Michalak Anna, Wodecki Jacek, Drozda Michał, Wyłomańska Agnieszka, Zimroz Radosław
Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, Poland.
KGHM Polska Miedz SA, Oddzial Zaklady Wzbogacania Rud, Kopalniana 1, 59-101 Polkowice, Poland.
Sensors (Basel). 2020 Dec 31;21(1):213. doi: 10.3390/s21010213.
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 α-stable component as a model of excitations caused by falling rocks pieces, and identified machine response to unitary excitations.
工业机械诊断是一个与损伤检测需求相关的主题，但它也有助于理解过程本身。对机器运行过程的正确了解以及对基础部件的识别，对其诊断至关重要。在本文中，我们提出了一个信号模型，该模型描述了筛分筛的振动。这种特殊类型的筛分筛用于采矿业，按尺寸对物料流中的矿石块进行分类。该模型描述了在作为该机器悬架的弹簧组上测量的实际振动信号。通过这种方式，有望有助于更好地理解机器的整体运动如何影响诊断工作。对筛网上测量的实际振动信号进行分析，能够识别关键信号成分并对其进行参数化，这些成分在该机器诊断过程的后续阶段携带了有价值的信息。在所提出的模型中，我们考虑了与轴旋转相关的确定性成分、与外部噪声相关的随机高斯成分、作为落石块引起的激励模型的随机α稳定成分，以及识别出的机器对单一激励的响应。
