Abstract
Tacholess order tracking techniques based on time-frequency (TF) ridge detection have been extensively used in bearing fault diagnosis under varying speed conditions for decades. However, the signal components of a fault bearing related to shaft rotational frequency (SRF) is difficult to be accurately extracted by these methods because of TF resolution limitation and strong noise interference. A new TF decomposing algorithm, that is, variational nonlinear chirp mode decomposition (VNCMD) is effective to extract the time-varying feature under limited TF resolution. However, its performance is influenced by prior knowledge of initial parameters. Besides, ridge information hidden in noise is difficult to be mined effectively, which increases the difficulty of ridge extraction. In this study, a feature isolation technology is proposed to enhance fault-related features and reduce the interference of noise and irrelevant components. Then inspired by the decomposing properties research on the convergence characteristics of VNCMD, an optimization tendency guiding mode decomposition (OTGMD) method is proposed to track the instantaneous frequency (IF) of fault-related mode, which can alleviate the personnel experience requirement and is not affected by the set of TF resolution. The proposed method mainly consists of three steps. First, SRF-related information is highlighted through low-pass filtering, and the dominant IF is achieved through ridge detection method. Subsequently, for the convenience of mode extraction, the fault characteristic is augmented through iterative envelope analysis. Then, the OTGMD optimization strategy is developed to gradually decompose the target mode on the basis of the above process. Finally, a stopping criterion based on characteristic frequency ratios (CFRs) is constructed to adaptively terminate the iteration process. Simulation and experiments demonstrate that the proposed method is effective and suitable for bearing fault diagnosis under varying speed conditions.
Highlights
Rolling bearings are extensively used components in rotating machines, including wind turbines and electric motors [1]–[4]
To ameliorate the disadvantages of traditional methods designed for constant speeds when they are used in fault detection under varying speed conditions, the order analysis is performed to eliminate the non-stationary effects of speed fluctuation by resampling method
The characteristic frequency ratios (CFRs) of different bearing faults are characterized as the ratio of the corresponding fault characteristic frequency (FCF) to shaft rotational frequency (SRF), which are constant and only determined by the geometric parameters
Summary
Rolling bearings are extensively used components in rotating machines, including wind turbines and electric motors [1]–[4]. Many vibration-based techniques, such as timedomain methods [8], [9], frequency-domain methods [10], [11], deep neural networks [12], and time-frequency mode decomposition [13], [14], have been constructed to detect bearing faults for the accurate extraction of FCF Most of these methods only show their effectiveness for fault diagnosis under constant speed conditions. It can efficiently eliminate the interference of irrelevant components and demodulate the impulse response to the low-frequency band [5], [29]. The initial location of the highest amplitude of the TFR can be artificially specified according to actual applications
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