On-line concurrent control chart pattern recognition using singular spectrum analysis and random forest

Chiu, Jing-Er; Tsai, Cheng-Han

doi:10.1016/j.cie.2021.107538

Cited by 22 publications

(4 citation statements)

References 39 publications

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“…Hu et al 23 identify the new activity of sensors through the bifurcated class incremental random forest method. Chiu and Tsai 24 propose a combined singular spectrum analysis and random forest method to establish an online detection system to achieve control chart pattern recognition. There are other approaches to pattern recognition.…”

Section: Related Previous Workmentioning

confidence: 99%

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

Wang,

Zhang

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part B:

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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.

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Section: Related Previous Workmentioning

confidence: 99%

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

Wang,

Zhang

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Additionally, SSPC incorporates predictive analytics skills provided by ML procedures, anticipating potential issues before they manifest and thereby saving time, preserving profits, and ensuring better overall performance [54]. Another distinguishing feature of SSPC is its proficiency in pattern recognition within production data, foreseeing unexpected trends and identifying erratic or compromising behavior [55]. Moreover, SSPC embraces adaptive control abilities, dynamically adjusting process parameters based on real-time data analysis.…”

Section: Application Example: Guidelines For An Sspcmentioning

confidence: 99%

Industry 4.0 and Smart Systems in Manufacturing: Guidelines for the Implementation of a Smart Statistical Process Control

Goecks,

Habekost,

Coruzzolo

et al. 2024

ASI

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Digital transformations in manufacturing systems confer advantages for enhancing competitiveness and ensuring the survival of companies by reducing operating costs, improving quality, and fostering innovation, falling within the overarching umbrella of Industry 4.0. This study aims to provide a framework for the integration of smart statistical digital systems into existing manufacturing control systems, exemplified with guidelines to transform an existent statistical process control into a smart statistical process control. Employing the design science research method, the research techniques include a literature review and interviews with experts who critically evaluated the proposed framework. The primary contribution lies in a set of general-purpose guidelines tailored to assist practitioners in manufacturing systems with the implementation of digital, smart technologies aligned with the principles of Industry 4.0. The resulting guidelines specifically target existing manufacturing plants seeking to adopt new technologies to maintain competitiveness. The main implication of the study is that practitioners can utilize the guidelines as a roadmap for the ongoing development and implementation of project management. Furthermore, the study paves the way for open innovation initiatives by breaking down the project into defined steps and encouraging individual or collective open contributions, which consolidates the practice of open innovation in manufacturing systems.

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“…Meanwhile, Yu et al [3] proposed a method for the identification of control chart anomalies based on a convolutional neural network (CNN) and a long-short-term memory (LSTM) network to recognize quality anomaly patterns in the manufacturing process. Chiu et al [4] proposed a hybrid technique that employs singular spectrum analysis and random forest to identify SPC process control charts and verify quality anomaly patterns. Wan et al [5] proposed a method for recognizing quality anomaly patterns by utilizing optimized random forest and multi-feature extraction, which addresses the limitation of conventional multi-source control charts in identifying the specific variables causing process anomalies.…”

Section: Introductionmentioning

confidence: 99%

Product Quality Anomaly Recognition and Diagnosis Based on DRSN-SVM-SHAP

Liu,

Wang,

Zhang

et al. 2024

Symmetry

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Conventional quality control methodologies are inadequate for fully elucidating the aberrant patterns of product quality. A multitude of factors influence product quality, yet the limited number of controlled quality characteristics is insufficient for accurately diagnosing quality abnormalities. Additionally, there are asymmetries in data collection, data pre-processing, and model interpretation. In this context, a quality anomaly recognition and diagnosis model for the complex product manufacturing process is constructed based on a deep residual network, support vector machine (SVM), and Shapley additive explanation (SHAP). Given the numerous complex product quality characteristic indexes and unpredictable accidental factors in the production process, it is necessary to mine the deep relationship between quality characteristic data and quality state. This mining is achieved by utilizing the strong feature extraction ability of the deep residual shrinkage network (DRSN) through self-learning. The symmetry of the data within the model has also been taken into account to ensure a more balanced and comprehensive analysis. The excellent binary classification ability of the support vector machine is combined with the DRSN to identify the quality anomaly state. The SHAP interpretable model is employed to diagnose the quality anomaly problem of a single product and to identify and diagnose quality anomalies in the manufacturing process of complex products. The effectiveness of the model is validated through case analysis. The accuracy of the DRSN-SVM quality anomaly recognition model reaches 99%, as demonstrated by example analysis, and the model exhibits faster convergence and significantly higher accuracy compared with the naive Bayesian model classification and support vector machine classification models.

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On-line concurrent control chart pattern recognition using singular spectrum analysis and random forest

Cited by 22 publications

References 39 publications

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

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

Industry 4.0 and Smart Systems in Manufacturing: Guidelines for the Implementation of a Smart Statistical Process Control

Product Quality Anomaly Recognition and Diagnosis Based on DRSN-SVM-SHAP

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