REAL TIME MACHINE LEARNING BASED PREDICTIVE AND PREVENTIVE MAINTENANCE OF VACUUM PUMP

摘要

A method and system of a machine learning architecture for predictive and preventive maintenance of vacuum pumps. The method includes receiving one of a motor sensor data and a blower sensor data over a communications network. The motor sensor data is classified into one of a vacuum state sensor data and break state sensor data. The vacuum state sensor data is analyzed to detect an operating vacuum level and an alarm is raised when the vacuum state sensor data exceeds a pre-defined safety range. Vacuum break data is classified into one of a clean filter category and clogged filter category and an alarm is raised if an entry under the clogged filter category is detected. The blower sensor data in association with the motor sensor data is analyzed based on machine learning to detect one of a deficient oil level and a deficient oil structure.

主权项

1. A method of a machine learning architecture comprising:
i) receiving at least one of a motor sensor data and a blower sensor data over a communications network,
wherein one of the motor sensor data and the blower sensor data comprises at least one of a vibration, a magnetometer, a gyroscope, a sound and a temperature; ii) classifying at least one of the motor sensor data and the blower sensor data into one of a vacuum state sensor data and break state sensor data,
wherein at least one of the motor sensor data and the blower sensor data are classified by one of individually and in combination,wherein the break state sensor data is received when a rotor of a vacuum pump is malfunctioning; iii) analyzing the vibration data of the vacuum state sensor data to detect an operating vacuum level,
wherein an alarm is raised when the vacuum state sensor data of one of a vibration and a temperature exceeds a pre-defined safety range; and iv) classifying vacuum break data into one of a clean filter category and clogged filter category,
wherein the alarm is raised if an entry under the clogged filter category is detected; and analyzing the blower sensor data in association with the motor sensor data based on machine learning to detect at least one of a deficient oil level and a deficient oil structure.