Systems and methods for predicting building faults using machine learning

A system comprising one or more memory devices configured to store instructions thereon that, when executed by one or more processors, cause the one or more processors to:

• receive a plurality of measurements for one or more points that are associated with a piece of building equipment, the plurality of measurements measured during a first time period;
• execute a machine learning model using the plurality of measurements as an input to generate fault data for a plurality of time periods subsequent to the first time period;
• select a second time period from the plurality of time periods responsive to an assessment of the fault data for the plurality of time periods indicating a fault will likely occur in the piece of building equipment during the second time period of the plurality of time periods;
• and
• perform an automated action responsive to the selection of the second time period.

A method, comprising:

• receiving, by one or more processors, a plurality of measurements for one or more points that are associated with a piece of building equipment, the plurality of measurements measured during a first time period;
• executing, by the one or more processors, a first machine learning model using the plurality of measurements to obtain an output predicting a fault will occur in the piece of building equipment within a second time period subsequent to the first time period;
• responsive to the prediction that a fault will occur in the piece of building equipment within the second time period, executing, by the one or more processors, a second machine learning model using the plurality of measurements and an identification of the second time period to obtain an output indicating a predicted root cause of the predicted fault in the piece of building equipment;
• and
• performing, by the one or more processors, an automated action responsive to the predicted root cause of the predicted fault in the piece of building equipment.