Diagnostic Method for a Process Automation System

• US 20110145180A1
• Filed: 07/02/2009
• Published: 06/16/2011
• Est. Priority Date: 08/08/2008
• Status: Abandoned Application

• Current Assignee: Endress + Hauser GmbH + Company KG (Endress+Hauser AG)
• Original Assignee: Endress + Hauser GmbH + Company KG (Endress+Hauser AG)
• Inventors: Muller, Alexander
• Application Number:
  US13/058,050
• Publication Number:
  US 20110145180A1
• Time in Patent Office: Days
• US Class Current: 706/23
• CPC Class Codes: G05B 19/042 using digital processors G05B19/05 takes precedence
• CPC Class Codes: G05B 2219/21002 Neural classifier for inputs, groups inputs into classes
• CPC Class Codes: G05B 2219/24048 Remote test, monitoring, diagnostic
• CPC Class Codes: G05B 2219/25255 Neural network
• CPC Class Codes: G05B 2219/25428 Field device
• CPC Class Codes: G05B 2219/33037 Learn parameters of network offline, not while controlling system

Abstract

A diagnostic method for a process automation system composed of at least one field device, a control unit, and at least one fieldbus. The following method steps are included: in a learning phase raw data of measured variables, raw data of manipulated variables and/or raw data of state variables are stored. The field devices or the processes as input variables are registered and stored normalized. Moreover, in the learning phase at least one parameter, a measuring condition, at least one parameter of a process state and/or at least one parameter of a field device state as output variable predetermines, which corresponding output variables are the input variables associated with that which is stored, during the learning phase. A neural network formed by the input variables and the associated with output variables, in the learning phase are the causal relationships between the ascertained input variables and the corresponding, specified output variables by a transfer function of the neural network ascertained and stored, in an operating phase is by means of the transfer function from the current raw data the field devices as input variables at least one change of the current measuring condition, the current process state and/or the current field device state ascertained.

Claims

1-12. -12. (canceled)

13. A diagnostic method for a process automation system comprising at least one field device, a control unit, and at least one fieldbus, the method comprising the steps of:

in a learning phase, raw data of measured variables, raw data of manipulated variables and/or raw data of state variables of the field devices or the processes are registered as input variables and stored normalized;

in the learning phase, moreover a user specifies at least one parameter of a measuring condition, at least one parameter of a process state and/or at least one parameter of a field device state as an output variable;

corresponding output variables are stored associated with input variables,

during the learning phase, the input variables and the associated output variables are fed to a neural network;

in the learning phase, causal relationships between the ascertained input variables and the corresponding, specified output variables are ascertained by a transfer function of the neural network and stored; and

,

in an operating phase, by means of the transfer function, from current raw data of the field devices as input variables, at least one change of a current measuring condition, a current process state and/or a current field device state is ascertained.

14. The diagnostic method as claimed in claim 13, wherein:

the diagnostic method is performed automatically in the field devices and results of diagnosis are transmitted to the control unit and/or other field devices.

15. The diagnostic method as claimed in claim 13, wherein:

the diagnostic method is performed in the control unit by transmitting raw data of the field devices via the fieldbus and by transmitting parameters likewise via the fieldbus or by inputting parameters directly at an input/output unit of the control unit.

16. The diagnostic method as claimed in claim 13, wherein:

the neural network stores causal relationships between ascertained input variables and corresponding, specified output variables in at least one transfer function.

17. The diagnostic method as claimed in claim 13, wherein:

in the learning phase, periodic registering of raw data as input variables and periodic specification of parameters as output variables are performed simultaneously.

18. The diagnostic method as claimed in claim 13, wherein:

the parameters are quantified by the user by gradual estimation of field device state, process state and/or measuring condition.

19. The diagnostic method as claimed in claim 18, wherein:

the parameters of the output variables are specified by the user in a range of 1 to 10.

20. The diagnostic method as claimed in claim 13, wherein:

limit values of the parameters are specified, by means of which validity of input variables, a critical measuring condition, a critical field device state and/or a critical process state is established.

21. The diagnostic method as claimed in claim 13, wherein:

the raw data of measured variables, of manipulated variables and/or of state variables of the different field devices of the same process are classified.

22. The diagnostic method as claimed in claim 21, wherein:

based on a comparison of change in classified raw data of measured variables, manipulated variables and/or of state variables of the field devices, a cause and/or a measure for the change of a current measuring condition, a current process state and/or a current field device state is ascertained.

23. The diagnostic method as claimed in claim 13, wherein:

the specification of the parameters by the user is performed by menu guided input via an in/output unit.

24. The diagnostic method as claimed in claim 13, wherein:

the learning phase is performed at start-up of the field device and/or the process.