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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknik ITS IPTEK Journal of Proceedings Series Bulletin of Electrical Engineering and Informatics Rekayasa Mesin INOVTEK POLBENG Jurnal Pengabdian kepada Masyarakat
Anindita Adikaputri Vinaya
Universitas Internasional Semen Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Other

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Journal : Rekayasa Mesin

 1 
Identifikasi Sumber Bising Berdasarkan Sinyal Campuran dengan Algoritma Minimum Variance Distortionless Response Weighted (MVDRW) pada Mesin Kompresor Vinaya, Anindita Adikaputri; Aviva, Nurul Dwi; Prasetyo, Andhika Eko
Rekayasa Mesin Vol 10, No 1 (2019)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jrm.2019.010.01.5

Abstract

The vibration of the rotating engine can produce mixed sound and noise. The purpose of this study is to localize the noise sources by using mixed signals. The angular spectrum method with the Minimum Variance Distortionless Response Weighted (MVDRW) algorithm was used in this study. The mixed signal recording of a compressor engine with turbine drive was performed in this study. The mixed signal consists of 3 sources that produced from some parts of the compressor engine in the real plant. The experimental set results, at a distance of 60 cm, there are 3 noise sources that located at 44 °, 99 °, and 151 ° of the axis with different spatial positions 1 ° at source 1, 1 ° at source 2, and 1 ° at source 3 of the experimental set. Based on the results, the noise source on the compressor component is at source 2, the opposite side turbine.
MEL-FREQUENCY CEPSTRAL COEFFICIENTS (MFCC) FEATURE FOR PUMP ANOMALY DETECTION IN NOISY ENVIRONMENTS Vinaya, Anindita Adikaputri; Aciandra , Tiffani Febiola
Jurnal Rekayasa Mesin Vol. 15 No. 2 (2024)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v15i2.1815

Abstract

The continuity of a production process is supported by the availability of good assets. One of the efforts to support asset availability is through asset maintenance. One of the important assets in the industry is the pump. To detect anomalous conditions in the pump, the sound of the engine can be used. However, noisy environmental conditions can change the characteristics of the sound produced. This can have an impact on errors in identifying the condition of the machine. In this study, Mel Frequency Cepstral Coefficients (MFCC) is used, because the characteristics of MFCC are very attached to the sound signal and are appropriate for sound signals in the case of this noisy environment where the signal tends to be non-stationary. Support Vector Machine will be used as a method that maps input (machine features) and output (machine condition). In this study, a comparison of the use of combined features of time and frequency domains with time-frequency features (MFCC) will be carried out. Improved performance is obtained when the time-frequency domain acoustic feature in the form of MFCC is used with an average accuracy reaching 99.88% on the Medium Gaussian SVM model.
MEL-FREQUENCY CEPSTRAL COEFFICIENTS (MFCC) FEATURE FOR PUMP ANOMALY DETECTION IN NOISY ENVIRONMENTS Vinaya, Anindita Adikaputri; Aciandra , Tiffani Febiola
Jurnal Rekayasa Mesin Vol. 15 No. 2 (2024)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v15i2.1815

Abstract

The continuity of a production process is supported by the availability of good assets. One of the efforts to support asset availability is through asset maintenance. One of the important assets in the industry is the pump. To detect anomalous conditions in the pump, the sound of the engine can be used. However, noisy environmental conditions can change the characteristics of the sound produced. This can have an impact on errors in identifying the condition of the machine. In this study, Mel Frequency Cepstral Coefficients (MFCC) is used, because the characteristics of MFCC are very attached to the sound signal and are appropriate for sound signals in the case of this noisy environment where the signal tends to be non-stationary. Support Vector Machine will be used as a method that maps input (machine features) and output (machine condition). In this study, a comparison of the use of combined features of time and frequency domains with time-frequency features (MFCC) will be carried out. Improved performance is obtained when the time-frequency domain acoustic feature in the form of MFCC is used with an average accuracy reaching 99.88% on the Medium Gaussian SVM model.
Co-Authors Aciandra , Tiffani Febiola Adhitya Febriansyah Agus Masroeri Aulia Siti Aisjah Aurillia Iftitah Putriana Revanggi - Aviva, Nurul Dwi Ellisa Kusuma Dewi Mardhiyyah, Yunita Siti Nugrahani, Elita Fidiya Prasetyo, Andhika Eko Qurrotin A’yunina Maulida Okta Arifianti Sefri Yulianto Ummatin, Kuntum Khoiro