Fisher-rosemount Systems, Inc

Fisher Controls International LLC

Peter Zornio

Mark J Nixon

Eric Darrell Rotvold

Terrence L Blevins

Wilhelm K Wojsznis

Paul Richard Muston

Gary Keith Law

J Michael Lucas

Duncan Schleiss

Kenneth William Junk

Trevor Duncan Schleiss

Robert J Karschnia

Neil J Peterson

Annette Lynn Latwesen

Michael J Lucas

Andre Arthur Dicaire

David P Imming

Annette L Latwesen

Techniques for automatically or autonomously performing signal processing-based learning in a process plant are disclosed. Generally, said techniques automatically or autonomously perform signal processing on a real-time signal that is generated based on the process plant controlling a process. Typically, the signal corresponds to a parameter value that varies over time, and the signal is processed as it is generated in real-time during on-line plant operations. Results of the signal processing may indicate characteristics of the signal, and one or more analytics functions may determine the sources of the characteristics, which may include a process element or device, a piece of equipment, and/or an asset of the process plant that is upstream, within the process, of the source of the signal. An autonomous signal processor may be integrated with or included in a process control device and/or a big data node of the process plant.

Automatic signal processing-based learning in a process plant

A data pipeline is used as a fundamental processing element for implementing techniques that automatically or autonomously perform signal processing-based learning in a process plant or monitoring system. Each data pipeline includes a set of communicatively interconnected data processing blocks that perform processing on one or more sources of data in a predetermined order to, for example, clean the data, filter the data, select data for further processing, perform supervised or unsupervised learning on the data, etc. The individual processing blocks or modules within a data pipeline may be stored and executed at different devices in a plant network to perform distributed data processing. Moreover, each data pipeline can be integrated into one or more higher level analytic modules that perform higher level analytics, such as quality prediction, fault detection, etc. on the processed data. The use of data pipelines within a plant network enables data collected within a plant control or monitoring system to be processed automatically and used in various higher level analytic modules within the plant during ongoing operation of the plant.

Data pipeline for process control system analytics

A regional big data node oversees or services, during real-time operations of a process plant or process control system, a respective region of a plurality of regions of the plant/system, where at least some of the regions each includes one or more process control devices that operate to control a process executed in the plant/system. The regional big data node is configured to receive and store, as big data, streamed data and learned knowledge that is generated, received, or observed by its respective region, and to perform one or more learning analyses on at least some of the stored data. As a result of the learning analyses, the regional big data node creates new learned knowledge which the regional big data node may use to modify operations in its respective region, and/or which the regional big data node may transmit to other big data nodes of the plant/system.

Regional big data in process control systems

An asset tracking system for use in a process control environment may include one or more asset tracking devices and an asset tracking host. The asset tracking device may receive signals corresponding to a position or location of an asset to be tracked, and may communicate, using an industrial automation protocol such as wireless HART, an indication of the position to the asset tracking host. The signals may be GPS signals that are re-radiated into the process control environment. Other information, such as environmental data, may be communicated in conjunction with the position of the asset. The asset tracking host may store and/or display the data or information included in the message, and may send a different message to the asset tracking device. An asset tracking device may be included in a field device, a network device, or a portable communications device used in the process control environment.

Asset tracking in process control environments

The present disclosure relates to various assets utilized within manufacturing and process plants for monitoring and control purposes. The asset data modules of the present disclosure include an integral near field communications (NFC) interface configured to provide access to asset data stored within memory integral to the given asset.

Asset data modules including an integral near field communication interface

An example method to identify a hazardous process control type associated with a process control device includes automatically detecting that a process control device is coupled to a port on a controller, detecting information associated with the process control device via the port, identifying a hazardous process condition type of the process control device based on the detected information, and sending information to a computing device to cause a display to include visual indication of the identified hazardous process condition type in association with the process control device.

Methods, apparatus, and articles of manufacture to identify hazardous process conditions associated with devices in a process control system

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Austin, TX, United States of America

Peter Zornio