Diagnostic System for a Valve that can be Actuated by a Control Pressure

Abstract

A diagnostic system for a valve that can be actuated by a control pressure includes a pressure sensor measuring the control pressure, a position sensor detecting the valve position, and an artificial neural network configured to describe a valve signature in the form of the control pressure-valve position correlation over the entire control range of the valve and to update the position correlation during the ongoing operation of the valve based on the measured control pressure and the detected valve position.

Claims

1.-6. (canceled)

7. A diagnostic system for a valve which is actuatable via a control pressure, the diagnostic system comprising: a pressure sensor which measures the control pressure; a position sensor which detects the valve position and an artificial neural network in a diagnostic unit which is configured to construct a valve signature comprising a control pressure-valve position dependency over an entire operating range of the valve and configured to update said control pressure-valve position dependency during operation of the valve based on the measured control pressure and the detected valve position; and a temperature sensor which measures a temperature of the valve; wherein the artificial neural network is configured to obtain the detected control pressure as an input variable, to generate an estimated value of the valve position as an output variable and is configured to be trained, dependent upon a deviation between the output variable and the detected valve position and at least one of (i) the measured temperature of the valve and (ii) surroundings of the temperature sensor; and wherein the artificial neural network is configured to obtain the measured temperature as an additional input variable.

8. The diagnostic system as claimed in claim 7, wherein the neural network is further configured to obtain a direction of change of the valve position as an additional input variable.

9. The diagnostic system as claimed in claim 7, wherein the neural network consists of two partial networks which are configured to construct and update a valve signature for different directions of change of the valve position.

10. The diagnostic system as claimed claim 7, further comprising: a memory store for storing a valve signature acquired with an intact valve; and an evaluating device which configured to, through a comparison of the current valve signature constructed by the neural network with the stored valve signature, make and output a diagnostic prediction regarding the valve.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The invention is described below using exemplary embodiments and making reference to the figures of the drawing, in which:

[0022] FIG. 1 shows a valve with an actuator, a positioner and a diagnostic system in accordance with the invention;

[0023] FIG. 2 shows a graphical plot of a valve signature;

[0024] FIG. 3 shows a neural network as a component of the diagnostic system in accordance with the invention; and

[0025] FIG. 4 shows an embodiment of the neural network consisting of two partial networks in accordance with the invention.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

[0026] The same reference characters have the same meaning in the different figures. The illustrations are purely schematic and do not show any size relationships.

[0027] FIG. 1 shows a valve (e.g., a globe valve) 1 which, via a corresponding stroke of a closing element 3 cooperating with a valve seat 2, controls the passage of a medium 4. The stroke is generated by a pneumatic drive 5 and is transmitted via a valve stem 6 to the closing element 3. The drive 5 is connected via a yoke 7 to the housing of the valve 1. Mounted on the yoke 7 is an electropneumatic positioner 8 that detects the valve position s at the input side via a position sensor 9 engaging with the valve stem 6, compares this detects the valve position s with a target value s* fed via a data interface from a field bus and controls the pneumatic drive 5 via a compressed air output 10 in the context of a correction of the system deviation.

[0028] The pneumatic drive 5 shown here is a single-acting diaphragm drive with spring return and a drive chamber 11. The drive chamber 11 is fed with or bled of air by the positioner 8 so that a control pressure p is generated therein, which acts against the force of a spring 12 on a diaphragm 13 connected to the valve stem 6. Alternatively, a double-acting drive can be used in conjunction with a double-acting positioner that generates two counteracting control pressures on the two sides of the diaphragm 13. Furthermore, in place of a membrane drive, a pivot drive can be provided if, in place of a linear stroke movement, a rotary movement is to be generated for the valve (e.g., a ball valve or flap valve).

[0029] A diagnostic unit 14 obtains, as input signals, the valve position s detected by the position sensor 9, the control pressure p measured by a pressure sensor 15 and the temperature T measured by a temperature sensor 16 on the drive 5. As described further below, the diagnostic unit 14 determines a current signature of the valve 1 from the input signals fed via a neural network 17. A starting signature of the intact valve 1 is stored in a memory store 18. An evaluating device 19 serves to compare a currently determined valve signature with the starting signature and, based on typical deviations, to diagnose errors such as wear, breakage of the return spring 12 and/or non-sealing closing of the valve 1.

