Abstract: A process plant data collection and organization system uses a common or integrated navigational tree structure to organize and enable a user to view and access information obtained from different applications or data sources within a process plant, even when those different applications have different manners of organizing or enabling users thereof to view the collected information. The integrated navigational tree may be developed from and may use the navigational tree categories of the different applications or may map the data within different categories of the navigational trees of the different applications into categories within the integrated navigational tree. The integrated navigational tree enables a user to view and access plant data collected or developed by different applications within the process plant in a single place and in an organized manner so that the user can view data related to the plant from a higher point of view than provided by any of the individual applications.

Abstract: Detection of one or more abnormal situations is performed using various statistical measures, such as a mean, a median, a standard deviation, etc. of one or more process parameters or variable measurements made by statistical process monitoring blocks within a plant. This detection is enhanced in various cases by using specialized data filters and data processing techniques, which are designed to be computationally simple and therefore are able to be applied to data collected at a high sampling rate in a field device having limited processing power. The enhanced data or measurements may be used to provided better or more accurate statistical measures of the data, may be used to trim the data to remove outliers from this data, may be used to fit this data to non-linear functions, or may be use to quickly detect the occurrence of various abnormal situations within specific plant equipment, such as distillation columns and fluid catalytic crackers.

Abstract: Detection of one or more abnormal situations is performed using various statistical measures, such as a mean, a median, a standard deviation, etc. of one or more process parameters or variable measurements made by statistical process monitoring blocks within a plant. This detection is enhanced in various cases by using specialized data filters and data processing techniques, which are designed to be computationally simple and therefore are able to be applied to data collected at a high sampling rate in a field device having limited processing power. The enhanced data or measurements may be used to provided better or more accurate statistical measures of the data, may be used to trim the data to remove outliers from this data, may be used to fit this data to non-linear functions, or may be use to quickly detect the occurrence of various abnormal situations within specific plant equipment, such as distillation columns and fluid catalytic crackers.

Abstract: Detection of one or more abnormal situations is performed using various statistical measures, such as a mean, a median, a standard deviation, etc. of one or more process parameters or variable measurements made by statistical process monitoring blocks within a plant. This detection is enhanced in various cases by using specialized data filters and data processing techniques, which are designed to be computationally simple and therefore are able to be applied to data collected at a high sampling rate in a field device having limited processing power. The enhanced data or measurements may be used to provided better or more accurate statistical measures of the data, may be used to trim the data to remove outliers from this data, may be used to fit this data to non-linear functions, or may be use to quickly detect the occurrence of various abnormal situations within specific plant equipment, such as distillation columns and fluid catalytic crackers.

Abstract: A system for visually presenting data receives signal processing data generated signal processing data collection blocks corresponding to devices associated with a process plant. The signal processing data collection blocks may generate data such as statistical data, frequency analysis data, auto regression data, wavelets data, etc. The system displays an image representative of the devices and representative of a context of the devices within the process plant. Additionally, data based on signal processing data corresponding to one or more devices is displayed. For example, the signal processing data for the device could be displayed. As another example, data may be generated based on the signal processing data and this generated data may be displayed.

Abstract: A process variable transmitter with diagnostics based on power spectral density (PSD) analysis of a process variable sensor signal is provided. In one embodiment, the process variable transmitter is a pressure transmitter and the diagnostics are used to diagnose impulse line obstruction or impending obstruction. Other diagnostics are also useful such as diagnosing primary element degradation. The sensor signal is digitized and the digitized signal is transferred into the frequency domain. The power of the frequencies on the sensor signal is examined to provide the enhanced diagnostics. In one aspect diagnostics are generated directly with the sensor PSD data. In another aspect, the PSD analysis is used to tune a filter in order to enhance traditional diagnostic algorithms.

Abstract: Detection of one or more abnormal situations is performed using various statistical measures, such as a mean, a median, a standard deviation, etc. of one or more process parameters or variable measurements made by statistical process monitoring blocks within a plant. This detection is enhanced in various cases by using specialized data filters and data processing techniques, which are designed to be computationally simple and therefore are able to be applied to data collected at a high sampling rate in a field device having limited processing power. The enhanced data or measurements may be used to provided better or more accurate statistical measures of the data, may be used to trim the data to remove outliers from this data, may be used to fit this data to non-linear functions, or may be use to quickly detect the occurrence of various abnormal situations within specific plant equipment, such as distillation columns and fluid catalytic crackers.

Abstract: Detection of one or more abnormal situations is performed using various statistical measures, such as a mean, a median, a standard deviation, etc. of one or more process parameters or variable measurements made by statistical process monitoring blocks within a plant. This detection is enhanced in various cases by using specialized data filters and data processing techniques, which are designed to be computationally simple and therefore are able to be applied to data collected at a high sampling rate in a field device having limited processing power. The enhanced data or measurements may be used to provided better or more accurate statistical measures of the data, may be used to trim the data to remove outliers from this data, may be used to fit this data to non-linear functions, or may be use to quickly detect the occurrence of various abnormal situations within specific plant equipment, such as distillation columns and fluid catalytic crackers.

Abstract: A device for diagnosing operation of an industrial process control or monitoring system includes an input configured to receive an input related to a process signal. A first statistical parameter module provides a first statistical parameter output related to a statistical parameter of the process signal. A filter provides a filter output related to a filtered value of the process signal. A second statistical parameter module provides a second statistical parameter output related to a statistical parameter of the filter output. A diagnostic module diagnoses operation of the industrial process based upon the first and second statistical parameters.

