This article discusses the design of a rotating machine vibration monitoring system in real time by utilizing a 3-axis accelerometer MEMS sensor. The system consists of 2 main parts, namely, vibration data acquisition system and data analyse system with the principle of wireless data communication. The results of vibration measurements from each three MEMS accelerometer sensors will be forwarded by the Arduino Nano microcontroller to the data collection device, the Arduino Uno microcontroller. Also, the machine rotating speed data acquired by the TCRT5000 speed sensor will be forwarded by the Arduino Pro Mini microcontroller to the same collection device. Data that has been recorded by the collection device is then transmitted to the computer through wireless communication using the Node-MCU device. The Lab View software is used as a machine vibration data display that has previously been processed by the computer in accordance with parameters desired by the user. Testing stability of data transmission with a certain length of time and communication distance is carried out to ensure the measurement results are in accordance with the real-time vibration conditions. Test results show an average delay time of below 200ms for the farthest distance from the wireless device signal range.
In this paper, a dynamic model of two-wheeled balancing robot has been created, and the two types of FLC has been designed. The Mamdani methods used on both FLC. The first FLC uses pendulum tilt angle theta (θ) as the input and it requires the motor torque to keep the robot remains balanced as the output. The second FLC uses two inputs, the first input is theta (θ) and the second input is the change in the value of theta (θ) which is the output torque of the motor. The second plant model and FLC built by using Matlab Simulink. The first case is one input using 5 membership functions (mf). The second case is two inputs using the 5 and 7 mf. The characteristics and effects of the changes in the input and mf have been simulated in the Simulink and compared. By expanding the number of the inputs can reduce motor specification required in balancing robot. Meanwhile, by increasing the number mf, it can improve the performance of the controller much faster to reach the settling time.
Indra Dwisaputra, Boy Rolastin, Irwan, Angga Satera, in this paper explain that one commodity that can be used as a superior post-mining area such as Bangka Belitung is shrimp ponds. Touch of technology is needed because of high rainfall in this area so that changes in temperature, humidity, salinity and pH are more dynamic. Then we need a control to determine the water quality in shrimp ponds to avoid crop failure. This research was conducted by means of simulations to make it easier to analyze the results before applying. Fuzzy logic control (FLC) method is used for the water quality decision making system. By utilizing 3 input variables such as temperature, salinity and pH of water and output is an index of water quality from a scale of 0-100. The Mamdani method is applied to FLC with 27 fuzzy rules. The simulation results show that a change in one of the input variables can cause water quality to drop to 70. When there are changes in the two input variables, it can reduce water quality by 25.1. FLC can work well in decision making for water quality in shrimp culture.
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