ScTCN-LightGBM:基于转置降维卷积的混合学习方法在工业材料载荷测量中的应用

IF 3.2 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zihua Chen, Runmei Zhang, Zhong Chen, Yu Zheng, Shunxiang Zhang
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引用次数: 0

摘要

基于深度学习的动态测量可以在许多工业领域得到广泛应用(如电力负荷和故障诊断采集)。在煤矿生产中,准确、连续的载荷测量是必不可少的。现有的载荷测量方法忽略了载荷和调节特征的符号特征,在载荷测量中存在一定的缺陷。为了解决这一问题,我们提出了一种混合学习方法(ScTCN-LightGBM)来有效地实现工业材料的载荷测量。首先,我们提供了一种异常数据处理方法来保证原始数据的准确性。其次,我们设计了一个侧面合成的时间卷积网络,该网络将一种新的转置降维卷积残差块与传统残差块相结合。该模块可以很好地提取符号特征和加载、调整特征值。最后,我们利用光梯度增强机来测量负载能力。实验结果表明,ScTCN-LightGBM的稳定性系数R2为0.923,优于现有的高指标测量模型。与传统加载测量方法相比,sctcn - lighthgbm的测量性能提高了40.2%,连续测量时间为11.28s。研究表明,该模型能够有效地实现工业物料的载荷测量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ScTCN-LightGBM: a hybrid learning method via transposed dimensionality-reduction convolution for loading measurement of industrial material
Dynamic measurement via deep learning can be applied in many industrial fields significantly (e.g. electrical power load and fault diagnosis acquisition). Nowadays, accurate and continuous loading measurement is essential in coal mine production. The existing methods are weak in loading measurement because they ignore the symbol characteristics of loading and adjusting features. To address the problem, we propose a hybrid learning method (called ScTCN-LightGBM) to realize the loading measurement of industrial material effectively. First, we provide an abnormal data processing method to guarantee raw data accuracy. Second, we design a sided-composited temporal convolutional network that combines a novel transposed dimensionality-reduction convolution residual block with the conventional residual block. This module can extract symbol characteristics and values of loading and adjusting features well. Finally, we utilize the light-gradient boosting machine to measure loading capacity. Experimental results show that the ScTCN-LightGBM outperforms existing measurement models with high metrics, especially the stability coefficient R2 is 0.923. Compared to the conventional loading measurement method, the measurement performance via ScTCN-LigthGBM improves by 40.2% and the continuous measurement time is 11.28s. This study indicates that the proposed model can achieve the loading measurement of industrial material effectively.
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来源期刊
Connection Science
Connection Science 工程技术-计算机:理论方法
CiteScore
6.50
自引率
39.60%
发文量
94
审稿时长
3 months
期刊介绍: Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.
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