Model of the Vibration Signal of the Vibrating Sieving Screen Suspension for Condition Monitoring Purposes

Diagnostics of industrial machinery is a topic related to the need for damage detection, but it also allows to understand the process itself. Proper knowledge about the operational process of the machine, as well as identification of the underlying components, is critical for its diagnostics. In this paper, we present a model of the signal, which describes vibrations of the sieving screen. This particular type is used in the mining industry for the classification of ore pieces in the material stream by size. The model describes the real vibration signal measured on the spring set being the suspension of this machine. This way, it is expected to help in better understanding how the overall motion of the machine can impact the efforts of diagnostics. The analysis of real vibration signals measured on the screen allowed to identify and parameterize the key signal components, which carry valuable information for the following stages of diagnostic process of that machine. In the proposed model we take into consideration deterministic components related to shaft rotation, stochastic Gaussian component related to external noise, stochastic alpha-stable component as a model of excitations caused by falling rocks pieces, and identified machine response to unitary excitations.

Automated summary

The paper presents a signal model focusing on describing vibrations of the sieving screen used in mining for ore classification. Analysis of real vibration signals measured on the screen identified and parameterized key signal components, providing valuable information for the diagnostic process of the machine. The model considers deterministic components related to shaft rotation, stochastic Gaussian component for external noise, stochastic alpha-stable component for excitations caused by falling rocks, and the machine response to unitary excitations.