PEN: Process Estimator neural Network for root cause analysis using graph convolution

Leonhardt, Viktor; Claus, Felix; Garth, Christoph

doi:10.1016/j.jmsy.2021.11.008

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…One such technique is the Relational Graph Convolutional Network (RGCN), which operates on realistic knowledge graphs, fusing both ML and semantics to improve anomaly detection together with the ability to identify root causes inside a stream of data accurately. In another study, a Graph Convolutional Neural Network (GCNN) is also utilized as a part of the proposed model termed Process Estimator Neural Network (PEN), which was developed to tackle the non-linear issue of the state-sparse model (Leonhardt et al, 2021). PEN is actually a NN that uses a single graph convolution layer followed by two fully connected layers and constitutes a novel RCA methodology targeting modern multistage assembly lines for increasing product quality and implementing zero-defect manufacturing.…”

Section: Resultsmentioning

confidence: 99%

A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing

Παπαγεωργίου

Theodosiou

Rapti

et al. 2022

Front. Manuf. Technol.

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The identification of defect causes plays a key role in smart manufacturing as it can reduce production risks, minimize the effects of unexpected downtimes, and optimize the production process. This paper implements a literature review protocol and reports the latest advances in Root Cause Analysis (RCA) toward Zero-Defect Manufacturing (ZDM). The most recent works are reported to demonstrate the use of machine learning methodologies for root cause analysis in the manufacturing domain. The popularity of these technologies is then summarized and presented in the form of visualizing graphs. This enables us to identify the most popular and prominent methods used in modern industry. Although artificial intelligence gains more and more attraction in smart manufacturing, machine learning methods for root cause analysis seem to be under-explored. The literature survey revealed that only limited reviews are available in the field of RCA towards zero-defect manufacturing using AI and machine learning; thus, it attempts to fill this gap. This work also presents a set of open challenges to determine future developments.

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Section: Resultsmentioning

confidence: 99%

A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing

Παπαγεωργίου

Theodosiou

Rapti

et al. 2022

Front. Manuf. Technol.

View full text Add to dashboard Cite

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“…Root cause analysis is also a challenge in manufacturing, specifically in multistage assembly lines. Leonhardt et al [142] proposed a GCNbased process estimator neural network (PEN), which solve the current limitation of linear approaches, that is the current solutions cannot process dense 3D point cloud data of the product. For energy fields, a larger number of photovoltaic (PV) systems have been added to electronic grids.…”

Section: 25mentioning

confidence: 99%

Graph Neural Networks in IoT: A Survey

Dong¹,

Tang²,

Wang³

et al. 2022

Preprint

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The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technologies, IoT devices including smart wearables, cameras, smartwatches, and autonomous vehicles can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph Neural Networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source code from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computing methodologies → Neural networks.

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Knowledge graph network-driven process reasoning for laser metal additive manufacturing based on relation mining

Xiong,

Xiao,

et al. 2024

Appl Intell

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PEN: Process Estimator neural Network for root cause analysis using graph convolution

Cited by 8 publications

References 23 publications

A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing

A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing

Graph Neural Networks in IoT: A Survey

Knowledge graph network-driven process reasoning for laser metal additive manufacturing based on relation mining

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