Abstract
Multivariate time series classification has been broadly applied in diverse domains over the past few decades. However, before applying the classification algorithms, the vast majority of current studies extract hand-engineered features that are assumed to detect local patterns in the time series. Therefore, the efficiency and precision of these classification approaches are heavily dependent on the quality of variables defined by domain experts. Recent improvements in the deep learning domain offer opportunities to avoid such an intensive hand-crafted feature engineering which is particularly important for managing the processes based on time-series data obtained from various sensor networks. In our paper, we propose a framework to extract the features in an unsupervised (or self-supervised) manner using deep learning, particularly stacked LSTM Autoencoder Networks. The compressed representation of the time-series data obtained from LSTM Autoencoders are then provided to Deep Feedforward Neural Networks for classification. We apply the proposed framework on sensor time series data from the process industry to detect the quality of the semi-finished products and accordingly predict the next production process step. To validate the efficiency of the proposed approach, we used real-world data from the steel industry.
Recommended Citation
N. Mehdiyev et al., "Time Series Classification using Deep Learning for Process Planning: A Case from the Process Industry," Procedia Computer Science, vol. 114, pp. 242 - 249, Elsevier, Oct 2017.
The definitive version is available at https://doi.org/10.1016/j.procs.2017.09.066
Meeting Name
Complex Adaptive Systems conference with Theme: Engineering Cyber Physical Systems, CAS (2017: Oct. 30-Nov. 1, Chicago, IL)
Department(s)
Engineering Management and Systems Engineering
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Adaptive systems; Complex networks; Deep learning; Deep neural networks; Efficiency; Embedded systems; Feedforward neural networks; Learning systems; Sensor networks; Steelmaking; Surface defects; Classification algorithm; Classification approach; Feature engineerings; Multivariate time series classifications; Process industries; Semi-finished products; Steel surface; Time series classifications; Time series; Process Industry; Steel Surface Defect Detection
International Standard Serial Number (ISSN)
1877-0509
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2017 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Publication Date
01 Oct 2017
Comments
This research was funded in part by the German Federal Ministry of Education and Research under grant number 01IS14004A (project iPRODICT) and 01IS12050 (project PRODIGY).