## 1 Introduction  
General wear of bearings and gears often leads to unexpected failures, causing unscheduled maintenance interventions, downtime, and economic losses. In the literature, the vibration-based approaches for bearing and gear diagnosis have been widely studied and a variety of processing techniques is currently available [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. Gearbox diagnostic features measure the impulse responses, introduced to gearbox during operation, when pitted teeth come into meshing contact, or bearing rolling elements pass through the faulty races. Damaged components in contact will excite high-frequency resonances of the structure. Moreover, a series of impulse responses in the vibration signal are modulated by shaft or cage frequencies, which is the key fault signature to be recognized in fault identification and localization. One of the most commonly used approaches is the demodulation analysis (i.e., envelope analysis), where a vibration signal is band-pass filtered and demodulated [2; 3; 6; 7; 8; 9; 11]. The spectrum of the envelope is used to detect characteristic defect frequencies in the low-frequency range. Although useful, this approach presents some disadvantages due to the need for selection of the demodulation frequency band and poor performance under high noise levels [3]. To address the challenge of the demodulation frequency band selection, an adaptive SK technique has been proposed and applied to the diagnosis of bearings [2; 12; 13; 14; 15; 16; 17; 18; 19] and gears [19; 20; 21; 22]. As an extension to the SK approach, the fast kurtogram technique has been proposed to determine the optimal SK resolution parameter and generalize the SK approach to a wider class of non-stationary signals [13; 14; 23]. Furthermore, the SK technique has been used to design detection filters, such as an optimal denoising (Wiener) filters, to extract non-stationary component with the highest level of impulsiveness from the background stationary noise [24]. Despite improvements, the above techniques did not address varying nature of bearing and gear signals due to a fault progression, a bearing slippage, and varying transmission path. In these conditions, damage-related frequency bands, estimated by the SK, can become less effective resulting in a poor denoising and a non-effective fault diagnosis. Therefore, in this study, we propose a novel technology based on simultaneous processing of SKs and the optimal denoising (Wiener) filter to address these challenges in rolling element bearings and gearboxes. The main novelty of the technology is a division of bearing/gear vibration signals into specially defined short duration segments, and simultaneous processing of SKs of multiple segments for damage diagnosis. The short duration of segments is defined as a specific time duration corresponding to the slowest modulating frequency of a component. Segments angular alignment, using the cross correlation, allows tracking of impacts, produced by the same rolling element or tooth. Low speed fluctuations are compensated by applying the angular resampling procedure [24; 25]. The frequency bands, selected by the SK for filtering/demodulation, are adapted for each short duration vibration segment to ensure accurate estimation of frequency bands, filtering of the stationary noise, and extraction of--the SK-filtered signals. The SK-filtered signals are demodulated, and a squared envelope is used as the diagnostic feature. Another paper novelty is a novel diagnosis effectiveness comparison of the proposed technology and wavelet-based technology for diagnosis of local bearing damage.