[0030] FIG. 2 shows an example of the valve signature 20 in the form of the control pressure p during feeding 21 and bleeding 22 of the pneumatic drive 5 dependent upon the valve position s. The value s=0% represents the completely closed valve 1 and the value s=100% represents the open valve 1.

[0031] FIG. 3 shows an example of the neural network 17 that obtains the detected control pressure p, the measured temperature T and the current direction of action dir as input variables and generates an estimated value ŝ for the valve position s as an output variable. The direction of action dir states in which of the two directions the valve 1 is currently being actuated and whether the pneumatic drive 5 is currently being fed or bled. The neural network 17 was pre-trained with the starting signature of the intact valve 1.

[0032] The neural network 17 shown is a feed-forward regression network that has an input layer with an input element 23 for each of the input variables p, T, dir. The input variables p, T, dir are fed to the neural network 17 only when the valve 1 is at rest and not being moved. The positioner 8 can contain, for example, a piezo valve unit 24 that converts control signals 26 obtained by a controller 25 dependent upon the target-actual comparison s*-s into pneumatic positioning increments, where compressed air present at a supply air connection 27 is dosed into the drive chamber 11 or it is bled via a venting connection 28. The input variables p, T, dir can thus be fed to the neural network 17 in the pauses between the control signals 20. Two hidden layers each consisting of a plurality of neurons 29 or 30 are arranged downstream of the input layer. The input variables p, T, dir are provided in each neuron 29 of the first hidden layer with individual weighting factors w.sub.ij and are summed to a response of the relevant neuron 29. The responses of the neurons 29 of the first hidden layer are provided in each neuron 30 of the second hidden layer with individual weighting factors w.sub.ij and are summed to a response of the relevant neuron 30. An output element 31 that sums the responses of the neurons 30, each with an individual weighting factor w.sub.jk, to the estimated value ŝ for the valve position is arranged downstream of the second hidden layer. In order to adapt the neural network 17 to changes in the valve behavior and to learn the relationship that is to be reproduced between the control pressure p and the valve position s (valve signature), the weighting factors w=w.sub.ij, w.sub.jk, w.sub.k of the neural network 17 are changed with the aid of adaptation algorithms 32 in the context of a reduction of the error Δs=s−ŝ between the estimated value ŝ of the valve position supplied by the neural network 17 and the measured valve position s.

[0033] In order to be able to estimate the trustworthiness of the learned signature 20, the frequencies of the valve positions s visited can be determined. If, for example, the valve 1 is mostly moved between s=70% and s=90%, then the learned signature 20 in this region is more trustworthy than outside thereof. Occasionally, however, e.g., on initialization, the valve 1 is always also moved over the full positioning path.

[0034] In the example shown in FIG. 4, the neural network 17 consists of partial networks 33, 34 for the two different directions of change dir of the valve position s. Dependent upon the direction of change dir, the input variables p, T are fed via a switchover device 35 to either one or the other of the two partial networks 33, 34 that supply the different estimates ŝ1, ŝ2 of the valve position s for the two directions of change dir.

[0035] Thus, while there have been shown, described and pointed out fundamental novel features of the invention as applied to a preferred embodiment thereof, it will be understood that various omissions and substitutions and changes in the form and details of the devices illustrated, and in their operation, may be made by those skilled in the art without departing from the spirit of the invention. For example, it is expressly intended that all combinations of those elements which perform substantially the same function in substantially the same way to achieve the same results are within the scope of the invention. Moreover, it should be recognized that structures and/or elements shown and/or described in connection with any disclosed form or embodiment of the invention may be incorporated in any other disclosed or described or suggested form or embodiment as a general matter of design choice. It is the intention, therefore, to be limited only as indicated by the scope of the claims appended hereto.