CATS: Cluster-Aided Two-Step Approach for Anomaly Detection in Smart Manufacturing

Shetve, Dattaprasad; VaraPrasad, Raja; Trestian, Ramona; Nguyen, Huan X.; Venkataraman, Hrishikesh

doi:10.1007/978-981-33-6987-0_9

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…As synthetic data continues to gain prominence across various domains, it delivers significant advantages such as cost reduction, increased accessibility, and reinforced privacy protection, thereby underscoring its pivotal role in contemporary research paradigms. Its utilization is evident in the discovery of causal relationships in manufacturing processes (Marazopoulou et al, 2016), predictive maintenance training in digital twins (Mihai et al, 2021), automotive manufacturing applications (Luckow et al, 2018), anomaly detection (Shetve et al, 2021), and production scheduling optimization (Georgiadis et al, 2022). Its role in the realm of big data analytics for future smart factories has been highlighted (Gao et al, 2020), along with its potential for improving quality and efficiency in the casting industry (Sun et al, 2021).…”

Section: Multidimensional Importance Of Synthetic Datamentioning

confidence: 99%

Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utility

Buggineni,

Chen,

Camelio

2024

Front. Manuf. Technol.

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Addressing the challenges of data scarcity and privacy, synthetic data generation offers an innovative solution that advances manufacturing assembly operations and data analytics. Serving as a viable alternative, it enables manufacturers to leverage a broader and more diverse range of machine learning models by incorporating the creation of artificial data points for training and evaluation. Current methods lack generalizable framework for researchers to follow and solve these issues. The development of synthetic data sets, however, can make up for missing samples and enable researchers to understand existing issues within the manufacturing process and create data-driven tools for reducing manufacturing costs. This paper systematically reviews both discrete and continuous manufacturing process data types with their applicable synthetic generation techniques. The proposed framework entails four main stages: Data collection, pre-processing, synthetic data generation, and evaluation. To validate the framework’s efficacy, a case study leveraging synthetic data enabled an exploration of complex defect classification challenges in the packaging process. The results show enhanced prediction accuracy and provide a detailed comparative analysis of various synthetic data strategies. This paper concludes by highlighting our framework’s transformative potential for researchers, educators, and practitioners and provides scalable guidance to solve the data challenges in the current manufacturing sector.

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Section: Multidimensional Importance Of Synthetic Datamentioning

confidence: 99%

Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utility

Buggineni,

Chen,

Camelio

2024

Front. Manuf. Technol.

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“…In order to validate this, a two-step anomaly detection technique was developed by Shetve et al [180] and was evaluated for its data pre-processing capability.…”

Section: A a Look At The Tea Industry In Indiamentioning

confidence: 99%

Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects

Mihai

Yaqoob

Hưng

et al. 2022

IEEE Commun. Surv. Tutorials

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354

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Adaptive N-Step Technique for Real-Time Anomaly Detection in Smart Manufacturing

Shetve

Raju

Prasad

et al. 2022

2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS)

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CATS: Cluster-Aided Two-Step Approach for Anomaly Detection in Smart Manufacturing

Cited by 3 publications

References 10 publications

Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utility

Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utility

Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects

Adaptive N-Step Technique for Real-Time Anomaly Detection in Smart Manufacturing

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