Open Access

ARTICLE

Accurate Multi-Scale Feature Fusion CNN for Time Series Classification in Smart Factory

Xiaorui Shao¹, Chang Soo Kim^{1, *}, Dae Geun Kim²

1 Department of Information Systems, Pukyong National University, Busan, 608737, South Korea.
2 Korea Dyeing & Finishing Technology Institute, Busan, 608737, South Korea.

* Corresponding Author: Chang Soo Kim. Email: .

Computers, Materials & Continua 2020, 65(1), 543-561. https://doi.org/10.32604/cmc.2020.011108

Received 20 April 2020; Accepted 13 May 2020; Issue published 23 July 2020

Abstract

Time series classification (TSC) has attracted various attention in the community of machine learning and data mining and has many successful applications such as fault detection and product identification in the process of building a smart factory. However, it is still challenging for the efficiency and accuracy of classification due to complexity, multi-dimension of time series. This paper presents a new approach for time series classification based on convolutional neural networks (CNN). The proposed method contains three parts: short-time gap feature extraction, multi-scale local feature learning, and global feature learning. In the process of short-time gap feature extraction, large kernel filters are employed to extract the features within the short-time gap from the raw time series. Then, a multi-scale feature extraction technique is applied in the process of multi-scale local feature learning to obtain detailed representations. The global convolution operation with giant stride is to obtain a robust and global feature representation. The comprehension features used for classifying are a fusion of short time gap feature representations, local multi-scale feature representations, and global feature representations. To test the efficiency of the proposed method named multi-scale feature fusion convolutional neural networks (MSFFCNN), we designed, trained MSFFCNN on some public sensors, device, and simulated control time series data sets. The comparative studies indicate our proposed MSFFCNN outperforms other alternatives, and we also provided a detailed analysis of the proposed MSFFCNN.

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Citations