Control Chart Patterns Research Articles

Statistical Process Control with Intelligence Based on the Deep Learning Model

Statistical process control (SPC) is an important tool of enterprise quality management. It can scientifically distinguish the abnormal fluctuations of product quality. Therefore, intelligent and efficient SPC is of great significance to the manufacturing industry, especially in the context of industry 4.0. The intelligence of SPC is embodied in the realization of histogram pattern recognition (HPR) and control chart pattern recognition (CCPR). In view of the lack of HPR research and the complexity and low efficiency of the manual feature of control chart pattern, an intelligent SPC method based on feature learning is proposed. This method uses multilayer bidirectional long short-term memory network (Bi-LSTM) to learn the best features from the raw data, and it is universal to HPR and CCPR. Firstly, the training and test data sets are generated by Monte Carlo simulation algorithm. There are seven histogram patterns (HPs) and nine control chart patterns (CCPs). Then, the network structure parameters and training parameters are optimized to obtain the best training effect. Finally, the proposed method is compared with traditional methods and other deep learning methods. The results show that the quality of extracted features by multilayer Bi-LSTM is the highest. It has obvious advantages over other methods in recognition accuracy, despite the HPR or CCPR. In addition, the abnormal patterns of data in actual production can be effectively identified.

Control chart patterns recognition using ANFIS with new training algorithm and intelligent utilization of shape and statistical features

This paper presents a new method for recognition of nine control chart patterns (CCPs) based on the intelligent use of shape and statistical features and optimized fuzzy system. The proposed technique contains three levels of separation. In each level of separation, an effective set of shape and statistical features are utilized as the input of classifier for recognizing a part of patterns. Due to the good performance of the adaptive neuro-fuzzy inference system (ANFIS) in pattern recognition problems, in the proposed method an ANFIS is used as a classifier at each level of separation which is trained by chaotic whale optimization algorithm (CWOA). Intelligent utilization of new extracted features, improving robustness of ANFIS and considering nine patterns in CCP recognition problem are the main contribution of the proposed method. The simulation results showed that the proposed method performs better than other similar methods and can recognize the type of pattern with 99.77% accuracy.

Efficient mixture control chart pattern recognition using adaptive RBF neural network

In this paper, a stochastic gradient method based adaptive version of the radial basis function neural network has proposed to map the pattern features of the control chart patterns in different categories to recognize their belonging class. Adaptiveness has given over the spreadness and centers of Gaussian basis function appeared in the hidden nodes of the radial basis function neural network. Along with normal abnormalities in patterns, the mixture of different abnormal patterns has also considered capturing the worst possible conditions of abnormalities in real time. The advantages of the proposed method have appeared as very high recognition accuracy, minimum error in learning and generalize performance with small training dataset in control chart pattern recognition. Achieved performance has compared with the state of art results available in the literature which has applied feature based recognition using Support vector machine and Genetic algorithm. The proposed method has enhanced the recognition generalization of control chart patterns with simplicity in design and high level of decision confidence. The performances have achieved through the simulation-based experiments over a huge number of patterns containing ten different types of pattern and on average, 99.99% accuracy has achieved.

Identification of various control chart patterns using support vector machine and wavelet analysis

Identification of various control chart patterns using support vector machine and wavelet analysis

A control chart pattern recognition system for feedback-control processes

The automated diagnosis of control charts to detect faults is a problem studied by many researchers. In recent years, they have turned their attention to processes that do not fulfil the condition of having normally, identically and independently distributed (NIID) variables. With those processes, it is common to have one or more manipulatable variables that can affect the quality characteristic under investigation. The Engineering Process Control (EPC) approach is often used to minimise the variance around the target value of the monitored characteristic by adjusting the manipulatable variables. In this work, a control chart pattern recognition (CCPR) system was developed for processes adjusted by EPC (also known as SPC-EPC or feedback-control processes). This issue of identification of simple control chart patterns for feedback-control processes had previously not been studied. A Machine Learning algorithm was proposed to train a pattern recognition system. All the possible combinations of factors of the CCPR system were studied to determine the combination yielding the highest recognition accuracy, namely, using raw data as input, generating patterns with significance level α = 0.01, monitoring the output signal, and employing a Proportional Integrative Derivative (PID) controller and the Radial Basis Function (RBF) kernel. This combination yielded overall accuracies of 94.18% and 94.14% for the AR(1) and ARMA(1,1) models, respectively.

