PROMIND – Management modules

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Main management modules of PROMIND.

Displays each item below to read content: 

Models identification

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Based on the expert experience and the system’s self-learning, a mathematical model of the system is build enabling the setting of structured relations between input variables and output modes.

A wide range of toolboxes enables supporting a significant range of models:

  • To stimulate relations among variables.
  • Estimates the relation among variables when these are qualitative.
  • Statistical methods.
  • Differential equations: relations between variables and time.
  • Temporal-logic rules. To organize events and establish time restrictions.
  • Signal models. Analysis of recurring signals, stochastic signals, etc…

Model interpretation

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The analysis tools provided by PROMIND provide an environment to work over a mathematical model of the system, deeply understanding the behaviour of the actual system.

  • Knowledge of the significant variables and the existing relations.
  • Model comprehension.
  • Assessment as regards how output variables are affected by input variables.
  • Identification of existing correlations among events-variables-indicators.
  • Determination of whether the system’s response is similar in different periods of time or in different situations.
  • A forecast of how the output variables will evolve in the future: Type of quality, product limits, process requests, failure modes, etc…
  • Simulate how the system will behave (quality, process, failure modes) under different work conditions.
  • Determine what input variables introduce a larger variation in the system’s response and how it affects.
  • Identification of the ideal configuration (optimal functioning) establishing different restrictions to the input variables.

Run Time and online analysis

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The system’s model works as a “clone” of the real system, constantly comparing the foreseen functioning situation with the real situation, to contribute useful information to maintain the process within the established response ranges.

Real time identification of model deviations: Fault modes detection.

  • Processes control
  • Management by exception.
  • Automatic operation or rules recommendation.
  • Helps making decisions when facing incidences based on knowledge workflows.
  • Avoids improvisation through the application of previously identified and tested standard solutions.

Experimentation

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PROMIND enables the development of a scientific mind-set at the plant, thus the factory is considered a permanent laboratory where to experiment in order to find radical improvements that provide concrete benefits to current operations.

Once the system model has been trained, the user can experiment with it by inputting variables or taking it to ranges of unconventional functioning spaces:

  • Providing deep additional knowledge about the process behaviour, the relation between the input variables and the sensitivity between the input variables and the output modes.
  • Enabling the identification of new functioning ranges outside of conventional spaces where functioning optimisations can be obtained, facilitating the identification of potential radical improvements.

PROMIND turns into a support tool for the Classroom lab at the factory, decisively supporting the improvement cycles operation.

Knowledge and utilities distribution

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The information output generation features help to distribute the knowledge generated by PROMIND among different management environments. Different user roles support their decisions on the system:

  • Supervisor user: Responsible for identifying challenges. They are furnished with information related to the assessment of accomplished achievements.
  • Master user: Expert in statistical methods and system modelling. It’s also in charge of designing and developing utilities that will later be used by the organisation.
  • Engineer/Technical user: It’s the expert on the system/process. Collaborates with the master user to develop all the technology surrounding PROMIND. Key element in the optimization of and the proposal for improvements for the process.
  • Worker user: Consumer of the utilities developed by the master user which will help them in decision-making regarding the monitoring and control of the system/process.

 

 

Connectivity

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It incorporates user tools that enable a faster connectivity to other systems and data sources:

  • Data capture: Connectivity with SCADA, PLCs, digital equipment, gauges, sensors, etc.
  • Integration with CAPTOR and PRISMA
  • Integration with other manufacturers’ MES and CMMS
  • Digital and analogue inputs/outputs
  • Historical
  • Alarms and events

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