Variational mode decomposition for rotating machinery condition monitoring using vibration signals

Abstract

The failure of rotating machinery applications has major time and cost effects on the industry. Condition monitoring helps to ensure safe operation and also avoids losses. The signal processing method is essential for ensuring both the efficiency and accuracy of the monitoring process. Variational mode decomposition (VMD) is a signal processing method which decomposes a non-stationary signal into sets of variational mode functions (VMFs) adaptively and non-recursively. The VMD method offers improved performance for the condition monitoring of rotating machinery applications. However, determining an accurate number of modes for the VMD method is still considered an open research problem. Therefore, a selection method for determining the number of modes for VMD is proposed by taking advantage of the similarities in concept between the original signal and VMF. Simulated signal and online gearbox vibration signals have been used to validate the performance of the proposed method. The statistical parameters of the signals are extracted from the original signals, VMFs and intrinsic mode functions (IMFs) and have been fed into machine learning algorithms to validate the performance of the VMD method. The results show that the features extracted from VMD are both superior and accurate for the monitoring of rotating machinery. Hence the proposed method offers a new approach for the condition monitoring of rotating machinery applications.