Interference Suppression of Nonstationary Signals for Bearing Diagnosis Under Transient Noise Measurements

Abstract

In real-world applications, the diagnostic efficiency of rolling bearings is commonly affected by operating conditions like fluctuating rotating speed and varying loads, especially, environmental disturbances like transient noises. These disturbances tend to mask the indicators of damage, presenting substantial obstacles for accurately pinpointing failures. Traditional diagnostic methods struggle with the complexity and the noise sensitivity of such scenarios, often failing to accurately identify failure signs amidst multivariate random transient noise. To address these challenges, the current study proposes a method known as short-term Markov transition frequency peak rate. This method focuses on precisely tracking temporal state changes and identifying abnormal signals. It is aimed at mitigating transient noise interference at its source and enhancing insensitivity to external transient noise, which facilitates a more accurate and reliable selection of demodulation bands. Furthermore, an amplitude interference-limiting mechanism is designed within this method to discern and mitigate the impact of transient noise that may adversely affect the demodulation band selection process. The experimental results validate the effectiveness of this approach, demonstrating that it can reliably diagnose bearing faults even in the presence of transient disturbances.