Smart fault detection and optimal variables identification using Kernel Mahalanobis Distance for industrial manufacturing processes

Author:

Reséndiz-Flores Edgar O.1,Navarro-Acosta Jesús Alejandro2,García-Calvillo Irma D.2

Affiliation:

1. Division of Postgraduate Studies and Research, Tecnológico Nacional de México/IT de Saltillo. Saltillo, Mexico

2. Research Center on Applied Mathematics, Autonomous University of Coahuila, Saltillo, Mexico

Funder

non-anonymous version before any Notes or References

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Computer Science Applications,Mechanical Engineering,Aerospace Engineering

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Machine learning-supported manufacturing: a review and directions for future research;Production & Manufacturing Research;2024-03-15

2. The multisensor information fusion-based deep learning model for equipment health monitor integrating subject matter expert knowledge;Journal of Intelligent Manufacturing;2024-03-13

3. Transmission Equipment Identification Model Based on Deep Learning Technology;2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC);2023-12-29

4. Potential causes analysis of abnormal observations diagnosed by improved Mahalanobis-Taguchi system;Expert Systems with Applications;2023-11

5. The deep learning-based equipment health monitoring model adopting subject matter expert;International Journal of Computer Integrated Manufacturing;2023-09-15

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