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Identification, Decomposition and Segmentation of Impulsive Vibration Signals with Deterministic Components-A Sieving
Karolina Gąsior1, Hanna Urbańska1, Aleksandra Grzesiek2
1Faculty of Pure and Applied Mathematics, Hugo Steinhaus Center, Wrocław University of Science and Technology, Wyspiańskiego 27, 50-370 Wrocław, Poland.
Sensors (Basel, Switzerland)
|October 7, 2020
Summary
This study presents a novel vibration analysis method for raw material sieving screens. The technique effectively separates deterministic and random components, enabling advanced diagnostics for rolling element bearing damage.
Area of Science:
- Mechanical Engineering
- Signal Processing
- Condition Monitoring
Background:
- Industrial condition monitoring faces challenges due to complex machinery, variable operating conditions, and non-Gaussian noise.
- Vibration analysis is crucial for detecting damage in equipment like sieving screens used in raw material industries.
Purpose of the Study:
- To develop an automated signal processing procedure for vibration analysis of sieving screens.
- To identify and extract deterministic (shaft imbalance) and random (ore impact) vibration components.
- To facilitate classical diagnostic procedures for local damage detection in rolling element bearings.
Main Methods:
- A two-step signal processing method involving R/D (random/deterministic) decomposition.
- Iterative sine wave fitting using the least square method and signal filtering by subtraction.
- Impulse segmentation and statistical analysis of impulse characteristics, including envelope modeling and extrapolation.
Main Results:
- Successfully decomposed vibration signals into deterministic and random components.
- Identified and characterized random impulses, enabling regime-switching detection.
- Demonstrated the method's effectiveness using both simulated and real-world data.
Conclusions:
- The proposed vibration analysis procedure is effective for pre-processing signals from sieving screens.
- The method's ease of implementation and interpretation makes it suitable for commercial condition monitoring systems.
- Accurate identification of vibration components aids in early detection of rolling element bearing damage.
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