公开(公告)号：US20210116904A1

公开(公告)日：2021-04-22

申请号：US16658822

申请日：2019-10-21

Applicant: Johnson Controls Technology Company

Inventor： Kelsey Carle Schuster ,  Christopher J. Verink

IPC: G05B23/02 ,  G06N20/00 ,  G05B13/02 ,  G01H11/00

Abstract: A building management system including building equipment operable to affect a variable state or condition of a building. The building management system includes a controller including a processing circuit. The processing circuit is configured to obtain a vibration data set related to vibrations of the building equipment. The processing circuit is configured to analyze the vibration data set by one or more machine learning models to generate a set of probabilities. The set of probabilities is related to a probability that the vibration data set is abnormal. The processing circuit is configured to identify the vibration data set as normal or abnormal based on the set of probabilities. The processing circuit is configured to initiate a corrective action responsive to identifying the vibration data set as abnormal.

公开(公告)号：US20200380387A1

公开(公告)日：2020-12-03

申请号：US16427806

申请日：2019-05-31

Applicant: Johnson Controls Technology Company

Inventor： Sajjad Pourmohammad ,  Kelsey Carle Schuster ,  Christopher J. Verink

IPC: G06N5/04 ,  G06N20/00 ,  F24F11/30

Abstract: One embodiment of the present disclosure is a system for predicting performance of building equipment. The system comprises one or more sensors in communication with the building equipment, and the sensors are operable to detect characteristics from the building equipment. The system further comprises a computing device in communication with the sensors and in the same geographic location as the sensors. The computing device comprises one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to receive data from the sensors, the data based on the detected characteristics. The one or more processors also generate, based on a machine learning model and the data, a predicted performance of the building equipment when the machine learning model comprises a prior data substantially similar to the data.