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
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