.

Recognition efficiency enhancement of control chart pattern using ensemble MLP neural network

chart pattern for six different fundamental patterns have been proposed. The multilayer perceptron based neural network has applied as an entity to form the ensemble. The loss of generalization property of an individual neural network has been fulfilled by providing the diversity in the training data for the individual entity of ensemble. The outcome of individual recognizer has combined over their decision value to include the outcome knowledge of each and every entity instead of majority voting of decision. The proposed method has been verified over a large set of data experimentally and observed that there is significant improvement with proposed ensemble approach in compare to individual recognizer.

Concurrent Control Chart Pattern Recognition: A Systematic Review

The application of statistical methods to monitor a process is critical to ensure its stability. Statistical process control aims to detect and identify abnormal patterns that disrupt the natural behaviour of a process. Most studies in the literature are focused on recognising single abnormal patterns. However, in many industrial processes, more than one unusual control chart pattern may appear simultaneously, i.e., concurrent control chart patterns (CCP). Therefore, this paper aims to present a classification framework based on categories to systematically organise and analyse the existing literature regarding concurrent CCP recognition to provide a concise summary of the developments performed so far and a helpful guide for future research. The search only included journal articles and proceedings in the area. The literature search was conducted using Web of Science and Scopus databases. As a result, 41 studies were considered for the proposed classification scheme. It consists of categories designed to assure an in-depth analysis of the most relevant topics in this research area. Results concluded a lack of research in this research field. The main findings include the use of machine learning methods; the study of non-normally distributed processes; and the consideration of abnormal patterns different from the shift, trend, and cycle behaviours.