Embodiments of systems and methods that can facilitate data collection for valve diagnostics. The systems can include a valve assembly with a valve and a sampling device that is configured to access a repository with a first buffer and a second buffer. During operation, the valve assembly is configured to read data representing operating variables for the valve into the first buffer. The valve assembly is also configure to determine a quality measure for a first sample set of data from the first buffer, the quality measure indicating the usefulness of the first sample set of data for predicting performance of the valve relative to a second sample set of data from the second buffer. In one embodiment, the valve assembly is further configured to read data from the first buffer into the second buffer in response to the quality measure indicating that the first sample set of data is relatively more useful than the second sample set of data.

A control layer automation device comprises a processor, one or more control layer applications, a database, a wireless interface, a device memory. Each control layer application is configured to perform a discrete set of automation functions. The database comprises a plurality of operator device identifiers and the wireless interface allows the one or more control layer applications to communicate with a plurality of operator devices via the plurality of operator device identifiers. The device memory comprises the one or more control layer applications. The control layer application manager is configured to manage execution of the one or more control layer applications on the processor.

A user assistance system of a reprocessing apparatus for cleaning and disinfecting at least one surgical instrument arranged in a cleaning basket, the user assistance system including: at least one electronically controlled display device; and a controller coupled with the at least one electronically controlled display device, the at least one electronically controlled display device being integrated in a loading station and being configured for arranging the cleaning basket on one side of the display device during the loading of the cleaning basket, the controller being configured to control the display device such that image information is displayed in an actual size and position which indicates a predetermined arrangement within the cleaning basket of the surgical instrument that is to be reprocessed.

A system for generating and using a predictive model to control building equipment includes building equipment operable to affect one or more variables in a building and an operating data aggregator that collects a set of operating data for the building equipment. The system includes an autocorrelation corrector that removes an autocorrelated model error from the set of operating data by determining a residual error representing a difference between an actual output of the building equipment and an output predicted by the predictive model, using the residual error to calculate an autocorrelation for the model error, and transforming the set of operating data using the autocorrelation. The system includes a model generator module that generates a set of model coefficients for the predictive model using the transformed set of operating data and a controller that controls the building equipment by executing a model-based control strategy that uses the predictive model.

The present invention relates to a dual controller system for analyzing a control signal received from two dual controllers, both of which operate in an active state, to check whether an error occurs in the controllers and to perform operation with a controller in a normal state. A dual controller system according to the present invention includes a plurality of lower-layer modules performing respective functions, and first and second controllers for controlling each of the plurality of lower-layer modules, wherein the first and second controllers transmit control signals to the plurality of lower-layer modules, and the lower-layer modules determine whether an error occurs in the two received control signals, remove an erroneous control signal and perform a function according to a normal control signal.

Described in detail herein is an automated fulfilment system including a computing system programmed to receive requests from disparate sources for physical objects disposed at one or more locations in a facility. The computing system can combine the requests, and group the physical objects in the requests based on object types or expected object locations. Autonomous robot devices can receive instructions from the computing system to retrieve a group of the physical objects and deposit the physical objects in storage containers.

An operation mode specifying unit specifies an operation mode of a machine by comparing time-series data of an amplitude and a frequency of measurement data obtained from a sensor with definition data of the operation mode of the machine created in advance by an operation mode data creation unit. In addition, an abnormality diagnosis unit performs processing of cluster analysis for the measurement data obtained from the sensor or the like, and diagnoses abnormality of the machine according to diagnosis procedure information that is set in advance depending on the set operation mode and an abnormality mode of the machine.

An error diagnosis method includes: the parameter value obtaining step of obtaining multiple parameter values; the error detection step of calculating a Mahalanobis distance from a unit space based on the obtained parameter values and diagnosing whether or not error is caused at the real machine based on the calculated Mahalanobis distance; the error portion estimation step of estimating a error portion of the real machine based on the Mahalanobis distance calculated at the error detection step; and the matching determination step of structuring an error analyzing model for analyzing the real machine based on the error portion of the real machine estimated at the error portion estimation step and determining whether or not an output analytical signal of the real machine obtained by analysis of the error analyzing model and the output signal output from the real machine match with each other.

A device may receive equipment information, associated with a first equipment, including information associated with anomalies identified based on operational information collected during operation of the first equipment, and messages generated during the operation of the first equipment. The device may receive maintenance information, associated with the first equipment, that identifies one or more part failures associated with one or more equipment parts. The device may identify associations between the one or more part failures and the first equipment information. The device may receive equipment information, associated with a second equipment, including information associated with anomalies identified based on operational information collected during operation of the second equipment, and messages generated during the operation of the second equipment. The device may generate and provide a prediction, associated with a future failure of an equipment part of the second equipment, based on the second equipment information and the associations.

A system, apparatus and method of determining remaining life of a bearing is disclosed. The method includes generating a bearing model of the bearing. The bearing model is based on one of condition data associated with operation of the bearing, historical condition data of the bearing, bearing specification and technical specification of a technical system including the bearing. The method further includes predicting a defect in the bearing based on the bearing model and predicting the remaining life of the bearing based on the predicted defect.