A Cognitive Analytics based Approach for Machine Health Monitoring, Anomaly Detection, and Predictive Maintenance. Farbiz, F., Miaolong, Y., & Yu, Z. In 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), pages 1104–1109, November, 2020. ISSN: 2158-2297doi abstract bibtex Traditionally, there are two major limitations for machine learning (ML)-assisted manufacturing applications. First, it would require a tremendous amount of manual data annotations for ML models. Second, ML models are often learned offline and unable to capture the machine dynamism and adapt to changes over the time. In this paper, we propose a framework based on the concept of cognitive analytics with unsupervised learning for machine health monitoring, anomaly detection and predictive maintenance. The experimental results on an industrial robot demonstrates the effectiveness of the proposed framework in the identified use case.
@inproceedings{farbiz_cognitive_2020,
title = {A {Cognitive} {Analytics} based {Approach} for {Machine} {Health} {Monitoring}, {Anomaly} {Detection}, and {Predictive} {Maintenance}},
doi = {10.1109/ICIEA48937.2020.9248409},
abstract = {Traditionally, there are two major limitations for machine learning (ML)-assisted manufacturing applications. First, it would require a tremendous amount of manual data annotations for ML models. Second, ML models are often learned offline and unable to capture the machine dynamism and adapt to changes over the time. In this paper, we propose a framework based on the concept of cognitive analytics with unsupervised learning for machine health monitoring, anomaly detection and predictive maintenance. The experimental results on an industrial robot demonstrates the effectiveness of the proposed framework in the identified use case.},
booktitle = {2020 15th {IEEE} {Conference} on {Industrial} {Electronics} and {Applications} ({ICIEA})},
author = {Farbiz, Farzam and Miaolong, Yuan and Yu, Zhou},
month = nov,
year = {2020},
note = {ISSN: 2158-2297},
keywords = {Adaptation models, Anomaly detection, Machine learning, Manufacturing, Monitoring, Predictive maintenance, Service robots, Unsupervised learning, anomaly detection, cognitive analytics, ecml, machine health monitoring, predictive maintenance},
pages = {1104--1109},
}
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