Detection of Component Degradation: A Study on Autoencoder-Based Approaches
2023 IEEE 19th International Conference on e-Science (e-Science)(2023)
Abstract
In the realm of predictive maintenance, the incorporation of artificial intelligence (AI) methods has revolutionized the field by empowering businesses to actively monitor and preemptively address equipment malfunctions. Detecting anomalies plays a crucial role in predictive maintenance as it serves as an early indicator of potential faults or failures. This paper introduces initial findings from the use of autoencoders and their associated vector reconstruction error within the context of the IMOCO4.E project.
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Key words
Predictive Maintenance,Anomaly Detection,Neural Networks
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