Abstract: A system for gathering data associated with a process plant, in which parameters are generated by a plurality of signal processing data collection blocks, automatically determines parameters to be monitored. The signal processing data collection blocks may generate data such as statistical data, frequency analysis data, auto regression data, wavelets data, etc. Then, the system monitors the determined parameters.

Abstract: A system for detecting abnormal situations associated with a heater in a process plant receives statistical data associated with the heater unit. The statistical data is analyzed to detect whether one or more abnormal situations associated with the heater exist. The statistical data may comprise statistical data generated based on pressure variables sensed by one or more pressure sensors associated with a furnace, a stack, a fuel supply, etc., associated with the heater. Additionally, the statistical data may comprise statistical data generated based on temperature variables sensed by one or more temperature sensors associated with the furnace, the stack, etc. If an abnormal situation is detected, an indicator of the abnormal situation may be generated.

Abstract: Detection of one or more abnormal situations is performed using various statistical measures, such as a mean, a median, a standard deviation, etc. of one or more process parameters or variable measurements made by statistical process monitoring blocks within a plant. This detection is enhanced in various cases by using specialized data filters and data processing techniques, which are designed to be computationally simple and therefore are able to be applied to data collected at a high sampling rate in a field device having limited processing power. The enhanced data or measurements may be used to provided better or more accurate statistical measures of the data, may be used to trim the data to remove outliers from this data, may be used to fit this data to non-linear functions, or may be use to quickly detect the occurrence of various abnormal situations within specific plant equipment, such as distillation columns and fluid catalytic crackers.

Abstract: A system for visually presenting data receives signal processing data generated signal processing data collection blocks corresponding to devices associated with a process plant. The signal processing data collection blocks may generate data such as statistical data, frequency analysis data, auto regression data, wavelets data, etc. The system displays an image representative of the devices and representative of a context of the devices within the process plant. Additionally, data based on signal processing data corresponding to one or more devices is displayed. For example, the signal processing data for the device could be displayed. As another example, data may be generated based on the signal processing data and this generated data may be displayed.

Abstract: A system for visually presenting data receives signal processing data generated signal processing data collection blocks corresponding to devices associated with a process plant. The signal processing data collection blocks may generate data such as statistical data, frequency analysis data, auto regression data, wavelets data, etc. The system displays an image representative of the devices and representative of a context of the devices within the process plant. Additionally, data based on signal processing data corresponding to one or more devices is displayed. For example, the signal processing data for the device could be displayed. As another example, data may be generated based on the signal processing data and this generated data may be displayed.

Abstract: A method of monitoring an entity within a process plant wherein the entity includes a plurality of lower level entities, includes acquiring a plurality of use indices, acquiring a plurality of weighting values, and creating an aggregate use index from a combination of the lower level use indices and weighting values, wherein the aggregate use index represents status information regarding the entity. Each use index pertains to status information regarding one of the plurality of lower level entities, and each weighting value pertains to the importance of a lower level entity among the plurality of lower level entities.

Abstract: In one embodiment, a pressure transmitter is provided which diagnoses the condition of a primary element and/or an impulse line which connects to a pressure sensor. A difference circuit coupled to the pressure sensor has a difference output which represents the sensed pressure minus a moving average. A calculate circuit receives the difference output and calculates a trained output of historical data obtained during an initial training time. The calculate circuit also calculates a monitor output of current data obtained during monitoring or normal operation of the transmitter. A diagnostic circuit receives the trained output and the monitor output and generates a diagnostic output indicating a current condition.

Abstract: A method of monitoring an entity within a process plant wherein the entity includes a plurality of lower level entities, includes acquiring a plurality of use indices, acquiring a plurality of weighting values, and creating an aggregate use index from a combination of the lower level use indices and weighting values, wherein the aggregate use index represents status information regarding the entity. Each use index pertains to status information regarding one of the plurality of lower level entities, and each weighting value pertains to the importance of a lower level entity among the plurality of lower level entities.

Abstract: A device for diagnosing operation of an industrial process control or monitoring system includes an input configured to receive an input related to a process signal. A first statistical parameter module provides a first statistical parameter output related to a statistical parameter of the process signal. A filter provides a filter output related to a filtered value of the process signal. A second statistical parameter module provides a second statistical parameter output related to a statistical parameter of the filter output. A diagnostic module diagnoses operation of the industrial process based upon the first and second statistical parameters.

Abstract: An industrial process diagnostic apparatus is provided which can identify a source, or “root cause”, of an aberration in an industrial process. A plurality of process configuration models are provided which each represent a physical (or actual) implementation of an industrial process. One of the plurality of models is selected and diagnostics performed on the process using the selected model and at least one process signal related to the process. Based upon the diagnostics, a root cause of the aberration is determined.

Abstract: A system detects abnormal conditions associated with a process plant. The system receives signal processing data generated by signal processing data collection blocks implemented by a plurality of devices. The signal processing data collection blocks may generate data such as statistical data, frequency analysis data, auto regression data, wavelets data, etc. The system includes an analysis engine configured to detect at least one abnormal condition associated with the process plant. The signal processing data is provided to the analysis engine, and the analysis engine determines if an action should be taken.