Fault Tolerant Control Based on an Observer on PI Servo Design for a High-Speed Automation Machine

Chommuangpuck, Prathan; Wanglomklang, Thanasak; Tantrairatn, Suradet; Srisertpol, Jiraphon

doi:10.3390/machines8020022

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…In this process, we achieve an overall weighted sum. Following this, we use equation (1) to calculate the weighted sum of 4,279 HDDs, each corresponding to a specific product, various weights. The results are presented in Table 1, where we showcase the outcomes.…”

Section: Fault Typesmentioning

confidence: 99%

“…Various data generated by machines across the production line are collected to bolster Industrial 4.0 technology. Consequently, the factory has integrated artificial intelligence technology to aid in data analysis, particularly in fault detection, diagnostic procedures, classification, isolation, and fault tolerance control [1][2][3][4]. This integration involves compiling essential information for decision-making, conducting visual inspections to ensure work quality, identifying and categorizing machine damage, and strategically planning the production process to optimize efficiency and maximize output.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing testing cell set efficiency: A machine learning approach on hard disk drive data

Rakcheep,

Laosakun,

Khaengkarn

et al. 2024

Manufacturing Rev.

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Hard Disk Drive (HDD) products undergo meticulous testing procedures to ensure their functionality prior to customer distribution. Nevertheless, anomalies can arise within the testing environment due to various factors, such as an increased number of media discs, leading to heightened current consumption by the spindle motor, and the frequent insertion and removal of HDDs during testing. These factors can induce malfunctions within the testing cell, which are identified by the tester's program. This study leverages diverse data measurements collected from tester HDDs within the testing cell to predict the status of the testing cell itself. Five distinct algorithms—Linear Discriminant Analysis (LDA), Ridge Classifier CV (RCCV), Extra-Tree Classifier (ETC), Random Forest Classifier (RFC), and Extreme Gradient Boosting (XGBoost)—were assessed. The research underscores that the proposed methodology, particularly utilizing XGBoost, achieves a notable prediction accuracy of 87.9% when applied to real datasets.

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Section: Fault Typesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing testing cell set efficiency: A machine learning approach on hard disk drive data

Rakcheep,

Laosakun,

Khaengkarn

et al. 2024

Manufacturing Rev.

Self Cite

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“…The results showed an accuracy of up to 93% using the triple parameters. In subsequent work, Prathan C. et al [18] used ANN to classify anomalies in a high-speed auto core adhesion mounting machine, followed by using a PI servo to compensate the machine back to an acceptable condition. They achieved an accuracy of 100% in the classification via the confusion matrix.…”

Section: Introductionmentioning

confidence: 99%

Classifying and Optimizing Spiral Seed Self-Servo Writer Parameters in Manufacturing Process Using Artificial Intelligence Techniques

Sapapporn,

Seangsri,

Srisertpol

2023

Systems

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This paper presents a technique for evaluating the performance of high-precision machines and classifying machine conditions in terms of test capability, such as hard disk drive (HDD) signal writing machines. In general, position errors generated during the signal writing process must be minimized to ensure high-quality writing. Position errors refer to deviations in the signal writing process and can be caused by several factors, such as deviations in the performance of the positioner that result in a position error signal exceeding its control limit. The proportion-al-integral-derivative (PID) controller must be optimized to minimize position errors. In model-based controller tuning, an accurate mathematical model is essential. The first step utilizes system identification methods, including adaptive weight least squares and peak detection, to create a partition resonance frequency model. This mathematical model is used to determine the open-loop stability, which involves achieving gain and phase margin at a specific crossover frequency, and the closed-loop dynamic response, which involves minimizing the discrete Fourier transform (DFT) of the position error signal. The DFT of the position error signal in each harmonic can be represented as a resonance peak in the transfer function model. The DFT and other combinations of operating parameters are analyzed and used as machine learning features. The ANN classifier was also effective in categorizing the performance of signal writing machines into four classes: 0 (healthy machine), 1 (sensor fault), 2 (loose pushpin), and 3 (tunable machine). The results showed that the classification performance was sufficient to separate class 1 and 2 for the maintenance process and class 3 for further optimization achieved using the mathematical model.

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“…The result demonstrated that CNN could be used to classify the fault condition by using image data [1]. High-Speed automation machines are widely used in HGA production process, therefore Fault Tolerant Control (FTC) utilizing PI servo with observer was used to increase the reliability of the machine control system [2]. Vibration signal analysis is a comprehensive method for machine condition monitoring.…”

Section: Introductionmentioning

confidence: 99%

Using fault detection and classification techniques for machine breakdown reduction of the HGA process caused by the slider loss defect

Wanglomklang

Chommaungpuck²,

Chamniprasart

et al. 2022

Manufacturing Rev.

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Fault Detection and Classification (FDC) based on Machine Learning (ML) approach was used to detect and classify mount head fault in the slider attachment process which causes the machine alarm 71 to occur which leads to 2% of machine downtime. This paper has focused on the use of classified pixel surface of mount head with fault difference conditions including Healthy, Fault I, Fault II, and Fault III to detect and diagnose mount head before a vacuum leak. The Artificial Neural Network (ANN) algorithm was a proposed classification model and has to be evaluated before using in the real processes. Three features of mount head surface pixel, i.e., inner, outer, and overall areas were investigated and used as model training data set. The experiment result indicates that the classification using the ANN model with three features performed with an accuracy of 94.3%. According to the result, it was found that the reliability of the production processes of FDC technique has increased as a result of the reduction of machine downtime by 1.886%.

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Fault Tolerant Control Based on an Observer on PI Servo Design for a High-Speed Automation Machine

Cited by 4 publications

References 14 publications

Enhancing testing cell set efficiency: A machine learning approach on hard disk drive data

Enhancing testing cell set efficiency: A machine learning approach on hard disk drive data

Classifying and Optimizing Spiral Seed Self-Servo Writer Parameters in Manufacturing Process Using Artificial Intelligence Techniques

Using fault detection and classification techniques for machine breakdown reduction of the HGA process caused by the slider loss defect

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