## 4 Deconvolution
### Minimum Entropy Deconvolution
#### 4.1.2 Application  
A new target vector has been introduced to accommodate the random impulse characteristics of planetary bearing vibrations. By adopting the multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) method, the influence of roller slippage on the vibration signal is considered, enabling the detection of outer race, inner race, and roller faults in planetary bearings [131].  
To further enhance the accuracy of fault feature extraction, minimum entropy deconvolution (MED) can be utilized to improve resistance to noise. A model known as the MOMEDA method incorporates MED to enhance acoustic features in rolling bearing fault diagnosis. This method effectively extracts and enhances bearing fault features under complex background noise and unknown transmission paths [132]. Some researchers have also introduced an enhanced minimum entropy deconvolution method: minimum entropy deconvolution with adaptive filter parameters (EMED-AFP). By integrating nonlinear transformations and a sample entropy minimization strategy, this method adaptively determines optimal filter parameters and effectively extracts fault features [133]. In terms of parameter optimization, the whale optimization algorithm (WOA) has been used to optimize variational mode decomposition (VMD) parameters, combined with MOMEDA to enhance fault-related impulse signals [134]. The combination of other technologies has elevated the application of MED to new heights. Minimum entropy morphological deconvolution (MEMD) has been proposed for bearing fault diagnosis, integrating morphological filtering into minimum entropy deconvolution (MED) and determining optimal filtering results through the diagonal slice spectrum (DSS) of higher-order spectra [135]. By combining continuous vibration separation (CVS) with minimum entropy deconvolution (MED), the technical challenge of sun gear fault diagnosis in planetary gearboxes has been overcome, with the model particularly excelling at signal modulation effects and weak signal detection [136].  
Furthermore, coarse-to-fine minimum entropy deconvolution (CFMED) has been introduced for rotating machinery fault detection. Through pre-iteration and discarding strategies, this method effectively extracts repetitive impulse signals related to faults and is robust to filter sizes [137]. A method to improve the accuracy of rotating machinery fault diagnosis, by combining kernel local outlier factor (KLOF) and phase editing (PE) with minimum entropy deconvolution (MED), has been shown to effectively extract repetitive impulse signals caused by faults [138]. An improved minimum entropy deconvolution method has been proposed to enhance the performance of rolling bearing fault diagnosis. Utilizing the standard particle swarm optimization algorithm and generalized spherical coordinate transformation, this method effectively detects and enhances periodic impulses in rolling bearing fault signals [139]. Additionally, combining minimum entropy deconvolution (MED) with the spectral centroid indicator (SCI) has been shown to help with the identification of bearing states with insufficient lubrication at different rotational speeds [140]. Combining time-delayed feedback stochastic resonance with minimum entropy deconvolution enhances the detection of weak fault features in rolling bearings. Through simulation and experimental validation, this method has been shown to outperform traditional minimum entropy deconvolution and the fast kurtosis Gram method at weak fault detection [141].