Chatter Detection and Diagnosis in Hot Strip Mill Process With a Frequency-Based Chatter Index and Modified Independent Component Analysis

Jo, Ha-Nui; Park, Byeong Eon; Ji, Yumi; Kim, Dong-Kuk; Yang, Jeong Eun; Lee, In‐Beum

doi:10.1109/tii.2020.2978526

Cited by 17 publications

(5 citation statements)

References 26 publications

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“…Recent research efforts are aimed to extend frequency analysis by using new chatter indicators. Jo et al [253] proposed the sum of frequency components in a high-frequency band to later extract statistical features to identify chatter. Chang et al [143] searched the vibration frequencies under diverse cutting conditions and identified chatter occurrence without needing a threshold.…”

Section: Frequency Domain Analysismentioning

confidence: 99%

“…Fu et al [132], Chen et al [144] and Dun et al [185] employed it as a reference to show the advantages of their methods. Jo et al [253] suggested that the use of the modified independent component analysis (MICA) method outperforms PCA, while Liu et al [175] illustrated the contribution of PCA with different signal processing and classification methods.…”

Section: Feature Selectionmentioning

confidence: 99%

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Chatter detection in milling processes—a review on signal processing and condition classification

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

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Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing.

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Section: Frequency Domain Analysismentioning

confidence: 99%

Section: Feature Selectionmentioning

confidence: 99%

Chatter detection in milling processes—a review on signal processing and condition classification

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

View full text Add to dashboard Cite

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“…However, the traditional ICA faces the problem of unstable performance due to random initialization. Therefore, modified ICA is proposed to obtain a constant solution, which takes the PCs extracted by PCA as the initialized independent components [13,14].…”

Section: Introductionmentioning

confidence: 99%

A novel monitoring method based on multi-model information extraction and fusion

Li,

Shen,

Tian

et al. 2024

Meas. Sci. Technol.

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Modern industrial processes are increasingly complex, where multiple characteristics usually coexist in process data. Therefore, traditional monitoring methods based on a single model may ignore other data characteristics and obtain poor monitoring performance. Aiming at this problem, a novel monitoring method based on multi-model information extraction and fusion is proposed in this paper. Firstly, several methods are used to extract different characteristics from process data. For example, principal component analysis, independent component analysis and slow features analysis can be used to extract Gaussian, non-Gaussian and dynamic characteristics respectively. Secondly, features extracted from multiple models are combined into new potential features. Then, Lasso regression models between potential features and process variables are established. In this way, not only are multiple characteristics in process data considered during the reconstruction, but key potential features (KPFs) can be selected for each process variable. The KPFs for each process variable can form a monitoring subspace to enhance the sensitivity for fault detection. Furthermore, cluster analysis is used to reduce the redundancy of monitoring subspaces based on the similarity of each subspace. Process monitoring can be achieved by fusing the monitoring results of finally determined multiple subspaces and residual space. Case studies on three simulation processes and a real industrial process demonstrate the effectiveness and better performance.

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“…A combination of continuous wavelet transform and a deep convolutional generative adversarial network (DCGAN) was proposed for tackling uneven data distribution in rolling bearing fault diagnosis [8]. Modified independent component analysis (MICA) was used to construct a multivariate statistical process monitoring model for detecting and analyzing chatter in hot strip mill processes [9]. Furthermore, a data-driven key performance indicator (KPI) prediction and diagnosis scheme was developed [10], offering a simplified alternative to the standard partial least squares (PLS) method.…”

Section: Introductionmentioning

confidence: 99%

Root Cause Tracing Using Equipment Process Accuracy Evaluation for Looper in Hot Rolling

Jing,

Li,

Song

et al. 2024

Algorithms

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The concept of production stability in hot strip rolling encapsulates the ability of a production line to consistently maintain its output levels and uphold the quality of its products, thus embodying the steady and uninterrupted nature of the production yield. This scholarly paper focuses on the paramount looper equipment in the finishing rolling area, utilizing it as a case study to investigate approaches for identifying the origins of instabilities, specifically when faced with inadequate looper performance. Initially, the paper establishes the equipment process accuracy evaluation (EPAE) model for the looper, grounded in the precision of the looper’s operational process, to accurately depict the looper’s functioning state. Subsequently, it delves into the interplay between the EPAE metrics and overall production stability, advocating for the use of EPAE scores as direct indicators of production stability. The study further introduces a novel algorithm designed to trace the root causes of issues, categorizing them into material, equipment, and control factors, thereby facilitating on-site fault rectification. Finally, the practicality and effectiveness of this methodology are substantiated through its application on the 2250 hot rolling equipment production line. This paper provides a new approach for fault tracing in the hot rolling process.

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Chatter Detection and Diagnosis in Hot Strip Mill Process With a Frequency-Based Chatter Index and Modified Independent Component Analysis

Cited by 17 publications

References 26 publications

Chatter detection in milling processes—a review on signal processing and condition classification

Chatter detection in milling processes—a review on signal processing and condition classification

A novel monitoring method based on multi-model information extraction and fusion

Root Cause Tracing Using Equipment Process Accuracy Evaluation for Looper in Hot Rolling

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