Johnson Controls Tyco IP Holdings Llp

Tyco Fire + Security Gmbh

Christopher J Verink

Kelsey Carle Schuster

Sajjad Pourmohammad

Andrew S Pike

Matthias Walczyk

John M Sullivan

A system for predicting performance of building equipment includes one or more processors and memory storing instructions. When executed by the one or more processors, the instructions cause the one or more processors to perform operations. The operations include obtaining sensor data generated by one or more sensors while operating the building equipment to affect one or more environmental conditions of a building, generating a predicted performance of the building equipment using a machine learning model if the machine learning model comprises prior data similar to the sensor data, and requesting feedback from an expert related to the predicted performance of the building equipment if the sensor data are different from the prior data.

Analysis system with machine learning based interpretation

A system includes a plurality of vibration sensors configured to be coupled to a unit of building equipment, an edge device arranged between the plurality of vibration sensors and a communications network. The edge device is programmed to detect an abnormality in vibration data from the plurality of vibration sensors by ingesting streams of vibration data from the plurality of vibration sensors, performing signal processing on the streams of vibration data to obtain inputs for a machine learning model, determining whether the abnormality is occurring by applying the inputs to the machine learning model, determining a trend associated with the abnormality based on outputs of the machine learning model, and causing a portion of the vibration data to be communicated to a first computing system via the communications network in response to the trend satisfying a criterion.

Edge devices and gateways with machine learning for detecting anomalies in building equipment vibration data

A building management system includes building equipment operable to affect a variable state or condition of a building and a controller including a processing circuit. The processing circuit is configured to obtain a vibration data set related to vibrations of the building equipment and 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 and initiate a corrective action responsive to identifying the vibration data set as abnormal.

Building management system with machine learning for detecting anomalies in vibration data sets

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.

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.

A vibration analysis system for predicting performance of a building system includes one or more memory devices configured to store instructions that, when executed on one or more processor, cause the one or more processors to receive vibration data from the building equipment, generate a performance prediction for the building equipment, generate a performance explanation for the performance prediction, and cause a user interface to display the performance prediction and the performance explanation.

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Glendale, WI, United States of America

Christopher J Verink