## 5 Conclusions  
The novel diagnosis technology, based on the simultaneous processing of multiple spectral kurtoses (SK) and SK based optimal denoising Wiener filtering, is proposed, experimentally validated for early diagnosis of local damage of rolling element bearings and validated via simulation for diagnosis of a gearbox under undamaged and pitted conditions.  
Figure 14: The anomaly detection decision maps, obtained using k-means and k-NN approaches for gear signal.  
The main novelty of the proposed technology is division of bearing/gear vibration signals into specially defined short duration segments and simultaneous analysis/processing of SKs of all these segments for damage diagnosis. The SK is used to identify the frequency bands of the fault related impulses in short vibration segments. The approach tackles diagnostic challenges due to bearing slippage using cross-correlation technique to track impulsive responses, caused by rolling elements that are going over a damaged area.  
The experimental technology validation is performed on early diagnosis of a bearing inner race defect (1.2% relative damage size). The separation of histograms of the diagnostic features for fault-free and damaged cases is measured by the FC, which shows essential separation with FC values between 2.5 and 4.5. The proposed technology provides successful diagnosis results, with 96.2% estimate of the total probability of the correct diagnosis, 0.1% of false alarms and 7.7% of missed detections.  
The diagnostic effectiveness, of the proposed approach is shown to be superior to the wavelet diagnosis approach. While the wavelet approach achieved feature separation with a mean FC of 1.1, the proposed approach achieved feature separation with a mean FC of 3.4 and an effectiveness gain in range of [1.9, 4.6] in terms of the FC.  
In the case of gears, processing of the simulated data revealed very good separation between diagnostic features from undamaged and damaged conditions with the FC reaching up to 6 for some of the teeth. and 100% estimate of the total probability of the correct diagnosis.  
The use of SK for bearing/gearbox diagnosis via simultaneous processing of SKs of specially defined short vibration segments makes the technology efficient, adaptive, and easily implementable for on-line industrial applications. Alternatively, as shown in [22, 32, 33, 34, 35], the SK can be used as a pre-processing technique for wavelet-based diagnosis, thus, offering a possibility of a fault diagnosis via combining several techniques.  
A continuation of work towards an ablation study, which would further evaluate the method's effectiveness, would be focused on gears for which we are planning experimental testing with a very early stage of a damage. For bearings we do not see a need for reducing the bearing damage size given that the current data contains a very early stage of a bearing damage.  
Conceptualization, L.G.; methodology, L.G. and G.P.; software, G.P.; validation, L.G. and G.P.; formal analysis, L.G. and G.P.; investigation, L.G. and G.P.; writing--original draft preparation, G.P.; writing--review and editing, L.G.; visualization, G.P.; supervision, L.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to express special thanks to SKF for the support, [PERSON] for performing experimental trials, [PERSON] and [PERSON] for supplying gear simulated data and [PERSON] (the University of Huddersfield) for valuable comments and manuscript review.  
The authors declare no conflict of interest.