학술논문
Research on Invalid Detection Data Model of Mine Catalytic Sensors Based on Machine Learning
Document Type
Periodical
Author
Source
IEEE Sensors Journal IEEE Sensors J. Sensors Journal, IEEE. 23(3):1925-1932 Feb, 2023
Subject
Language
ISSN
1530-437X
1558-1748
2379-9153
1558-1748
2379-9153
Abstract
In order to solve the problem of nonlinear failure data output by the catalytic combustible sensor (later referred as a sensor) when working in the mine environment, this article proposes a backpropagation (BP) neural network nonlinear data filtering model based on the Levenberg–Marquardt (L-M) algorithm. The experimental analysis shows that this model has obvious advantages in training speed and error performance compared with the BP neural network model established by a quasi-Newton algorithm and adaptive linear regression (lr) momentum gradient descent algorithm. In terms of generalization ability, this model has better generalization ability than the radial basis function (RBF) feedforward neural network model with ${K}$ -means clustering. Based on the above advantages, the model can effectively filter the sensor failure output data, eliminate the hidden danger of safety production caused by the failure output data, and improve the level of safety production in coal mines.