Control chart pattern recognition using the convolutional neural network

Zan, Tao; Liu, Zhihao; Wang, Hui; Wang, Min; Gao, Xiangsheng

doi:10.1007/s10845-019-01473-0

Cited by 72 publications

(39 citation statements)

References 38 publications

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“…In order to further verify the effectiveness of the proposed method in HPR tasks, we compare multilayer Bi-LSTM with traditional machine learning methods (MLP) and several deep learning methods (DBN and 1D-CNN). These methods are described in detail as follows: [18]. The input of 1D-CNN is the frequency of 25 intervals of histogram.…”

Section: Comparison Of Hpr Results Of Several Methodsmentioning

confidence: 99%

“…Its special gate structure enables it to capture both short-term dependencies and long-term dependencies. This paper is an extension of previous studies [18]. Different from previous studies, multilayer Bi-LSTM is used to replace the former one-dimensional CNN (1D-CNN), because it is specially used to process one-dimensional data such as time series, and has a stronger ability to process the relationship between before and after the sequence.…”

Section: Introductionmentioning

confidence: 99%

“…They are mainly based on statistical tests and heuristic algorithms [17]. Because of the shortcomings of these judgment rules, early expert systems cannot get satisfactory results [18]. This stimulates interest in developing more accurate CCPR algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…On the basis of relevant research in recent years [18,46,47], this paper proposes to use multilayer bidirectional long short-term memory networks (Bi-LSTM) to learn the features of histogram and control chart, and finally realize HPR and CCPR. The Bi-LSTM is an improved method of RNN.…”

Section: Introductionmentioning

confidence: 99%

“…In order to detect anomalies in production as quickly as possible, the length of the data we use for pattern recognition is 25. This is because longer data may mean that more defective products have been produced in the factory when abnormalities are identified [18].…”

Section: Introductionmentioning

confidence: 99%

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Statistical Process Control with Intelligence Based on the Deep Learning Model

Zan

Liu

et al. 2019

Applied Sciences

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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.

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Section: Comparison Of Hpr Results Of Several Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical Process Control with Intelligence Based on the Deep Learning Model

Zan

Liu

et al. 2019

Applied Sciences

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Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective

Tran¹,

Nadi

Nguyễn

et al. 2021

Springer Series in Reliability Engineering

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Over the past decades, control charts, one of the essential tools in Statistical Process Control (SPC), have been widely implemented in manufacturing industries as an effective approach for Anomaly Detection (AD). Thanks to the development of technologies like the Internet of Things and Artificial Intelligence (AI), Smart Manufacturing (SM) has become an important concept for expressing the end goal of digitization in manufacturing. However, SM requires a more automatic procedure with capabilities to deal with huge data from the continuous and simultaneous process. Hence, traditional control charts of SPC now find difficulties in reality activities including designing, pattern recognition, and interpreting stages. Machine Learning (ML) algorithms have emerged as powerful analytic tools and great assistance that can be integrating to control charts of SPC to solve these issues. Therefore, the purpose of this chapter is first to presents a survey on the applications of ML techniques in the stages of designing, pattern recognition, and interpreting of control charts respectively in SPC especially in the context of SM for AD. Second, difficulties and challenges in these areas are discussed. Third, perspectives of ML techniquesbased control charts for AD in SM are proposed. Finally, a case study of an ML-based control chart for bearing failure AD is also provided in this chapter.

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