Abstract: A system for detecting malicious operation of a building system includes a power characteristic input connected to a plurality of power characteristic sensors, a processor and a memory. The memory stores instructions for operating at least a physics model detection, a machine learning model detection and a combination module. The physics model detection includes multiple predefined expected power characteristics and is configured to detect an anomaly when at least one power characteristic received at the power characteristic input deviates from a corresponding predefined expected power characteristic of the predefined expected power characteristics. The machine learning model includes a machine learning system configured to learn a set of expected normal power characteristics and detect the anomaly when at least one power characteristic received at the power characteristic input deviates from the learned set of expected normal power characteristics.

Abstract: A system for detecting a sub-surface defect comprising a transducer fluidly coupled to a part located in a tank containing a liquid configured to transmit ultrasonic energy, the transducer configured to scan the part to create scan data of the scanned part; a pulser/receiver coupled to the transducer configured to receive and transmit the scan data; a processor coupled to the pulser/receiver, the processor configured to communicate with the pulser/receiver and collect the scan data; and the processor configured to detect the sub-surface defect and the processor configured to have a sub-surface defect confidence assessment and a prioritization for further human evaluation.

Abstract: A system comprising a computer readable storage device readable by the system, tangibly embodying a program having a set of instructions executable by the system to perform the following steps for detecting a sub-surface defect, the set of instructions comprising an instruction to receive scan data for a part from a transducer; an instruction to collect the scan data; an instruction to determine an indication in the scan data that indicates a distractor, wherein the indication is based on a learning phase module and an inference phase module that the processor uses to self-assess the indication; and an instruction to create a defect indication report.

Abstract: A method of inspecting a component for anomalous regions includes recording a plurality of sensor readings using one or more sensors, each reading corresponding to a different region of the component; determining an appearance metric for each reading; and determining a nominal appearance metric based on individual values of the appearance metric for a first subset of the readings. The method includes, for a particular sensor reading outside the first subset: determining a difference between the nominal appearance metric and the appearance metric of the particular sensor reading; updating the nominal appearance metric based on the particular sensor reading; and, based on the difference exceeding a threshold: determining that the particular sensor reading is anomalous and corresponds to an anomalous region of the component, and recording additional sensor readings of the anomalous region using one or more sensing parameters that differ from those used to record the anomalous sensor reading.

Abstract: A method of controlling a matrix converter system is provided. The method includes receiving an operating condition and consulting a trained Q-data structure for reward values associated with respective switching states of the switching matrix for an operating state that corresponds to the operating condition. The Q-data structure is trained using Q-learning to map a reward value predicted for respective switching states to respective discrete operating states. The method further includes sorting the reward values predicted for the respective switching states mapped to the operating state that corresponds to the operating condition, selecting a subset of the set of the mappings as a function of a result of sorting the reward values associated with the switching states of the operating state, evaluating each switching state included in the subset, and selecting an optimal switching state for the operating condition based on a result of evaluating the switching states of the subset.

Abstract: An analytics device for monitoring maintenance on an elevator system performed by an individual including: a processor; and a memory including computer-executable instructions that, when executed by the processor, cause the processor to perform operations, the operations including: capturing a first video stream using a first video camera; extracting sequences from at least the first video stream; extracting features from the sequences; and analyzing, using a long short-term memory model, the sequence to determine whether the maintenance performed on the elevator system by the individual is performed correctly.

Abstract: A system comprising a computer readable storage device readable by the system, tangibly embodying a program having a set of instructions executable by the system to perform the following steps for detecting a sub-surface defect, the set of instructions comprising an instruction to receive scan data for a part from a transducer; an instruction to collect the scan data; an instruction to determine an indication in the scan data that indicates a distractor, wherein the indication is based on a learning phase module and an inference phase module that the processor uses to self-assess the indication; and an instruction to create a defect indication report.

Abstract: A system for detecting a sub-surface defect comprising a transducer fluidly coupled to a part located in a tank containing a liquid configured to transmit ultrasonic energy, the transducer configured to scan the part to create scan data of the scanned part; a pulser/receiver coupled to the transducer configured to receive and transmit the scan data; a processor coupled to the pulser/receiver, the processor configured to communicate with the pulser/receiver and collect the scan data; and the processor configured to detect the sub-surface defect and the processor configured to have a sub-surface defect confidence assessment and a prioritization for further human evaluation.

Abstract: An analytics device for monitoring maintenance on an elevator system performed by an individual including: a processor; and a memory including computer-executable instructions that, when executed by the processor, cause the processor to perform operations, the operations including: capturing a first video stream using a first video camera; extracting sequences from at least the first video stream; extracting features from the sequences; and analyzing, using a long short-term memory model, the sequence to determine whether the maintenance performed on the elevator system by the individual is performed correctly.

Abstract: A method of controlling a matrix converter system is provided. The method includes receiving an operating condition and consulting a trained Q-data structure for reward values associated with respective switching states of the switching matrix for an operating state that corresponds to the operating condition. The Q-data structure is trained using Q-learning to map a reward value predicted for respective switching states to respective discrete operating states. The method further includes sorting the reward values predicted for the respective switching states mapped to the operating state that corresponds to the operating condition, selecting a subset of the set of the mappings as a function of a result of sorting the reward values associated with the switching states of the operating state, evaluating each switching state included in the subset, and selecting an optimal switching state for the operating condition based on a result of evaluating the switching states of the subset.

Abstract: A system for detecting malicious operation of a building system includes a power characteristic input connected to a plurality of power characteristic sensors, a processor and a memory. The memory stores instructions for operating at least a physics model detection, a machine learning model detection and a combination module. The physics model detection includes multiple predefined expected power characteristics and is configured to detect an anomaly when at least one power characteristic received at the power characteristic input deviates from a corresponding predefined expected power characteristic of the predefined expected power characteristics. The machine learning model includes a machine learning system configured to learn a set of expected normal power characteristics and detect the anomaly when at least one power characteristic received at the power characteristic input deviates from the learned set of expected normal power characteristics.