The present invention provides a bioreactor controller with a simple user interface comprising a microcontroller and a linear motor. The bioreactor controller is microcontroller-based, has a greater temporal accuracy, and inexpensive. The microcontroller of the bioreactor controller selects a protocol mode and a setup mode by the user, runs the protocol mode if the setup mode is not selected by the user, stops running the protocol mode for a predetermined time if the protocol mode is selected by the user until the user is completed with calculations and record-keeping, detects movement error of the bioreactor controller, and informs the error to the user.

Various embodiments of a method and apparatus for diagnosing the status of a vehicle using cognitive SMART sensors are disclosed. The SMART sensors have at least some processing power directly associated with the sensor. The SMART sensor that include an artificial intelligence (AI) core are is tightly coupled to the sensor within the SMART sensor. The sensor may be an audio sensor, such as a high quality microphone, that can record sounds that emanate from of the vehicle. Such sounds may be associated with one or more of the components of the vehicle. The AI core analyzes the sounds that emanate from the vehicle to determine the operational status of the vehicle. A communication module allows the result to be communicated to remotely located interested entities.

A building monitoring system, wherein the system comprises an HVAC monitor, which comprised a condensing unit connector, an air handler connector, a thermostat connector and a control module, a wetness detector; and a communicator. The communicator accesses a location of the building monitoring system and selectively communicates with a technician based at least in part on the location. A monitor system for use with an existing HVAC unit, wherein the HVAC unit has a condenser or furnace, an air handler, and a thermostat, the HVAC monitor system comprising at least one condenser or furnace sensor, at least one air handler sensor and at least one control module. A wetness monitor system, comprising at least one wetness detector connected to a control, wherein the control is also connected to a network and the network is connected to a flow shutoff valve. A method of monitoring a location for wetness comprising detecting wetness through at least one wetness detector, monitoring the state of a shutoff valve.

Systems and methods presented herein include a real-time operations center includes at least one processor and at least one memory medium. The at least one processor is configured to execute computer-readable instructions stored in the at least one memory medium that, when executed by the at least one processor cause the real-time operations center to receive operational data in substantially real-time from equipment that is located at a worksite and that is being monitored by the real-time operations center; perform data analytics on the operational data during operation of the equipment; and provide one or more graphical user interfaces to one or more computing devices, wherein the one or more graphical user interfaces illustrate results of the data analytics. In addition, systems and methods presented herein also include a data analytics kiosk having substantially similar features and configured to display the one or more graphical user interfaces via a display of the data analytics kiosk.

A method used for generating a zoomable diagram includes providing a topology diagram of at least a part of the industrial plant for interacting with components of the industrial plant; generating, for each zoom-level of the zoomable diagram, a zoomed diagram comprising a zoomed view of each one of the plurality of objects and connections, by applying at least following rules to each object: from a first predefined zoom-level, collapse all objects of a plant-segment to one object and keep only connections that cross the plant-segment's border, and introduce an plant-segment related alarm measure as a function of all alarm statuses of the plurality of objects of the plant-segment; and when interacting with the components of the industrial plant, determining, for each plant-segment, the plant-segment related alarm measure, and outputting the plant-segment related alarm measure for healing a cause of the alarm.

The invention discloses a visualization method for process monitoring based on bi-kernel t-distributed stochastic neighbor embedding. It includes two steps of offline modeling and online monitoring. In offline modeling, standard t-SNE method is used to reduce the dimension of historical normal data. The mapping parameter matrix from the input kernel matrix to the feature kernel matrix is calculated. PCA is used to reduce the feature kernel matrix to two dimensions, and then the square Mahalanobis distance is calculated as a statistic and the control limit is solved. Online monitor and calculate the kernel function to between the collected data and the modeling data; and the obtained kernel vector is multiplied by the mapping parameter matrix to obtain the mapped feature kernel vector. PCA is used to reduce the dimension of the mapped feature kernel vector to obtain two-dimensional features for visualization. Draw the scatter diagram of the feature and observe whether it is within the ellipse control limit. Compared with the prior art, the present invention retains the advantage of data dimension reduction of the standard t-SNE method, and meanwhile applies it to the visualization of industrial process fault monitoring, reducing the rate of misreport and underreport of industrial process monitoring.

Equipment in a production facility is managed with use of information from the equipment and information from a sensor for measurement of a state of the equipment, while achieving a high degree of freedom in increasing and decreasing the number of such sensors.

The present invention is provided with a gateway (10) and a server (20). The gateway is connected to a controller included in each piece of equipment and connected, without being intermediated by the controller, to an externally attached sensor accompanying the each piece of equipment. The server is connected to the gateway. The gateway carries out the processes of: receiving, from each controller, information indicative of an internal state of the piece of equipment; receiving, from each sensor, information indicative of an external state of the piece of equipment; and transmitting these pieces of information to the server. The server carries out a process of combining and outputting the information indicative of the internal state and the information indicative of the external state received from the gateway.

Systems and methods for verifying correct completion of a power control sequence in a switchgear provide a commissioning agent that can automatically verify correct completion of the power control sequence. The commissioning agent includes a sequence verification tool that can initiate performance of a power control sequence in the switchgear and immediately compare the sequence actually performed to the sequence intended to be performed. If the two sequences do not match in order and timing, the verification tool reports the mismatch and identifies the error. In some embodiments, the commissioning agent further includes a log viewer that captures the sequence actually performed in the switchgear and displays the result for viewing. Such an arrangement provides a faster and more reliable way to double-check the system integrity of the switchgear during initial engineering and after factory and/or field modifications.

A learning apparatus is provided, which comprises: a learning data acquiring unit for acquiring learning data including measurement data obtained by measuring a facility and a state of the facility; a learning pre-processing unit for performing a pre-processing for reducing a drift of the measurement data in the learning data and outputting pre-processed learning data; and a learning processing unit for performing a processing for learning a model for determining the state of the facility from the pre-processed measurement data, by using the pre-processed learning data.

Systems and methods are provided for dynamically grouping data analysis content derived from a plurality of sensors. In one embodiment, a plurality of sensors can be disposed on a plurality of machine trains or one or more machines within the plurality of machine trains configured in an industrial environment. A communication circuit can be operatively coupled to the plurality of sensors and configured to communicate data measured by the plurality of sensors, and a dynamic graphical user interface (GUI) can be provided on a touchscreen display and can be configured to dynamically generate one or more visualizations of the measured data. A processor can be configured to receive the measured data via the communication circuit, to generate a plurality of measurements based on the measured data, and to operatively control the dynamic GUI in response to a grouping mode selection.