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. We focus on analyzing device health conditions and propose a data fusion method that combines sensor data with the devices operating context. We propose a segmentation algorithm that provides a temporal representation of the devices operation context, which is combined with sensor data to facilitate device health estimation. Sensor data is decomposed into features by time-domain and frequency-domain analysis. Principal component analysis (PCA) is used to project the high-dimensional feature space into a low-dimensional space followed by a linear discriminant analysis (LDA) to search the optimal separation among different device health conditions. Our industrial experimental results show that by combining device operating context with sensor data, our proposed segmentation and PCA-LDA approach can accurately identify various device imbalance conditions.
Honda, T.; Liao, L.; Eldardiry, H.; Saha, B.; Abreu, R.; Pavel, R.; Iverson, J. C. Device Health Estimation by Combining Contextual Control Information with Sensor Data. DX 2015 : The 26th International Workshop on Principles of Diagnosis.; Paris, France, CA USA. Date of Talk: 08/31/2015