Sistem Monitoring Dan Klasifikasi Kondisi Operasional Motor Hoist Overhead Crane Berbasis Logika Fuzzy Berdasarkan Beban Angkat Dan Arus Motor

Fernanda, Vicky (2026) Sistem Monitoring Dan Klasifikasi Kondisi Operasional Motor Hoist Overhead Crane Berbasis Logika Fuzzy Berdasarkan Beban Angkat Dan Arus Motor. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Motor hoist merupakan komponen utama pada overhead crane yang berfungsi mengangkat beban. Pemantauan kondisi operasional motor secara real-time diperlukan untuk mengetahui penggunaan crane sesuai kapasitas dan mendukung evaluasi operasional. Penelitian ini bertujuan mengembangkan sistem monitoring dan klasifikasi kondisi operasional motor hoist berdasarkan parameter beban angkat dan arus motor menggunakan metode Fuzzy Inference System (FIS) Mamdani. Sistem menggunakan sensor load cell untuk mengukur beban angkat dan Inverter Variabel Speed Drive (VSD) untuk memperoleh data arus motor melalui komunikasi Modbus RS485. Data diproses menggunakan aturan fuzzy untuk menghasilkan nilai Motor Operational Index (MOI) dan mengklasifikasikan kondisi operasional motor ke dalam tiga kategori utama, yaitu Aman, Waspada, dan Bahaya. Hasil pengujian menunjukkan bahwa sistem berhasil melakukan monitoring dengan menyimpan 18.805 data ke database InfluxDB selama 24 jam dengan dari estimasi data 24 yaitu 20.046 data, dengan waktu pembaruan rata-rata 4,40 detik. Pada pengujian aktual, nilai MOI berada pada rentang 77,28% hingga 80,82%, sehingga seluruh data aktual diklasifikasikan dalam status Aman. Verifikasi rule base menggunakan skenario input menunjukkan bahwa seluruh aturan fuzzy berhasil menghasilkan keluaran sesuai rancangan, terbukti saat verifikasi arus maksimal 4,8 A pada beban ringan 100 kg, sistem secara akurat mengunci nilai MOI turun ke angka 19,44% dengan status Bahaya. Dengan demikian, sistem yang dikembangkan dapat digunakan sebagai pendukung pemantauan kondisi operasional pada motor hoist pada overhead crane.
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The hoist motor is a key component of an overhead crane that is used to lift loads. Real-time monitoring of the motor’s operational condition is necessary to ensure the crane is operated within its capacity and to support operational evaluations. This study aims to develop a system for monitoring and classifying the operational condition of a hoist motor based on load and motor current parameters using the Mamdani Fuzzy Inference System (FIS) method. The system uses a load cell sensor to measure the load and a Variabel Speed Drive (VSD) Inverter to obtain motor current data via Modbus RS485 communication. The data is processed using fuzzy rules to generate a Motor Operational Index (MOI) value and classify the motor’s operational condition into three main categories: Safe, Caution, and Danger. Test results show that the system successfully performed monitoring by storing 18,805 data points in the InfluxDB database over a 24-hour period—out of an estimated 24-hour total of 20,046 data points—with an average update time of 4.40 seconds. In actual testing, the MOI values ranged from 77.28% to 80.82%, so all actual data was classified as Safe. Verification of the rule base using input scenarios showed that all fuzzy rules successfully produced outputs as designed. This was demonstrated during verification of a maximum current of 4.8 A under a light load of 100 kg, when the system accurately identified the MOI value dropping to 19.44% with a “Danger” status. Thus, the developed system can be used to support the monitoring of operational conditions in hoist motors on overhead cranes.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Overhead Crane, Motor Hoist, Motor Operational Index, Fuzzy Mamdani
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ1363 Cranes, derricks, etc. T Technology > TJ Mechanical engineering and machinery > TJ174 Maintenance and repair of machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2785 Electric motors, Induction.
Divisions:	Faculty of Vocational > 36304-Automation Electronic Engineering
Depositing User:	Vicky Fernanda
Date Deposited:	05 Aug 2026 01:10
Last Modified:	05 Aug 2026 01:10
URI:	http://repository.its.ac.id/id/eprint/143705

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