ScTCN-LightGBM: a hybrid learning method via transposed dimensionality-reduction convolution for loading measurement of industrial material

Abstract

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.

KEYWORDS:

• Loading measurement of industrial material
• transposed dimensionality-reduction convolution
• gradient boosting machine
• hybrid learning model

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Anhui Simulation Design and Modern Manufacturing Engineering Technology Research Center Open Subject [Grant Number SGCZXZD2101]; the Provincial Natural Science Research Projects in Anhui Universities (A study of life detection methods for two-dimensional optimization of MIMO array signals in space and time) [Grant Number KJ2021JD22].