Underdetermined blind source separation based condition monitoring

Vinaya, Anindita Adikaputri; Arifianto, Dhany

doi:10.1109/icsitech.2015.7407775

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…The multiway array decomposition [11] method has also been used to obtain estimation signals from 4 machines in pump machines [12]. In 2015, the same method was used to separate the signal on the pump engine where the convolution mixing process was used [13]. Based on the results of his research, the source signal can be reconstructed, but the problem of ambiguity and localization of the source signal has been negligible.…”

Section: Introductionmentioning

confidence: 99%

Detection of Pump Faults Based on Sound Signals Using Non-Negative Matrix Factorization

et al. 2020

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Maintenance is very closely related to the performance of the production process. An alternative method that can be used to determine the damage to the engine is from the analysis of the sound pattern produced. If the sound source is more than one, then there will be signal mixing, and it will be a challenge in detecting damage to the engine. In this study, mixed signals will be separated. Separation of mixed sound signals was done using non-negative matrix factorization (NMF) method. Overall this study is aimed at detecting unbalance, misalignment, and bearing faults at pumps with microphones as sensors. The pumps used in this study were three pumps, where each pump had different conditions (unbalance, misalignment, and bearing fault). All three pumps have 3000 rpm. In this study, the recording process was carried out for 5 s. In this study, we also compare the location of the instantaneous frequency in full spectrum and corresponding frequency in local spectrum, and the distance between the spectra via the log spectral distance from the baseline signal and the estimated signal. Based on the instantaneous frequency approach, no error was found because of the instantaneous frequency suitability of the unbalanced machine condition with the estimated signal in the mixing configuration of three sources with two sensors. From the log spectral instance (LSD) results, the smallest value was obtained the smallest value in estimation 2, which tends to approach the unbalance condition with the LSD value of 1.0889. The most significant relative error is the estimated misalignment signal with a value of 11.2. However, overall damage can still be identified based on the pattern formed and some statistical parameters.

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confidence: 99%

Detection of Pump Faults Based on Sound Signals Using Non-Negative Matrix Factorization

et al. 2020

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“…In that study, the approach used to estimate the mixing matrix is AJD [8]. In previous research, [9] we have succeeded in separating the mixed signal so it can be used to determine the condition of the engine by using AJD approach [9].…”

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Multi-way Array Decomposition on Acoustic Source Separation for Fault Diagnosis of a Motor-Pump System

Vinaya¹,

Arifianto²

2017

IJECE

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<span>In this study, we propose a multi-way array decomposition approach to solve the complexity of approximate joint diagonalization process for fault diagnosis of a motor-pump system. Sources used in this study came from drive end-motor, nondrive end-motor , drive end pump , and nondrive end pump. An approximate joint diagonalization is a common approach to resolving an underdetermined cases in blind source separation. However, it has quite heavy computation and requires more complexity. In this study, we use an acoustic emission to detect faults based on multi-way array decomposition approach. Based on the obtained results, the difference types of machinery fault such as misalignment and outer bearing fault can be detected by vibration spectrum and estimated acoustic spectrum. The performance of proposed method is evaluated using MSE and LSD. Based on the results of the separation, the estimated signal of the nondrive end pump is the closest to the baseline signal compared to other signals with LSD is 1.914 and MSE is 0.0707. The instantaneous frequency of the estimated source signal will also be compared with the vibration signal in frequency spectrum to test the effectiveness of the proposed method.</span>

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Sound Reconstruction of an Overdetermined Mixture in a Mini Plant: An Experimental Study

Vinaya

Arifianto

Sudianto

et al. 2018

IOP Conf. Ser.: Mater. Sci. Eng.

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Underdetermined blind source separation based condition monitoring

Cited by 4 publications

References 17 publications

Detection of Pump Faults Based on Sound Signals Using Non-Negative Matrix Factorization

Detection of Pump Faults Based on Sound Signals Using Non-Negative Matrix Factorization

Multi-way Array Decomposition on Acoustic Source Separation for Fault Diagnosis of a Motor-Pump System

Sound Reconstruction of an Overdetermined Mixture in a Mini Plant: An Experimental Study

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