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A Proposal of Sensor-based Time Series Classification Model using Explainable Convolutional Neural Network

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(5), pp.55-67
  • DOI : 10.9708/jksci.2022.27.05.055
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : March 16, 2022
  • Accepted : April 28, 2022
  • Published : May 31, 2022

Youngjun Jang 1 KimJiho 1 LEE, Hong Chul 1

1고려대학교

Accredited

ABSTRACT

Sensor data can provide fault diagnosis for equipment. However, the cause analysis for fault results of equipment is not often provided. In this study, we propose an explainable convolutional neural network framework for the sensor-based time series classification model. We used sensor-based time series dataset, acquired from vehicles equipped with sensors, and the Wafer dataset, acquired from manufacturing process. Moreover, we used Cycle Signal dataset, acquired from real world mechanical equipment, and for Data augmentation methods, scaling and jittering were used to train our deep learning models. In addition, our proposed classification models are convolutional neural network based models, FCN, 1D-CNN, and ResNet, to compare evaluations for each model. Our experimental results show that the ResNet provides promising results in the context of time series classification with accuracy and F1 Score reaching 95%, improved by 3% compared to the previous study. Furthermore, we propose XAI methods, Class Activation Map and Layer Visualization, to interpret the experiment result. XAI methods can visualize the time series interval that shows important factors for sensor data classification.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.