A Load Identification Method of Ore Ball Milling Process Based on Grinding Sound Signal

Abstract

The invention discloses a load identification method of ore ball milling process based on grinding sound signal. Firstly, the collected original signal is preprocessed, including eliminating DC component and filtering, and then a geometric spectrum subtraction suppression based on autoregressive (AR) spectrum estimation is proposed. Noise signal to improve the signal-to-noise ratio; secondly, based on the Ensemble Empirical Mode Decomposition (EEMD) method, the grinding sound signal is decomposed into K intrinsic mode components IMF, and the modal components with high reliability are selected by permutation entropy to reproduce the grinding sound signal. Then calculate the box fractal dimension of the reconstructed signal under each load type, and use it as the basis for the final load classification; finally, establish a load identification model based on fuzzy C-means clustering Bagging and extreme learning machine to realize load identification. The invention identifies the actual production load of a domestic polymetallic dressing plant, and the result shows that the identification model can accurately identify different mill load states, and has good generalization performance and identification accuracy.

Description

technical field [0001] The invention relates to the technical field of ore grinding process detection, in particular to a load identification method for ore ball milling process based on grinding sound signals. Background technique [0002] Mill load refers to the instantaneous total load in the mill, including new ore feed, cycle load, water volume and medium load, etc. The mechanism of the grinding process of the mill is relatively complex, with the characteristics of large inertia and long time lag, which makes the optimal working point of the mill load time-varying. However, the internal state of the mill is a black box, and the mechanism is complex, so it is difficult to establish an accurate model for this process. There are often three states in the internal load state of the mill: underload, normal, and overload. When the mill is in the underload state, the efficiency of the mill cannot be fully utilized, and when the mill is in the overload state, there will be "sw...

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Problems solved by technology

There are also certain grinding sound analysis products on the market, but due to the different application scenarios of each dressing plant, the results are not satisfactory. Foreign products have disadvantages such as expensive prices and inconvenient maintenance, while it is difficult to provide effective and accurate monitoring products in China.

At this stage, most manufacturers still rely on experienced field experts to judge the load status of the mill by "listening". This method has strong real-time performance and can detect the load of the mill without damage, but artificial listening is highly subjective

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