Classification Identification of Acoustic Emission Signals from Underground Metal Mine Rock by ICIMF Classifier

Abstract

To overcome the drawback that fuzzy classifier was sensitive to noises and outliers, Mamdani fuzzy classifier based on improved chaos immune algorithm was developed, in which bilateral Gaussian membership function parameters were set as constraint conditions and the indexes of fuzzy classification effectiveness and number of correct samples of fuzzy classification as the subgoal of fitness function. Moreover, Iris database was used for simulation experiment, classification, and recognition of acoustic emission signals and interference signals from stope wall rock of underground metal mines. The results showed that Mamdani fuzzy classifier based on improved chaos immune algorithm could effectively improve the prediction accuracy of classification of data sets with noises and outliers and the classification accuracy of acoustic emission signal and interference signal from stope wall rock of underground metal mines was 90.00%. It was obvious that the improved chaos immune Mamdani fuzzy (ICIMF) classifier was useful for accurate diagnosis of acoustic emission signal and interference signal from stope wall rock of underground metal mines.

Investigation on Safety Dynamic Evolution Mechanism of a Distributed Low Carbon Manufacturing System with Large Time Delay

“…(1) Based on the safety theory [28][29][30], the safety level and carelessness level of the humanmachine safety system under a low carbon manufacturing process are denoted as x(τ) and y(τ), respectively, and also satisfy the following conditions:…”

Application of Data Mining in an Intelligent Early Warning System for Rock Bursts

“…However, during the monitoring processes, various signals coming from environmental noise or generally false signals, which are not useful for monitoring and prediction, may occur. To solve this problem, several studies have been conducted; for instance, a sort of alarm system based on the warning network was set up to detect the electromagnetic signals; the Mamdani fuzzy classifier based on the improved chaos immune algorithm and Iris database was developed for the classification and recognition of acoustic emission and interference signals [8,30]. Despite the tremendous efforts that have already been made, in view of the diversity and magnitude of monitoring data and signals, the issue of how to separate the various signals into useful signals and false signals based on the data mining and data warehouse technologies, which are in line with the current technological situation and industrial needs, needs further study.…”

“…Currently, the ARAMIS M/E microseismic monitoring system and the ARES-5/E earth-sound monitoring system developed in Poland are widely used around the world. In China, according to the characteristics of the strong destructivity, complexity and suddenness of rock bursts in Chinese coalmines, the monitoring methods of microseismic events, electromagnetic radiation, drilling chips and ground sound are carried out [5][6][7][8][9].…”