Control chart pattern recognition using the convolutional neural network

Unnatural control chart patterns (CCPs) usually correspond to the specific factors in a manufacturing process, so the control charts have become important means of the statistical process control. Therefore, an accurate and automatic control chart pattern recognition (CCPR) is of great significance for manufacturing enterprises. In order to improve the CCPR accuracy, experts have designed various complex features, which undoubtedly increases the workload and difficulty of the quality control. To solve these problems, a CCPR method based on a one-dimensional convolutional neural network (1D-CNN) is proposed. The proposed method does not require to extract complex features manually; instead, it uses a 1D-CNN to obtain the optimal feature set from the raw data of the CCPs through the feature learning and completes the CCPR. The dataset for training and validation, containing six typical CCPs, is generated by the Monte-Carlo simulation. Then, the influence of the network structural parameters and activation functions on the recognition performance is analyzed and discussed, and some suggestions for parameter selection are given. Finally, the performance of the proposed method is compared with that of the traditional multi-layer perceptron method using the same dataset. The comparison results show that the proposed 1D-CNN method has obvious advantages in the CCPR tasks. Compared with the related literature, the features extracted by the 1D-CNN are of higher quality. Furthermore, the 1D-CNN trained with simulation dataset still perform well in recognizing the real dataset from the production environment.

Control chart pattern identification using a synergy between neural networks and bees algorithm

Control chart pattern identification using a synergy between neural networks and bees algorithm

Feature extraction in control chart patterns with missing data

Data preprocessing and feature extraction are critical steps in control chart pattern (CCP) recognition for reducing dimensionality and irrelevant information. To ensure good quality of input representation, it is important to handle missing values on control charts before feature extraction. Excluding missing values and imputing them with plausible values are two common missing data handling approaches in the literature. In this paper imputation capability of exponentially weighted moving average (EWMA) was investigated. Incomplete process data for three missingness mechanisms namely, missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR) were investigated using computer simulation. Missing data at severity levels i.e. 0, 5, 10, 15, 20, 25 and 50 % were evaluated. The investigation covers feature mean, standard deviation, skewness, kurtosis and quartiles extracted from imputed patterns. The imputation performance was measured by comparing the deviation between full patterns and patterns with missing values in term of mean square error (MSE). The results show that EWMA imputation was highly reliable to recover missing values as evident form low feature deviations, MSE values; 0.04 (random), 0.04 (trend-up), 0.3 (shift-up) and 0.5 (cycle) respectively. The results suggest that EWMA imputation technique is superior than the mean and median imputations.

Control Chart Pattern Recognition Method Based on Improved One-dimensional Convolutional Neural Network

The application of statistical process control (SPC) has promoted production quality improvement of many enterprises. As a core tool of SPC, control chart is used to reflect the production state. In addition to normal pattern, abnormalities in the production process can be summarized in seven basic control chart patterns (CCPs). The recognition of CCPs is helpful to identify quality failures and find root abnormal causes in time. Convolutional neural network (CNN) is a classical model in the field of deep learning. CNN can automatically extract features from the raw data, so the operation of constructing manual features can be omitted. In this paper, the one-dimensional CNN is applied to the recognition of CCPs and achieves 98.96% average recognition accuracy in 30 tests. What’s more, even if there is a deviation between the distribution of test data and training data, the model still shows excellent generalization performance.

