A innovative wavelet transformation method optimization in the noise-canceling application within intelligent building occupancy detection monitoring

Vaňuš, Jan; Kubíček, Jan; Vilímek, Dominik; Penhaker, Marek; Bilík, Petr

doi:10.1016/j.heliyon.2023.e16114

Cited by 2 publications

(1 citation statement)

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“…The application of economical TCM in industrial settings using acquired internal encoder spindle motor current signals poses challenges due to the presence of distorted machine tool control actions and operation cycles, including concealed information within the noise and/or undesired data in the machining process [13]. The noise in time series signals can be effectively reduced through the utilization of signal processing techniques, such as decomposition algorithms [14] or wavelet transformation methods [15]. These methods allow for the extraction of desired signal components by separating them from unwanted noise components.…”

Section: Introductionmentioning

confidence: 99%

Motor Current-Based Degradation Modeling for Tool Wear Hybrid Prognostics in Turning Process

Gavahian

Mechefske

2023

Machines

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For many machines with turning process systems, the application of economical indirect Tool Condition Monitoring (TCM) is enhanced by utilizing internal encoder spindle motor current signals. In this study, we proposed a novel approach to extract the total harmonic distortion (THD) feature associated with the metal cutting frequency of a specific working tool in the time domain. Our method entailed the application of filtered variational mode decomposition (VMD) combined with envelope analysis to demodulate the motor current signal and define TCM features based on the THD of odd harmonics, which are more related to the motor structure. These features serve as inputs for a hybrid prognostics technique, employing the Geometric Brownian Motion (GBM) to stochastically model the degradation process along with a deep learning transformer-based framework called the time series Transformer (TST) to improve the life prediction. Finally, to validate our approach, we conducted experiments based on 36 sets of tool run-to-wear data extracted from a CNC machine operating under turning process conditions using two different tools. Finally, we compared the degradation models based on the extracted odd-THD and even-THD features.

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Section: Introductionmentioning

confidence: 99%