DETECTION OF PUMP FAULTS BASED ON SOUND SIGNALS USING NON-NEGATIVE MATRIX FACTORIZATION

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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Machinery signal separation using non-negative matrix factorization with real mixing

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v9i4.1956 ◽

2020 ◽

Vol 9 (4) ◽

pp. 1468-1476

Author(s):

Anindita Adikaputri Vinaya ◽

Sefri Yulianto ◽

Qurrotin A’yunina Maulida Okta Arifianti ◽

Dhany Arifianto ◽

Aulia Siti Aisjah

Keyword(s):

Fundamental Frequency ◽

Matrix Factorization ◽

Performance Testing ◽

Signal Separation ◽

Mixing Process ◽

Effective Solution ◽

Spectral Distance ◽

The Mean ◽

Bearing Damage ◽

Non Negative Matrix Factorization

A big challenge in detecting damage occurs when the sound of a machine mixes with the sound of another machine. This paper proposes the separation of mixed acoustic signals using Non-negative Matrix Factorization (NMF) method for fault diagnosis. The NMF method is an effective solution for finding hidden parameters when the number of observations obtained by the sensor is less than the number of sources. The real mixing process is done by placing two microphones in front of the machine. Two microphones will be used as sensors to capture a mixture of four machinery signals. Performance testing of signal separation is done by comparing baseline signals with estimated signals through the mean log spectral distance (LSD) and the mean square error (MSE). The smallest spectral distance between the estimated signal and the baseline signal is found in Ŝ2 with an average LSD of 1.26. The estimated signal Ŝ2 is the closest to the baseline signal with MSE of 1.15 x 10-2. The pattern of bearing damage in the male screw compressor can be identified from the spectrum of estimated signal through harmonic frequencies as in the estimated signal Ŝ3 which is seen at 11x fundamental frequency, 12x fundamental frequency, 15x fundamental frequency, and 16x fundamental frequency.

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