A system and method for real-time detection of anomalies in a motor drive includes a controller receiving one or more signals corresponding to real-time operation of a controlled system. The controller samples the real-time signal during operation of the controlled system and maintains a moving window of the sampled data. A signature of the sampled data within the moving window is then generated. Each signature corresponds to operation of the controlled system within the period of time defined by the moving window. An identifier and a number of occurrences of each signature may be stored with the signature. An initial table of expected signatures may be generated, for example, by executing a training, or learning, period within the control system. The controller compares each real-time signature against the table of expected signatures to detect the occurrence of an anomaly.

A system and method for real-time detection of anomalies in a motor drive includes a controller receiving one or more signals corresponding to real-time operation of a controlled system. The controller samples the real-time signal during operation of the controlled system and maintains a moving window of the sampled data. A signature of the sampled data within the moving window is then generated. Each signature corresponds to operation of the controlled system within the period of time defined by the moving window. An identifier and a number of occurrences of each signature may be stored with the signature. An initial table of expected signatures may be generated, for example, by executing a training, or learning, period within the control system. The controller compares each real-time signature against the table of expected signatures to detect the occurrence of an anomaly.

A facility employing systems, methods, and/or techniques for dynamically and adaptively configuring configurable energy consuming and producing devices (e.g., smart energy devices) based on user profiles and user presence information is disclosed. In some embodiments, the facility periodically detects the presence of users, and retrieves preference information for those users. For each of one or more configurable energy devices in the area, the facility generates a combined setting based on the preferences of each user present and adjusts the devices based on the combined settings. For example, if User A, User B, and User C are present in a room and User A's preferred temperature setting is 75 F., User B's preferred temperature setting is 68 F., and User C's preferred temperature setting is 70 F., the facility may generate a combined setting for a thermostat by taking the average value of the users in the room.

A facility employing systems, methods, and/or techniques for dynamically and adaptively configuring configurable energy consuming and producing devices (e.g., smart energy devices) based on user profiles and user presence information is disclosed. In some embodiments, the facility periodically detects the presence of users, and retrieves preference information for those users. For each of one or more configurable energy devices in the area, the facility generates a combined setting based on the preferences of each user present and adjusts the devices based on the combined settings. For example, if User A, User B, and User C are present in a room and User A's preferred temperature setting is 75 F., User B's preferred temperature setting is 68 F., and User C's preferred temperature setting is 70 F., the facility may generate a combined setting for a thermostat by taking the average value of the users in the room.

An online monitoring system for industrial processes, such as nuclear power processes, including a data acquisition unit configured to sample output signals simultaneously from a plurality of process sensors, and a computing unit configured to record sampled output signals from the data acquisition unit and to cross-correlate the output signals from two or more of the process sensors to diagnose operation of the industrial process, identify loose parts and/or degradation of industrial plant equipment, enable virtual sensing, calculate sensor response time using the noise analysis technique, and to verify sensor calibration using the cross calibration method and/or empirical and/or physical modeling.

A method of controlling tint of a tintable window to account for occupant comfort in a room of a building. The tintable window is between the interior and exterior of the building. The method predicts a tint level for the tintable window at a future time based on a penetration depth of direct sunlight through the tintable window into the room at the future time and space type in the room. The method also provides instructions over a network to transition tint of the tintable window to the tint level.

A method of controlling tint of a tintable window to account for occupant comfort in a room of a building. The tintable window is between the interior and exterior of the building. The method predicts a tint level for the tintable window at a future time based on a penetration depth of direct sunlight through the tintable window into the room at the future time and space type in the room. The method also provides instructions over a network to transition tint of the tintable window to the tint level.

A table assembly comprising a worktop, a frame supporting the worktop, an integrated drive subassembly, a memory storing defined conditions, a control system including a user input control interface and at least one processor, the input control interface for receiving user commands to initiate changes in worktop height, the processor programmed to perform the steps of a) at any time, respond to direct manual user input via the interface to change the worktop height and b) enable a reminder function to periodically change the worktop height by i) monitoring worktop use, ii) comparing worktop use to at least one of the defined conditions, iii) after the at least one condition is met, providing a signal to the user to change the worktop height to a different position, iv) monitoring for user input via the interface to change the worktop height and (v) upon each of the worktop height being changed prior to providing a signal to the user to change the worktop height and the worktop height being changed subsequent to providing a signal to the user to change the worktop height, repeating steps (i) through (v).

A system and method are disclosed for dynamically learning the optimum energy consumption operating condition for a building and monitor/control energy consuming equipment to keep the peak demand interval at a minimum. The dynamic demand limiting algorithm utilized employs two separate control schemes, one for HVAC loads and one for non-HVAC loads. Separate operating parameters can be applied to the two types of loads and multiple non-HVAC (control zones) loads can be configured. The algorithm uses historical peak demand measurements in its real-time limiting strategy. The algorithm continuously attempts to reduce peak demand within the user configured parameters. When a new peak is inevitable, the algorithm strategically removes and/or introduces loads in a fashion that limits the new peak magnitude and places the operating conditions within the user configured parameters. In an embodiment, the algorithm that examines the previous seven days of metering information to identify a peak demand interval. The system then uses real-time load information to predict the demand peak of the upcoming interval, and strategically curtails assigned loads in order to limit the demand peak so as not to set a new peak.

A system and method are disclosed for dynamically learning the optimum energy consumption operating condition for a building and monitor/control energy consuming equipment to keep the peak demand interval at a minimum. The dynamic demand limiting algorithm utilized employs two separate control schemes, one for HVAC loads and one for non-HVAC loads. Separate operating parameters can be applied to the two types of loads and multiple non-HVAC (control zones) loads can be configured. The algorithm uses historical peak demand measurements in its real-time limiting strategy. The algorithm continuously attempts to reduce peak demand within the user configured parameters. When a new peak is inevitable, the algorithm strategically removes and/or introduces loads in a fashion that limits the new peak magnitude and places the operating conditions within the user configured parameters. In an embodiment, the algorithm that examines the previous seven days of metering information to identify a peak demand interval. The system then uses real-time load information to predict the demand peak of the upcoming interval, and strategically curtails assigned loads in order to limit the demand peak so as not to set a new peak.