Gear fault detection based on instantaneous frequency estimation using variational mode decomposition and permutation entropy under real speed scenarios

Abstract

Structural compactness with multiple meshing points excites multiple vibrations in a planetary gearbox of a wind turbine operating at varying speeds. The nonstationary, multicomponent vibration signals result in complex modulations challenging the effectiveness of a signal processing technique‐based fault diagnosis method. This paper aims to detect gear tooth faults under varying speed conditions based on the instantaneous frequency (IF) estimate of the decomposed component. Vibration signals were decomposed using variational mode decomposition (VMD), which is capable of frequency demodulation by exhibiting mono‐oscillatory components, followed by the IF estimation to select the most sensitive decomposed mode. The selected sensitive mode was further analyzed to deduce envelope spectrums for the extraction of frequency components highlighting fault symptoms. Furthermore, permutation entropy (PE) was evaluated to characterize the presence of randomness due to fault severity and to validate the proposed signal processing approach. In this paper, the vibration signals simulating planet gear faults were analyzed using the proposed signal processing approach. Symptoms of planet tooth faults were analyzed using the experimental investigation under real‐time varying speed to validate and exhibit the effectiveness of the proposed fault diagnosis approach for a planetary gearbox.

A Review on Vibration-Based Fault Diagnosis Techniques for Wind Turbine Gearboxes Operating Under Nonstationary Conditions

“…Similar to EMD, variational mode decomposition (VMD) also decomposes a multicomponent signal into band-limited mode functions non-recursively [130]. These band-limited mono-components have been employed for planetary gearbox fault diagnosis using demodulation analysis [131,132]. VMD compensates the problem of mode mixing shown by both EEMD and EMD, finds quickly and accurately estimates the center frequency of each component in a multicomponent nonstationary signal using iterative frequency-domain non-recursion solution [133].…”

Analysis of Signals from Air Conditioner Compressors with Ordinal Patterns

“…The technique reveals its usefulness in the analysis of complex and non-linear systems. Sharma [14] uses Variational Mode Decomposition and Entropy Permutation for gear failure detection. This approach combines instantaneous frequency estimation with ordinal pattern analysis techniques, making it a powerful tool for identifying anomalies.…”

Evaluation of gear pitting severity by using various condition monitoring indicators

“…In the last decades were developed different gear fault diagnosis methods and condition monitoring techniques [4], [5], [6]. Fundamentally, vibration signals acquired from gearboxes by means of accelerometers, are filtered, amplified, processed and analysed in time domain [7], frequency domain [8], or time-frequency domain [9]. With the fast development of artificial intelligence technologies, classification of gear faults using machine learning became a hot topic in the field of gear fault diagnosis methods.…”