Work

Advanced gear fault diagnosis in non-stationary conditions with an improved CEEMDAN-wavelet denoising technique

Year: 2025

Type: article

Abstract: Ensuring reliable fault detection in rotating machinery under variable speed conditions is crucial for effective predictive maintenance. This study presents a refined hybrid approach for detecting gear defects by analyzing vibration signals in non-stationary conditions. The proposed method combines the strengths of Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and wavelet denoising to enhance defect identification. The methodology begins with ICEEMDAN, which decomposes the measured vibration signal into Intrinsic Mode Functions (IMFs), each representing distinct oscillatory components. A method based on the use of the kurtogram is proposed to identify the most relevant IMF containing the defect signature. To further enhance the signal-to-noise ratio (SNR), wavelet denoising is coupled with Principal Component Analysis (PCA). Subsequently, Order Tracking Analysis (OTA) is applied to the denoised IMF’s envelope, compensating for speed variations and enabling the extraction of a precise envelope order spectrum that highlights defect harmonics. Experimental validation demonstrates the effectiveness of the approach across various speed variations, including acceleration, deceleration, and combined regimes. The results confirm its superiority over conventional fault detection techniques, offering a robust and practical solution for real-world industrial applications. This method contributes to improve condition monitoring strategies, minimizing unplanned downtime and optimizing maintenance efforts.

Source: Advances in Mechanical Engineering

DOI: 10.1177/16878132251356546