Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing

Hsu, Chia‐Yu; Liu, Wei-Chen

doi:10.1007/s10845-020-01591-0

Cited by 93 publications

(28 citation statements)

References 36 publications

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“…Hsu and Liu (2020) proposed a Convolutional Neural Network (CNN) intelligent diagnosis algorithm, which could automatically extract the mechanical fault features and recognize the faults. The feasibility of this method was proved through experimental simulation [24]. Amirat et al (2020) put forward a method based on variable modal decomposition combined with the optimized SVM network for joint FD [25].…”

Section: Research Progress Of Fdmentioning

confidence: 99%

Automatic fault detection of sensors in leather cutting control system under GWO-SVM algorithm

Luo

Jiao

2021

PLoS ONE

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The purposes are to meet the individual needs of leather production, improve the efficiency of leather cutting, and increase the product’s competitiveness. According to the existing problems in current leather cutting systems, a Fault Diagnosis (FD) method combining Convolutional Neural Network (CNN) and the Support Vector Machine (SVM) of Gray Wolf Optimizer (GWO) is proposed. This method first converts the original signal into a scale spectrogram and then selects the pre-trained CNN model, AlexNet, to extract the signal scale spectrogram’s features. Next, the Principal Component Analysis (PCA) reduces the obtained feature’s dimensionality. Finally, the normalized data are input into GWO’s SVM classifier to diagnose the bearing’s faults. Results demonstrate that the proposed model has higher cutting accuracy than the latest fault detection models. After model optimization, when c is 25 and g is 0.2, the model accuracy can reach 99.24%, an increase of 66.96% compared with traditional fault detection models. The research results can provide ideas and practical references for improving leather cutting enterprises’ process flow.

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Section: Research Progress Of Fdmentioning

confidence: 99%

Automatic fault detection of sensors in leather cutting control system under GWO-SVM algorithm

Luo

Jiao

2021

PLoS ONE

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“…[32]. CNN is often used for fault detection in multivariate time-series data from multiple sensors due to its capability to learn key features with stacked convolutions and pooling layers [63]. Compared to existing studies in other manufacturing domains, there are few pieces of research on time-series analysis in the plastic injection molding domain.…”

Section: B Time-series Analysis In a Manufacturing Domainmentioning

confidence: 99%

A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process

Kim

Choi

et al. 2021

IEEE Access

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The authors of this work propose a deep learning-based fault detection model that can be implemented in the field of plastic injection molding. Compared to conventional approaches to fault detection in this domain, recent deep learning approaches prove useful for on-site problems involving complex underlying dynamics with a large number of variables. In addition, the advent of advanced sensors that generate data types in multiple modalities prompts the need for multimodal learning with deep neural networks to detect faults. This process is able to facilitate information from various modalities in an endto-end learning fashion. The proposed deep learning-based approach opts for an early fusion scheme, in which the low-level feature representations of modalities are combined. A case study involving real-world data, obtained from a car parts company and related to a car window side molding process, validates that the proposed model outperforms late fusion methods and conventional models in solving the problem. INDEX TERMS Machine learning, deep learning, multimodal learning, early fusion, industrial AI, plastic injection moldingThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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“…A manufacturing system includes several sub-functions [1,2], such as planning [3], scheduling [4][5][6][7], dispatching [8], machine maintenance [9,10], and quality control and inspection [11,12]. In particular, to facilitate the effective operation of a manufacturing process, automated predictive maintenance operations are preferentially performed via fault detection, diagnosis, and predictions based on sensor signals collected during process execution [9,10,[13][14][15]. For example, Lu et al [16] detected occurrences of bearing faults during operation under harsh conditions (i.e., low signal-to-noise ratio) by applying adaptive stochastic resonance.…”

Section: Introductionmentioning

confidence: 99%

System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems

Baek

2021

Int J Adv Manuf Technol

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As automation and digitalization are being increasingly implemented in industrial applications, manufacturing systems comprising several functions are becoming more complex. Consequently, fault analysis (e.g., fault detection, diagnosis, and prediction) has attracted increased research attention. Investigations involving fault analysis are usually performed using real-time, online, or automated techniques for fault detection or alarming. Conversely, recovery of faulty states to their healthy forms is usually performed manually under offline conditions. However, the development of intelligent systems requires that appropriate feedback be provided automatically, to facilitate faulty-state recovery without the need for manual operator intervention and/or decision-making. To this end, this paper proposes a system integration technique for predictive process adjustment that determines appropriate recovery actions and performs them automatically by analyzing relevant sensor signals pertaining to the current situation of a manufacturing unit via cloud computing and machine learning. The proposed system corresponds to an automated predictive process adjustment module of an automated storage and retrieval system (ASRS). The said integrated module collects and analyzes the temperature and vibration signals of a product transporter using an internet-of-things-based programmable logic controller and cloud computing to identify the current states of the ASRS system. Upon detection of faulty states, the control program identifies corresponding process control variables and controls them to recover the system to its previous no-fault state. The proposed system will facilitate automatic prognostics and health management in complex manufacturing systems by providing automatic fault diagnosis and predictive recovery feedback.

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Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing

Cited by 93 publications

References 36 publications

Automatic fault detection of sensors in leather cutting control system under GWO-SVM algorithm

Automatic fault detection of sensors in leather cutting control system under GWO-SVM algorithm

A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process

System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems

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