A New Automatic Method for Control Chart Patterns Recognition Based on ConvNet and Harris Hawks Meta Heuristic Optimization Algorithm

The productions quality has become one of the essential issues in the modern manufacturing industry and several techniques have introduced for control and monitoring the production process. Control charts are the most practical and popular tools for continuously monitoring and, if required, make adjustments to the product or process. A new automatic method based on deep learning and optimization algorithms for nine control chart patterns (CCPs) recognition are proposed in this paper. This method has two principal parts: the classification part and the tuning part. In the last few years, a convolutional neural network (ConvNet) has led to an excellent performance on various tasks, like image processing, speech recognition, and signal processing. Therefore, in the classification part, ConvNet is used as the intelligent classifier for CCPs recognition. One significant difficulty of ConvNet is that it requires considerable proficiency to select suitable parameters like a number of kernels and their spatial sizes, learning rate, etc. The ConvNet parameters have domestic dependencies which make the tuning of these parameters a challenging task. According to these issues, in the tuning part of the proposed method, the Harris hawks optimization (HHO) algorithm is used for optimal tuning of ConvNet parameters. Contrasting the common CCPs recognition methods, the proposed method takes unprocessed data and passes to more than one hidden layer for extracting the optimal feature representation instead of relying on any feature engineering mechanisms. The quantitative and simulation results show the superiority of the proposed method over the previous techniques in terms of its performance.

Recognition of mixture control chart patterns based on fusion feature reduction and fireworks algorithm-optimized MSVM

Unnatural control chart patterns (CCPs) can be associated with the quality problems of the production process. It is quite critical to detect and identify these patterns effectively based on process data. Various machine learning techniques to CCPs recognition have been studied on the process only suffer from basic CCPs of unnatural patterns. Practical production process data may be the combination of two or more basic patterns simultaneously in reality. This paper proposes a mixture CCPs recognition method based on fusion feature reduction (FFR) and fireworks algorithm-optimized multiclass support vector machine (MSVM). FFR algorithm consists of three main sub-networks: statistical and shape features, features fusion and kernel principal component analysis feature dimensionality reduction, which make the features more effective. In MSVM classifier algorithm, the kernel function parameters play a very significant role in mixture CCPs recognition accuracy. Therefore, fireworks algorithm is proposed to select the two-dimensional parameters of the classifier. The results of the proposed algorithm are benchmarked with popular genetic algorithm and particle swarm optimization methods. Simulation results demonstrate that the proposed method can gain the higher recognition accuracy and significantly reduce the running time.

Control chart pattern recognition using RBF neural network with new training algorithm and practical features

The control chart patterns are the most commonly used statistical process control (SPC) tools to monitor process changes. When a control chart produces an out-of-control signal, this means that the process has been changed. In this study, a new method based on optimized radial basis function neural network (RBFNN) is proposed for control chart patterns (CCPs) recognition. The proposed method consists of four main modules: feature extraction, feature selection, classification and learning algorithm. In the feature extraction module, shape and statistical features are used. Recently, various shape and statistical features have been presented for the CCPs recognition. In the feature selection module, the association rules (AR) method has been employed to select the best set of the shape and statistical features. In the classifier section, RBFNN is used and finally, in RBFNN, learning algorithm has a high impact on the network performance. Therefore, a new learning algorithm based on the bees algorithm has been used in the learning module. Most studies have considered only six patterns: Normal, Cyclic, Increasing Trend, Decreasing Trend, Upward Shift and Downward Shift. Since three patterns namely Normal, Stratification, and Systematic are very similar to each other and distinguishing them is very difficult, in most studies Stratification and Systematic have not been considered. Regarding to the continuous monitoring and control over the production process and the exact type detection of the problem encountered during the production process, eight patterns have been investigated in this study. The proposed method is tested on a dataset containing 1600 samples (200 samples from each pattern) and the results showed that the proposed method has a very good performance.

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.

