Water Pump Mechanical Faults Display at Various Frequency Resolutions

Sumarno, Linggo; Tjendro, Tjendro; Widyastuti, Wiwien; Wihadi, Raden Benedictus Dwiseno

doi:10.12928/telkomnika.v12i1.1693

Cited by 3 publications

(9 citation statements)

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“…Impeller yang rusak akan menyebabkan berkurangnya aliran air dalam pipa. Kerusakan impeller dapat disebabkan oleh gesekan antara impeller dengan partikel-partikel pasir yang ada dalam air [6]. Gbr.…”

Section: A Bantalanunclassified

“…Katup kaki yang rusak (dalam hal ini bocor) akan menyebabkan bocornya air dalam pipa, yang akhirnya mengakibatkan tidak ada lagi air di dalam pipa. Kerusakan katup kaki dapat disebabkan oleh kualitas katup kaki yang buruk, sehingga mudah bocor dimakan usia [6]. Gbr.…”

Section: Katup Kakiunclassified

“…Hasil pengujian sistem pengenalan, pada berbagai resolusi frekuensi dan jumlah sampel/kelas, diperlihatkan pada Tabel I. Catatan yang pertama untuk Tabel I adalah, hasil yang diperoleh mengacu pada (6), dengan jumlah data uji adalah 100 runtun data getaran dari sumbu Y, yang terdiri atas masing-masing 25 runtun data pompa air yang kondisinya baik, dan yang mengalami kerusakan bantalan, impeller, dan katup kaki. Sejumlah 100 runtun data ini secara statistik sudah mencukupi.…”

Section: B Pengujian Sistem Pengenalanunclassified

“…Sinyal getaran pada pompa air dapat juga dianalisis pada ranah frekuensi, dengan berbagai resolusi frekuensi [5] [6]. Berdasarkan analisis tersebut, pada resolusi-resolusi tertentu, secara visual telihat secara jelas perbedaan antara sinyalsinyal yang mengindikasikan pompa air yang baik dan yang mengalami kerusakan bantalan, impeller, atau katup kaki.…”

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Pengenalan Kerusakan Mekanis Pompa Air Berbasis Sinyal Getaran pada Ranah Frekuensi

Sumarno¹,

Wihadi²,

Tjendro³

et al. 2015

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)

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Abstract-One way in recognizing mechanical faults of electrical machines is using the vibration signals. This paper studies the recognition of mechanical faults of electrical machines which have faulty bearing, faulty impeller, and faulty foot valve. Electrical machines studied in this paper are household water pumps. The recognition of mechanical faults is conducted using the vibration signals at the frequency domain at various frequency resolution. The recognition method used is template matching for pattern classes. Euclidean distance function is used in this template matching. Based on the results, the highest recognition rate of 100% is optimally obtained using the vibration signal at frequency resolution of 5,9 Hz, and the number of 5 samples/classes in the water pump database. The 5,9 Hz frequency resolution was obtained using a 256 point FFT, where the number of 128 FFT coefficients are used as the feature extraction.Intisari-Salah satu cara pengenalan kerusakan mekanis mesin listrik adalah dengan menggunakan sinyal getaran. Tulisan ini membahas pengenalan kerusakan mekanis mesin listrik yang mempunyai kerusakan bantalan, impeller, atau pun katup kaki. Mesin listrik yang dibahas dalam tulisan ini adalah pompa air skala rumah tangga. Pengenalan kerusakan mekanis dilakukan menggunakan sinyal getaran pada ranah frekuensi, pada berbagai resolusi frekuensi. Metode pengenalan yang digunakan adalah pencocokan template pada kelas-kelas pola. Fungsi jarak Euclidean digunakan dalam pencocokan template ini. Berdasarkan hasil pengujian, tingkat pengenalan tertinggi 100% secara optimal diperoleh menggunakan sinyal getaran pada resolusi frekuensi 5,9 Hz, dan sejumlah 5 sampel/kelas pada basis data pompa air. Resolusi frekuensi 5,9 Hz ini diperoleh menggunakan FFT 256 titik dengan sejumlah 128 koefisien FFT yang digunakan sebagai ekstraksi cirinya.Kata Kunci-pompa air, kerusakan mekanis, sinyal getaran, ranah frekuensi, resolusi frekuensi.

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Section: A Bantalanunclassified

Section: Katup Kakiunclassified

Section: B Pengujian Sistem Pengenalanunclassified

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Pengenalan Kerusakan Mekanis Pompa Air Berbasis Sinyal Getaran pada Ranah Frekuensi

Sumarno¹,

Wihadi²,

Tjendro³

et al. 2015

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)

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“…The acoustic signal can be used to analyze the condition of the machinery beside the vibration signal. The characteristics of each machine can be distinguished from the pattern formed [2][3][4]. Mixing of one signal with another signal is a common condition which can make destruction of the original signal.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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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Recurrent Neural Network (RNN) Based Bearing Fault Classification of Induction Motor Employed in Home Water Pump System

Purwahyudi

2018

J. Electr. Eng. Comput. Sci.

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In home appliances, the water pump is used to supply the water from a room to the other rooms. Defects of the waterpump are not distributing the water, inner stator winding short circuit, and bearing failure. In this paper, bearingfault detection of induction motor (IM) used in home water pump system is developed by using recurrent neuralnetwork (RNN) method. It is difficult to detect fault bearing of IM using a mathematical model. So that, a recurrentneural network (RNN) method is applied to solves this problem. These bearing faults classifications are based on IMstator current waveform. Bearing fault types are all normal (AN), front fault (FF), rear fault (RF), and all fault (AF).While, the detection process consist of three step. They are taking bearing fault data, features extraction, and RNNfault detection. The bearing fault data is taken from the stator currents of IM by using soundcard oscilloscopesoftware. Second step is features extraction process to obtain more bearing fault signs. In this step, stator currentsof IM is converted from time domain into frequency domain by using Fast Fourier Transform (FFT). Last stage isRNN model to clasify the bearing fault of IM. The effectiveness of proposed RNN method is clarified by using fourbearing fault types.

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Water Pump Mechanical Faults Display at Various Frequency Resolutions

Cited by 3 publications

References 7 publications

Pengenalan Kerusakan Mekanis Pompa Air Berbasis Sinyal Getaran pada Ranah Frekuensi

Pengenalan Kerusakan Mekanis Pompa Air Berbasis Sinyal Getaran pada Ranah Frekuensi

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

Recurrent Neural Network (RNN) Based Bearing Fault Classification of Induction Motor Employed in Home Water Pump System

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