Abstract: There is disclosed a building automation system, and a method thereof, for managing causal chain. Facility data of the building automation system is collected. One or more suggested causes and one or more causal chains are generated based on the facility data. One or more responsive actions are determined based on the suggested cause or causes, the causal chain or chains, and a cause-action mapping. A particular causal chain is provided based on the at least one responsive action and manager information.

Abstract: There is described a building management system and a method for auto-tagging points. Data associated with multiple points of a site are received, and each point is associated with a point name and a point descriptor. A building name is identified based on the point name for each point by extracting a first part detected frequently among the data associated with the points. A point equipment is determined from a second part of each point name and a point function is determined from a third part of each point name. A set of point tags is generated based on the point equipment, the point function, and the point descriptor. Confidence scores are created for the set of point tags based on matching characteristics to a common tag set.

Abstract: There is described a building management system and a method for auto-tagging points. Data associated with multiple points of a site are received, and each point is associated with a point name and a point descriptor. A building name is identified based on the point name for each point by extracting a first part detected frequently among the data associated with the points. A point equipment is determined from a second part of each point name and a point function is determined from a third part of each point name. A set of point tags is generated based on the point equipment, the point function, and the point descriptor. Confidence scores are created for the set of point tags based on matching characteristics to a common tag set.

Abstract: Methods for failure analysis in a building automation system and corresponding systems and computer-readable mediums. A method includes receiving device event data for a plurality of devices and executing a fault diagnostics inference engine to determine faults corresponding to the device event data. The fault diagnostics inference engine includes a dynamic Bayesian network and a conditional probability table. The method includes executing a predictive maintenance engine to produce probabilities of hardware failures based on the determined faults and the device event data. The method includes updating the conditional probability table based on the probabilities of hardware failures. The method includes producing updated faults by the predictive maintenance engine according to the updated conditional probability table. The method includes displaying the updated faults.

Abstract: An approach that collects sensor data associated with a building automation system having filters and determining the optimal timing of the replacement of filters that includes replacement dates based upon use, utility, and labor costs.

Abstract: An approach that collects sensor data associated with a building automation system having filters and determining the optimal timing of the replacement of filters that includes replacement dates based upon use, utility, and labor costs.