Development of fitted line and fitted cosine curve for recognition and analysis of unnatural patterns in process control charts

Unnatural patterns in process control charts exhibit out-of-control conditions. Therefore, increase in sensitivities in control charts is mandatory to study these situations. Because of the existence of inevitable natural variations, real-time detection and analysis of the significant patterns is a problem, especially when sensitivity level of the process to unnatural patterns formation is high. In the previous studies, most researchers have applied neural networks techniques to monitor significant patterns. Although this approach is effective, but structures of networks are complex and their architectures are difficult. The current paper develops fitted line and fitted cosine curve of samples to recognize and analyze the unnatural patterns. This simpler solution is more efficient and consumes less feedback time. The proposed model alarms occurrence of single and concurrent patterns and estimates their corresponding parameters. These fitted line and curve facilitate recognition and analysis of significant patterns at different levels of sensitivity, while the presented models often face with patterns misclassification error when high level of sensitivity is desired for unnatural patterns discrimination. To implement the proposed model, S2 control chart has been selected as a case study. The accuracy and precision of the proposed tools are evaluated by simulated experiments.

Recognition performance of imputed control chart patterns using exponentially weighted moving average

Performance of control chart pattern recogniser (CCPR) is dependent on the quality of data. Furthermore, when data is partially missing, false alarms and misclassification rate are high. This paper studied CCPR with incomplete data and investigated effectiveness of the exponential smoothing in restoring the patterns aiming to increase the recognition accuracy. The results demonstrated that average overall recognition accuracy degrades from 99.57 (without missingness) to 76.33 in severe missingness. Classification errors in the incomplete random and trend patterns increased up to 38 and 44 times, respectively. Exponential smoothing with a constant of 0.9 is found to be an effective imputation technique. In 50% missingness, recognition accuracy of imputed dataset improved by 99.2% and 19.4% in stable and unstable patterns respectively. Type I error in trend and type II error in random and cyclic patterns were reduced significantly with EWMA imputation. Sensitivity tests proved pattern recognition using proposed imputation technique resulted in superior robustness performance. [Received 28 April 2016; Revised 4 November 2017; Accepted 26 March 2018]

Recognition performance of imputed control chart patterns using exponentially weighted moving average

Recognition performance of imputed control chart patterns using exponentially weighted moving average

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.

Decision support tool for diagnosing the source of variation

Identifying the source of unnatural variation (SOV) in manufacturing process is essential for quality control. The Shewhart control chart patterns (CCPs) are commonly used to monitor the SOV. However, a proper interpretation of CCPs associated to its SOV requires a high skill industrial practitioner. Lack of knowledge in process engineering will lead to erroneous corrective action. The objective of this study is to design the operating procedures of computerized decision support tool (DST) for process diagnosis. The DST is an embedded tool in CCPs recognition scheme. Design methodology involves analysis of relationship between geometrical features, manufacturing process and CCPs. The DST contents information about CCPs and its possible root cause error and description on SOV phenomenon such as process deterioration in tool bluntness, offsetting tool, loading error, and changes in materials hardness. The DST will be useful for an industrial practitioner in making effective troubleshooting.

Recognition of control chart patterns with incomplete samples

In quality control, automated recognition of statistical process control (SPC) chart patterns is an effective technique for monitoring unnatural variation (UV) in manufacturing process. In most studies, focus was given on complete patterns by assuming there is no constrain in the SPC samples. Nevertheless, there is in-practice case whereby the SPC samples cannot be captured properly due to measurement sensor error or human error. Thus, this research aims to design a recognition scheme for incomplete samples pattern that will be useful for an industrial application. The design methodology involves three phases: (i) simulation of UV and SPC chart patterns, (ii) design of pattern recognition scheme, and (iii) evaluation of performance recognition. It involves modelling of the simulated SPC samples in bivariate quality control, raw data input representation, and recognizer training and testing. The proposed technique indicates a high recognition accuracy (normal pattern = 99.5%, shift patterns = 97.5%). This research will provide a new perspective in SPC charting scheme when dealing with constraint in terms of incomplete samples, which is greatly useful for an industrial practitioner in finding the solution for corrective action.

Control Chart Pattern Classification Based on Optimized Intelligent Approach: A Comparative Study

Control Chart Pattern Classification Based on Optimized Intelligent Approach: A Comparative Study