Development and Verification of the Diagnostic Model of the Sieving Screen

Abstract

The minerals processing enterprises are widely using vibrating machines to separate different fractions of materials. Sieving efficiency is greatly dependent on particle trajectories, or orbit, of periodical motion over the sieving decks. A screening process is very dependable on design parameters such as the vibrator power, synchronisation of their drives, and oscillation frequency as well as the stiffness of supporting springs. Deterioration of supporting springs (stiffness reduction and cracks) due to cyclic loading and fatigue is difficult to determine by the visual inspection, static loading tests, or nondestructive testing techniques. Vibration monitoring systems of different vendors are analysed where vibration sensors usually installed on the bearings of vibrators are as well used for supporting springs diagnostics. However, strong cyclic components from the unbalanced exciters and stochastic disturbances from the input stream and vibrating pieces of the material make analysis a not trivial task. The considered vibrating screen is investigated on the 6-DOF (degree-of-freedom) dynamical model to reflect all linear and rotational components of spatial motion. Besides the main periodic motion, the model accounts for stochastic alpha-stable distributed impacts from the material. Instead, the Gaussian normal distribution is considered for the position of equivalent force application point. Supporting springs are represented by the bilinear stiffness characteristics. Specific features of vibration signals (angle of orbit inclination, natural frequency change, harmonics of natural frequency, and phase space plots) are analysed to recognise the weak nonlinear features of a system under conditions of small stiffness changes in springs. The extensive measurements are conducted on the industrial vibrating screen, and the dynamic model is verified by the measurement data. Recommendations are given on failure diagnostics of springs in the industrial vibrating screens.

Identification, Decomposition and Segmentation of Impulsive Vibration Signals with Deterministic Components—A Sieving Screen Case Study

“…The screen is driven by two electric motors and individual belt transmissions. Special spherical roller bearings are used on the shafts of unbalanced exciters, see [ 12 ] for more details.…”

“…Therefore, in fact, we have two mentioned phenomena: varying load (due to varying amount of ore to classify and unbalance of the shaft) and impulsive load considered to be impulsive background noise. As discussed in research works published already [ 11 , 12 ], for this class of machines the problem of local damage detection is challenging and may be defined as the detection of cyclic impulsive signal in presence of non-cyclic impulsive noise [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ].…”

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

“…The ore is divided into two products: pieces of small size (<40 mm) and large size (>40 mm) [ 3 ]. Fine-grained product is fed to ball mills for grinding.…”

“…Model-based diagnostics (MBD) is a widely used approach in the field of maintenance management of industrial machinery, where it is important to distinguish dynamics-related models [ 3 , 12 , 13 ] as well as signal models [ 14 , 15 , 16 , 17 , 18 , 19 ]. Krot et al described diagnostics of springs from the point of view of a dynamic model [ 3 ], and in this paper authors attempt an alternative approach using data-driven signal model. Naturally, it is crucial is to be able to use an appropriate model, that very often has to be prepared from scratch.…”

Vibration-Based Diagnostics of Radial Clearances and Bolts Loosening in the Bearing Supports of the Heavy-Duty Gearboxes

“…Some methods are accommodated to bearing condition monitoring under non-stationary working conditions [ 89 , 90 , 91 , 92 , 93 ] using wavelets, bicoherence, spectral kurtosis and covariance, and high-order spectrum techniques to detect non-linear features of the signals. Methods are developed for impulsive components extraction in the presence of non-Gaussian heavy-tailed noise due to stochastic impacts from mineral pieces [ 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 ] or transient vibration analysis in industrial rolling mills [ 102 , 103 ].…”