IMS Center Researchers Present at the ESREL2020 PSAM15 Conference
Abhijeet Ainapure and Shahin Siahpour, researchers from the IMS Center at the University of Cincinnati, presented at the recent ESREL2020 PSAM15 Conference, held virtually on November 1-5, 2020. This presentation was based on their paper titled A Deep Learning Framework For Health Anomaly Detection of Multi-component Systems in Evolving Environments: A Case Study in PHM, co-authored with their colleagues Dr. Xiang Li and Professor Jay Lee. In this paper, the authors present a deep learning-based framework that seeks to deliver anomaly detection in multi-component systems. In this framework, convolutional neural network and long short-term memory models are adopted and improved upon to capture the relationship between different components and ultimately determine the health condition. This work is highly relevant for industries in which complex, multi-component assets are monitored.
The full abstract for this paper can be read below.
Abstract
Deep learning has been emerging as a highly effective method for data-driven prognostic and health management (PHM) studies in the past years. Many machinery condition monitoring tasks have been largely benefited from the development of deep neural network-based algorithms, such as fault diagnosis, remaining useful life prediction and so forth. While successful applications can be found in the current literature with respect to the conventional health monitoring cases with a single system component, limited attention has been paid on the complex machinery systems with multiple components. In this paper, a deep learning-based framework is proposed for the health anomaly detection problem on multi-component systems. The convolutional neural network and long short-term memory models are adopted and improved for the specific PHM scenario. The relationship between different components is implicitly captured and leveraged by the deep neural networks for identifying the health conditions. Experiments on the multi-component condition monitoring dataset validate the effectiveness of the proposed method. The proposed framework can be also easily extended for additional deep learning models, and that is well suited for the multicomponent system PHM tasks.
For more information about the ESREL2020 PSAM15 Conference, please visit the event website here.
