Design of ANFIS system to detect the condition of generator set model P22-6 based on Omron CJ1M PLC - Rahmawati, Nanda Putri; Adhitya, Ryan Yudha; Widodo, Hendro Agus; Afianto, Afif Zuhri; Khumaidi, Agus; Budiawati, Ratna; Hardiyanti, Fitri; Santoso, Mochamad Yusuf (Journal of Soft Computing Exploration, 2024)
The application of machine monitoring systems is currently increasingly needed, one of which is on generators. Generator sets are one of the important elements in providing energy needed in company operations. However, to ensure optimal performance and prevent unexpected engine damage, careful monitoring of the generator set's operational conditions is required, especially of key variables such as temperature, rotation speed, and engine vibration. The purpose of this study is to identify the condition of the generator set using three parameters. In this research, adaptive neuro fuzzy inference system (ANFIS) is used as a tool to model the relationship between inputs (temperature, speed, and vibration) and outputs (engine condition). The dataset for normal conditions amounted to 25 data and for abnormal conditions amounted to 25 data. From this data, an RMSE of 0.000032 was obtained in the 3-3-5 membership function structure with a trapezoidal type membership function. And at the stage of applying fuzzy to the Omron PLC, the RMSE is 0. Simulations are carried out to test the effectiveness of ANFIS in predicting machine conditions based on monitored parameters.