Device Health Estimation by Combining Contextual Control Information with Sensor Data and Device Health Prognostics Utilizing Restricted Boltzmann Machine
The goal of this work is to bridge the gap between business decision-making and real-time factory data. Beyond real- time data collection, we aim to provide analysis capability to obtain insights from the data and converting the learnings into actionable recommendations.
For device health estimation, we focus on analyzing device health conditions and propose a data fusion method that com- bines sensor data with limited diagnostic signals with the de- vice’s operating context. We propose a segmentation algo- rithm that provides a temporal representation of the device’s operation context, which is combined with sensor data to fa- cilitate device health estimation. Sensor data is decomposed into features by time-domain and frequency-domain analy- sis. Principal component analysis is used to project the high- dimensional feature space into a low-dimensional space fol- lowed by a linear discriminant analysis to search the optimal separation among different device health conditions. Our in- dustrial experimental results show that by combining device operating context with sensor data, our proposed segmenta- tion and linear transformation approach can accurately iden- tify various device imbalance conditions even for limited sen- sor data which could not be used to diagnose imbalance on its own.
For device health prediction, we propose a restricted Boltz- mann machine based method to automatically generate fea- tures that can be used for remaining useful life prediction, which is performed by a random forest regression algorithm. The proposed method was validated through run-to-failure dataset of a machine tool spindle test-bed.
How to Cite
fault diagnosis, prognostics, failure prediction, restricted boltzmann machine
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.