Temperature Estimation and Vibration Monitoring for Induction Motors and the Potential Application in Electrical Submersible Motors

Liang, Xiaodong

doi:10.1109/cjece.2018.2875111

Cited by 15 publications

(6 citation statements)

References 83 publications

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“…Diagnosis yang benar dan deteksi dini kerusakan awal dapat digunakan untuk mengklasifikasikan kondisi motor atau pompa dalam keadaan baik atau rusak [4]. Predictive maintenance adalah tindakantindakan maintenance yang dilakukan pada tanggal yang ditetapkan berdasarkan prediksi hasil analisa dan evaluasi data operasi yang diambil untuk melakukan predictive maintenance itu dapat berupa getaran, suhu, vibrasi, flow rate dan lain lainnya [5].…”

Section: Pendahuluanunclassified

Model Alat Uji Kerusakan Pada Pompa Sentrifugal Menggunakan Sensor Accelerometer Untuk Predictive Maintenance

Syarief

2023

JITET

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Pompa sentrifugal merupakan salah satu jenis pompa yang digunakan dalam berbagai industri, seperti industri pengolahan air. Pengoperasian pompa yang dilakukan secara terus-menerus memungkinkan terjadinya kerusakan, salah satunya misalignment, sebagai akibat dari adanya vibrasi atau getaran yang dialami oleh pompa. Adapun sistem pengukuran pada penelitian ini menggunakan sensor Accelerometer ADXL345 untuk mendeteksi getaran dengan metode FFT (Fast fourier transform) dalam memperoleh data spektrum vibrasi. Hasil dari pengukuran pada kondisi misalignment dengan nilai frekuensi 15Hz diperoleh rata-rata getaran sebesar 0.433601mm/s yang mana berdasarkan ISO-10816-3 kondisi tersebut masih tergolong baik, saat frekuensi 20Hz diperoleh rata-rata getaran sebesar 6.249186mm/s di mana kondisi tersebut tergolong masih diizinkan (dapat ditoleransi), dan saat frekuensi 25Hz diperoleh rata-rata getaran sebesar 10.55852mm/s yang tergolong kondisi berbahaya. Selain itu, untuk menjaga produktivitas suatu sistem maka diperlukan suatu pemantauan kondisi dan diagnosis kerusakan pompa secara dini sebagai fungsi dari predictive maintenance. Nilai getaran pompa tersebut diprediksi mengggunakan metode regresi linier dan dievaluasi berdasarkan standar ISO-10816-3. Dengan sistem monitoring dan logging sinyal getaran berbasis Node-RED, diperoleh nilai akurasi dalam memprediksi kondisi pompa pada frekuensi 20Hz sebesar 99,87%. Dalam hal tersebut, pompa diprediksi akan mengalami downtime dalam waktu 10279.1 detik atau 2.8 jam.Keywords: Pompa sentrifugal, Vibration, Misalignment, Predictive maintenance, Fast Fourier Transform

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Section: Pendahuluanunclassified

Model Alat Uji Kerusakan Pada Pompa Sentrifugal Menggunakan Sensor Accelerometer Untuk Predictive Maintenance

Syarief

2023

JITET

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“…It offers an indepth survey of methods for detecting stator-related faults in IMs, providing insights into the strengths and limitations of various diagnostic approaches. Most current review and reported by researchers at the time of this writing included a Canadian researcher, Liang [24] reported about the criticality of condition monitoring for the reliable operation of electrical submersible IMs especially in the oil and gas industries. It allows for the early detection of incipient faults, correct diagnosis, and the prevention of machine failures.…”

Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing Motor Health: IoT-Driven Detection of Electrical Abnormalities in Three-Phase A.C. Induction Motors

Alex Kok Bin See,

Jed Jian Yao Ong

2023

Malaysian J. Sci. Adv. Tech.

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This paper presented a comprehensive investigation into the detection of electrical abnormalities in a 3-phase alternating current (AC) induction motor (IM) rated at 1.5 kW under simulated single phasing and overloading test conditions. The findings from data analysis on electrical abnormalities simulated physically on an IM were reported, employing a non-invasive technique. Sensors were deployed to monitor the IM and measurements were taken. The data logging and control were designed using an industrial-grade graphical system design software, LabVIEW, and NI PXIe-1071 embedded controller hardware. A novel combination of in-situ on-line current measurements, infrared temperature detection, and 3-axes micro-electro-mechanical systems (MEMS) accelerometers were utilized for measurements. The paper described an internet of things (IoT) monitoring system capable of alerting the operator of the motor to any electrical abnormalities. Experimental results suggested that an IM subjected to single phasing experienced observable z-plane vibration, with a standard deviation of 0.24 G. For overload tests (i.e., 50 Hz and 30 Hz) on the IM based on the national electrical manufacturers association (NEMA) standard, the percentage of heating ranged between 103.98% and 109.67%, respectively; however, no significant increase in z-plane vibrations was observed.

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“…As shown in [119], electrical motor faults can be detected from the supply current measurements, and some other measurements, such as magnetic stray flux, motor's output torque, vibrations monitoring and input electric power, as stated in [120]. For example, temperature estimations and vibration measurements are used in [121] and acoustic signals extraction methods have been used in [122] to detect electric motor faults. Usually, to detect a faulty motor a healthy reference is needed in order to compare the measurements and to discover distortions that would suggest the fault in a motor.…”

Section: A Overall Analysismentioning

confidence: 99%

A Review of Artificial Neural Networks Applications in Maritime Industry

Assani,

Matić,

Kaštelan

et al. 2023

IEEE Access

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Artificial neural networks (ANN) are a data driven tool that has been used for modeling, prediction, optimization, classification, diagnostics, decision-making, etc., in various systems where measurements are available to produce significant amount of data. Ship processes are constantly monitored in order to control the operation of the ship and to ensure efficient and safe environment, generating large amount of data. Those data are increasingly being exploited by ANNs and the number of applications is growing. The aim of this paper is to analyze the applications of ANNs in maritime industry, and especially on ships. Based on the review analysis of the sixty-nine papers found published on this topic over the last 10 years in relevant databases, applications have been classified into eight categories in this paper. ANN types, training algorithms, activation functions, as well as measures used to evaluate the performance of the ANN models, have been analyzed for each application category. ANNs rely on data, therefore data acquisition, data processing, organization of the data for training ANN models, their validation and testing have also been addressed in this paper. The conclusions from the review analysis presented should be useful for future work in the area of ANN applications on ships and in maritime industry.INDEX TERMS Applications of artificial neural networks (ANN), ANN types, activation functions, evaluation measures, maritime industry, review analysis, ships, training algorithms

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Temperature Estimation and Vibration Monitoring for Induction Motors and the Potential Application in Electrical Submersible Motors

Cited by 15 publications

References 83 publications

Model Alat Uji Kerusakan Pada Pompa Sentrifugal Menggunakan Sensor Accelerometer Untuk Predictive Maintenance

Model Alat Uji Kerusakan Pada Pompa Sentrifugal Menggunakan Sensor Accelerometer Untuk Predictive Maintenance

Revolutionizing Motor Health: IoT-Driven Detection of Electrical Abnormalities in Three-Phase A.C. Induction Motors

A Review of Artificial Neural Networks Applications in Maritime Industry

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