The left case describes how to use our MEMS vibration sensor to monitor the condition of motors, allowing for predictive maintenance. The MEMS vibration sensor attached to a motor is directly connected to either PL-R4 SENSOR, a microcontrollerintegrated Industrial Raspberry Pi or rPL-vibration, a wireless sensor box.
In this case, over 15,000 data sets sampled by rPL-vibration at a sampling interval of 50 microseconds, are wirelessly sent to a rPL-master. If the measurement frequency is set to five times per day, the expected the battery life is 3 to 5 years. When raw data is received by the PL-R4, it is immediately processed by open source software, like SciPy, which applies mathematical algorithms, such as FFT or envelope transformation.
After plotting and evaluating the results, the data files are then sent to an external server, usually a customer’s database. Without knowing the mechanical parameters of the motors, our algorithms can still accurately detect early defects by continuously comparing defective index values with initial vibration samples. Because our PL-R4 motor condition monitoring software is open source, customers can freely customize the GUI, detection algorithm, or add additional features such as machine learning.
