Interpretable multi-task learning for product quality prediction with attention mechanism

Cheng Han Yeh, Yao Chung Fan, Wen-Chih Peng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In this paper, we investigate the problem of mining multivariate time series data generated from sensors mounted on manufacturing stations for early product quality prediction. In addition to accurate quality prediction, another crucial requirement for industrial production scenarios is model interpretability, i.e., to understand the significance of an individual time series with respect to the final quality. Aiming at the goals, this paper proposes a multi-task learning model with an encoder-decoder architecture augmented by the matrix factorization technique and the attention mechanism. Our model design brings two major advantages. First, by jointly considering the input multivariate time series reconstruction task and the quality prediction in a multi-task learning model, the performance of the quality prediction task is boosted. Second, by incorporating the matrix factorization technique, we enable the proposed model to pay/learn attentions on the component of the multivariate time series rather than on the time axis. With the attention on components, the correlation between a sensor reading and a final quality measure can be quantized to improve the model interpretability. Comprehensive performance evaluation on real data sets is conducted. The experimental results validate that strengths of the proposed model on quality prediction and model interpretability.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019
PublisherIEEE Computer Society
Pages1910-1921
Number of pages12
ISBN (Electronic)9781538674741
DOIs
StatePublished - 1 Apr 2019
Event35th IEEE International Conference on Data Engineering, ICDE 2019 - Macau, China
Duration: 8 Apr 201911 Apr 2019

Publication series

NameProceedings - International Conference on Data Engineering
Volume2019-April
ISSN (Print)1084-4627

Conference

Conference35th IEEE International Conference on Data Engineering, ICDE 2019
CountryChina
CityMacau
Period8/04/1911/04/19

Keywords

  • Attention mechanism
  • Deep learning
  • Early stage prediction
  • Encoder decoder
  • Multi task learning
  • Neural network
  • Quality prediction

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