Device Health Estimation by Combining Contextual Control Information with Sensor Data

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

Honda, T (Author) Other; Liao, L (Author) Other; Eldardiry, H (Author) Other; Saha, B (Author) Other; Rui Abreu (Author) FEUP; Pavel, R (Author) Other; Iverson, J (Author) Other

Conference proceedings International

Title: Proceedings of the 26th International Workshop on Principles of Diagnosis (DX-2015) co-located with 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (Safeprocess 2015), Paris, France, August 31 - September 3, 2015.

Pages: 209-216

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-00K-AQM

Abstract (EN): 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 limited diagnostic signals with the device's operating context. We propose a segmentation algorithm that provides a temporal representation of the device's 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 highdimensional 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 even for limited sensor data which could not be used to diagnose imbalance on its own.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7