公开(公告)号：US20160245279A1

公开(公告)日：2016-08-25

申请号：US14628322

申请日：2015-02-23

申请人： Biplab Pal ,  Steve Gillmeister ,  Amit Purohit

发明人： Biplab Pal ,  Steve Gillmeister ,  Amit Purohit

IPC分类号： F04B51/00 ,  G01N15/08 ,  G01M3/02

CPC分类号： F04B51/00 ,  G01M3/025 ,  G01M3/26

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.

摘要翻译： 一种用于真空泵预测和预防性维护的机器学习架构的方法和系统。 该方法包括通过通信网络接收电机传感器数据和鼓风机传感器数据中的一个。 电机传感器数据分为真空状态传感器数据和断开状态传感器数据之一。 分析真空状态传感器数据以检测操作真空度，并且当真空状态传感器数据超过预定义的安全范围时，提高报警。 真空断裂数据被分为清洁过滤器类别和过滤器类别中的一个，并且如果检测到堵塞的过滤器类别下的入口，则报警。 基于机器学习分析与电机传感器数据相关联的鼓风机传感器数据，以检测油位不足和油结构不足之一。