Honeywell International Inc.

Ademco Inc.

Petr Stluka

Karel Marik

Jiri Rojicek

Roman Navratil

Aravind Padmanabhan

Wendy K Foslien

Bin Sai

Vladimir Havlena

Ondrej Holub

Girija Parthasarathy

Jiri Vass

Wendy Foslien Graber

Zdenek Schindler

Petr Cisar

Eva Jerhotova

Josef Rieger

Rylan Clark

A structure having convergence of a control system and a data analytics system. The control system may involve input-output models of items, for example, homes, buildings and process plants. The system may use sensor data and scientific principles to implement control models. The models may help to precisely achieve a desired value of a parameter. The data analytics system may involve analysis of diverse data and may create insights of the items. The control system plus the data analytics system may close the loop between insights and on-premise controls of the items represented by the input-output models. The control and data analytics systems may merge the best of both worlds to solve difficult issues pertaining to the items. The structure may be for comfort control, energy reduction, and so on, having improved performance based on the convergence.

Convergence structure for control and data analytics systems

A system and method obtain a database stored on a storage device containing information on multiple assets, the information including measurements taken from devices monitoring each asset, and context information corresponding to the environment the items are subjected to. The system and method groups assets via a computer system into a homogenous group as a function of selected context information and performs analytics via the computer system on the grouped assets to manage the assets.

System and method for performance monitoring of a population of equipment

A computer implemented method includes providing information related to wind speed and direction prediction to a wind turbine energy prediction module, providing wind energy production prediction information to a resource allocation engine, and combining the wind energy production prediction information with information about non-renewable energy resources to provide an output identifying energy production resources to use for optimal load servicing.

Energy resource allocation including renewable energy sources

A method for online condition-based monitoring (CBM) of a tank farm including a plurality of storage tanks includes providing a tank model including a diagnostic and/or predictive tank model based on calculated tank metrics that is derived from historical data including tank operation data. The calculated tank metrics include tank operational metrics based on the tank operational data for the storage tanks and tank condition metrics based on tank inspection or maintenance data for the storage tanks. The tank model provides relationships between the tank condition metrics and the tank operational metrics. Results are generated using the tank model including at least one failure indicia for at least a first of the storage tanks using the calculated tank metrics and current measured data for the first tank as inputs to the tank model. The failure indicia is processed for scheduling at least one maintenance task for the first tank.

Online condition-based monitoring for tank farms

A method () for automatically checking proposed adjustments by an individual to at least one parameter at a manufacturing plant includes () creating a proposed vector including at least one proposed entry from the individual. The proposed vector represents a proposed adjustment to the parameter. A database of historical vectors including the parameter is searched relative to the proposed vector, wherein a number &#x2267;0 of similar stored vectors is identified in the searching based on respective distances (d) relative to the proposed vector (). If the number &#x2267;1, a distance measure R is calculated from the (d) for the similar stored vector(s) to indicate of how far or close the said proposed vector is to the similar stored vector (). An alert is provided if the number=0 or R is above a predetermined threshold value (). The method can be used for validation of proposed production planning parameters or process control parameters.

Automated system for checking proposed human adjustments to operational or planning parameters at a plant

A method for automatically monitoring the performance of equipment includes compiling current operating conditions associated with current conditions (PMc). A historical database including a plurality of stored operating conditions and associated stored performance measure (PM*) is searched, each stored operating condition including at least one stored sensor reading, wherein at least one similar operating condition is identified in the search using distances between the current operating conditions and the stored operating conditions. The performance measure (PM*) associated with the similar operating condition is fit to generate a regression model. The regression model is applied to the current operating condition to generate an estimate for the performance measure for the current operating condition (PMe). A difference between PMc and PMe is computed. The first difference is compared to a predetermined threshold, wherein a warning is automatically generated if the first difference has a value greater than the predetermined threshold.

System, method and algorithm for data-driven equipment performance monitoring

Deviations associated with a process system are identified, such as by identifying deviations of process variables or production measurements from target values. A graphical display is generated that identifies at least some of the deviations and one or more characteristics associated with the deviations. For example, the graphical display includes a time axis, and each deviation is associated with a bar positioned in the graphical display to identify a start time and an end time of the deviation along the time axis. Also, each bar may include one or more indicators (such as patterns, colors, or shadings) that identify the one or more characteristics (such as an economic impact and/or a cause) of each deviation identified in the graphical display. In this way, a user can, among other things, identify when deviations from target values occur, durations of the deviations, and economic impacts and/or causes associated with the deviations.

Apparatus and method for visualization of operating status in a process control system

Methods (), algorithms, and implementing systems () for guided annotation of process deviations by an individual responsible for annotations (e.g. process operator) for a manufacturing process having an associated computing system () including a historical database (stored in memory). The historical database is provided () including data associated with a plurality of process performance measures including stored deviations for the plurality of process measures, stored quantitative deviation attributes thereof and at least one cause associated with each of the stored deviations process performance measures. The method includes the individual responsible for annotation selecting at least one present deviation (D) to be annotated responsive to a deviation in a target process performance measure (T) selected from the plurality of process measures (). Automatically, the stored quantitative attributes associated with D, T, and a target value or target interval for T are retrieved (). The database is searched to identify a plurality of similar deviations from the stored deviations determined to be similar to D and the plurality of similar deviations are statistically processed to generate a listing indicating a relative likelihood of the at least one cause associated with the plurality of similar deviations (). The listing is presented to the individual to prompt selection of at least one present cause for D from the cause(s) associated with the plurality of similar deviations ().

Guided entry system for individuals for annotating process deviations

In a process control system, the hidden properties of a catalyst are estimated by including those properties in hidden states within a state space model and solving the state space model based on measurable inputs and outputs of the process. The process may include defining a state space model for a process having a catalyst state comprising a hidden catalyst property; defining a set of empirically measurable input variables for the state space model, defining a set of output variables for the state space model, measuring a set of input values corresponding to the set of input variables; measuring a set of output values corresponding to the set of output variables; and estimating the hidden catalyst property based on the input values, the output values, and the state space model.

Methods and apparatus for process control using catalyst state estimation

The subject mater herein relates to oil well testing and, more particularly, automated oil well test classification. Various embodiments described herein provide systems, methods, and software for statistical analysis and classification of oil well tests. Some embodiments include receiving a first set of oil well test results from one or more measurement devices of a well test separator, storing the first set of oil well test results in a database, and annotating one or more tests of the first set oil well test results. The annotated test results are then used to build one or more classification models to enable automated oil well test classification as new oil well tests are performed.

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Prague, Czechia

Petr Stluka