Open Access
ARTICLE
Accurate Multi-Scale Feature Fusion CNN for Time Series Classification in Smart Factory
Xiaorui Shao1, Chang Soo Kim1, *, 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.
Keywords
Cite This Article
X. Shao, C. Soo Kim and D. Geun Kim, "Accurate multi-scale feature fusion cnn for time series classification in smart factory,"
Computers, Materials & Continua, vol. 65, no.1, pp. 543–561, 2020.
Citations