@ARTICLE{Bartmanski_Cezary_Feasibility_Online,
 author={Bartmanski, Cezary and Bramorska, Alicja},
 journal={Archives of Acoustics},
 howpublished={online},
 year={Online first},
 publisher={Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics},
 abstract={This paper presents the results of preliminary research aimed at developing a method for rapid, noncontact diagnostics of the electric drive of car seats. The method is based on the analysis of acoustic signals produced during the operation of the drive. Pattern recognition and machine learning processes were used in the diagnosis. A method of feature extraction (diagnostic symptoms) using wavelet decomposition of acoustic signals was developed. The discriminative properties of a set of diagnostic symptoms were tested using the “Classification Learner” application available in MATLAB. The obtained results confirmed the usefulness of the developed method for the technical diagnostics of car seats.},
 type={Article},
 title={Feasibility of Using Wavelet Analysis and Machine Learning Method in Technical Diagnosis of Car Seats},
 URL={http://journals.pan.pl/Content/132407/PDF/3951%20(3).pdf},
 doi={10.24425/aoa.2024.148809},
 keywords={acoustic diagnostics, wavelet decomposition, machine learning},
}