Research on the fault diagnosis method of drying roller based on VMD-RF

Abstract

The Variational Mode Decomposition (VMD) method is a signal processing technique commonly used for decomposing complex signals, especially for analyzing time-series signals (such as speech signals and vibration signals). It has been widely applied in signal diagnosis. A fault diagnosis method based on Variational Mode Decomposition (VMD) and Random Forest (RF) is proposed for the problem that early faults are difficult to diagnose when the drying roller is running with load. The method primarily applies VMD to decompose the raw vibration acceleration signal, breaking down the complex base frequency signal of the drying drum into a series of intrinsic mode functions in order to find the optimal frequency band decomposition. The decomposed modal signals are then synthesized to reconstruct the original signal. Finally, the method employs Random Forest, Decision Trees, and K-Nearest Neighbors for training. To eliminate random errors and uncertainties from the experiments, 20 repeated tests are conducted to establish a classification model. Vibration signals from three different models of drying drums were collected to further verify the generality and engineering application value of the VMD-RF-based fault diagnosis method. Experimental results show that the VMD-RF method has a higher overall recognition rate for early faults in rotary dryers, while also demonstrating good applicability to various models of rotary dryers.