Abstract: Methods for predictive maintenance with using machine learning in a building automation system and corresponding systems and computer-readable mediums. A method includes receiving device event data corresponding to a device and executing an inference engine to determine root cause fault data corresponding to the device event data. The method includes executing a predictive maintenance engine to produce a survival analysis for the physical device based on the root cause fault data. The method includes producing updated failure data by the predictive maintenance engine, based on the survival analysis, and providing the updated failure data to the inference engine. The inference engine thereafter uses the updated failure data in a subsequent root cause analysis. The method includes outputting the survival analysis.

Abstract: Methods for data quality analysis and aggregation in a building automation system and corresponding systems and computer-readable mediums. A method includes receiving input data and receiving a configuration file that defines data quality (DQ) processes to be performed on the input data. The method includes dynamically building a configurable pipeline based on the configuration file, the pipeline including one or more Data Quality Indicator (DQI) or Data Quality Aggregation (DQA) process components from a DQ core library. The method includes performing DQ processes on the input data, including executing each of the DQI or DQA process components included in the pipeline, producing one or more DQ results based on the DQ processes, and returning the one or more DQ results.

Abstract: Methods for data quality analysis and aggregation in a building automation system and corresponding systems and computer-readable mediums. A method includes receiving input data and receiving a configuration file that defines data quality (DQ) processes to be performed on the input data. The method includes dynamically building a configurable pipeline based on the configuration file, the pipeline including one or more Data Quality Indicator (DQI) or Data Quality Aggregation (DQA) process components from a DQ core library. The method includes performing DQ processes on the input data, including executing each of the DQI or DQA process components included in the pipeline, producing one or more DQ results based on the DQ processes, and returning the one or more DQ results.

Abstract: Methods for predictive maintenance with using machine learning in a building automation system and corresponding systems and computer-readable mediums. A method includes receiving device event data corresponding to a device and executing an inference engine to determine root cause fault data corresponding to the device event data. The method includes executing a predictive maintenance engine to produce a survival analysis for the physical device based on the root cause fault data. The method includes producing updated failure data by the predictive maintenance engine, based on the survival analysis, and providing the updated failure data to the inference engine. The inference engine thereafter uses the updated failure data in a subsequent root cause analysis. The method includes outputting the survival analysis.

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.