Abstract
Strong background noise and complicated interfering signatures when implementing vibration-based monitoring make it difficult to extract the weak diagnostic features due to incipient faults in a multistage gearbox. This can be more challenging when multiple faults coexist. This paper proposes an effective approach to extract multi-fault features of a wind turbine gearbox based on an integration of minimum entropy deconvolution (MED) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA). By using simulated periodic transient signals with different noise to signal ratios (SNR), it evaluates the outstanding performance of MED in noise suppression and reveals the deficient in extract multiple impulses. On the other hand, MOMEDA can performs better in extracting multiple pulses but not robust to noise influences. To compromise the merits of them, therefore the diagnostic approach is formalized by extracting the multiple weak features with MOMEDA based on the MED denoised signals. Experimental verification based on vibrations from a wind turbine gearbox test bed shows that the approach allows successful identification of multiple faults occurring simultaneously on the shaft and bearing in the high speed transmission stage of the gearbox.
Highlights
Wind turbines are important in modern industrial electric power production
It is usually difficult to diagnose potential faults, especially when multiple faults exist under strong background noise, vibrations excited by several faults are combined with each other non-linearly and non-stationarily
In order to overcome the shortcomings of Minimum entropy deconvolution (MED) in the detection of rotating machinery faults, McDonald proposed in 2016 a position multi-pulse target recognition deconvolution algorithm with known positions for rotating machinery fault detection, which can identify continuous impulse pulses
Summary
Wind turbines are important in modern industrial electric power production. The health status of the gearbox directly affects the working condition of the wind turbine system. This feature is very suitable for noise reduction in rotating machinery shock failure diagnosis. In order to overcome the shortcomings of MED, McDonald et al [11] proposed a rotating machinery fault feature extraction method, referred to as Multipoint Optimal Minimum Entropy. The method uses a time target vector to define the position and weight of the pulse sequence obtained by deconvolution These targets are suitable for the feature extraction of a vibration source of a rotating machine that generates a shock pulse for every revolution. This method does not need an iterative algorithm to obtain the optimal filter. By setting different period intervals, MOMEDA is used as the filter to extract the components of multiple faults, which can effectively identify the fault characteristics of wind turbine gearboxes
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