METHOD OF DETERMINING THE TIME INTERVAL UNTIL A SERVICE ACTION IS REQUIRED

Abstract

Disclosed is a method of determining the remaining time interval until a measurement characteristic of a field device will have drifted outside of a predetermined tolerance range and a service action is required. The method includes predetermining a maximum tolerance of the measurement characteristic correlated/related to the measuring performance of the field device; registering continuously the measurement characteristic of the field device; estimating a lag time interval wherein the estimated lag time interval depends on the drift of the measurement characteristic of the field device in the process specific application; using a method of Artificial Intelligence to determine, at the end of the estimated lag time interval, the remaining time interval until the measurement characteristic of a field device will have drifted outside the predetermined maximum tolerance; and generating a message informing of the remaining time interval until the service action is required.

Claims

1. A method of determining the remaining time interval until a measurement characteristic of a field device will have drifted outside of a predetermined tolerance range and a service action is required, wherein the field device is measuring or monitoring at least one process variable of a medium in a process-specific application of automation technology, comprising the steps of: predetermining a maximum tolerance of the measurement characteristic correlated or related to a measurement performance of the field device, wherein the measurement performance of the field device in the process-specific application is unacceptable if the measurement characteristic reaches or exceeds the predetermined maximum tolerance; registering continuously the measurement characteristic of the field device; estimating a lag time interval, wherein the estimated lag time interval depends on a drift of the measurement characteristic of the field device in the process specific application; using a method of Artificial Intelligence to determine, at the end of the estimated lag time interval, a remaining time interval until the measurement characteristic of the field device will have drifted outside the predetermined maximum tolerance; and generating a message informing of the remaining time interval until the service action is required.

2. The method as claimed in claim 1, further comprising the step of: estimating the lag time interval based on continuously registered data related or correlated to the measurement characteristic of the field device during a past time interval.

3. The method as claimed in claim 1, further comprising the step of: estimating the lag time interval of the field device in the process specified application based on expertise.

4. The method as claimed in claim 1, further comprising the step of: generating a status message indicating that the measurement performance of the field device is okay when the measurement characteristic of the field device, because of a drift, will not reach the maximum tolerance within a first subsequent time interval, wherein the first subsequent time interval is k.sub.1 times the lag time interval, and wherein k.sub.1>=1.

5. The method as claimed in claim 4, further comprising: generating a warning status message indicating that the measurement performance of the field device is decreasing, if the measurement characteristic of the field device, because of a drift, will reach the maximum tolerance within a second subsequent time interval following the first subsequent time interval, wherein the second subsequent time interval is k.sub.2 times the lag time interval, and wherein 0<k.sub.2<k.sub.1.

6. The method as claimed in claim 5, further comprising: generating a critical status message indicating that the measuring performance of the field device is close to the maximum tolerance, if the measurement characteristic of the field device, because of a drift, will reach the maximum tolerance within a portion of a subsequent time interval in the size smaller than k.sub.2 times the lag time interval.

7. The method as claimed in claim 1, further comprising the step of: generating a message of failure if the measurement characteristic of the field device has reached or exceeded the maximum tolerance at the end of the lag time interval.

8. The method according to claim 1, further comprising the steps of: generating at least one status message; and determining a confidence level of the at least one generated status message of the field device wherein the confidence level provides information about a reliability of the determined remaining time interval.

9. The method as claimed in claim 8, further comprising the step of: using a non-linear transformation of the determined remaining time interval relative to the lag time interval to determine the confidence level.

10. The method as claimed in claim 8, further comprising the step of: providing the confidence level as a percentage, wherein a percentage close to 0% indicates a low confidence level and a percentage close to 100% indicates a high confidence level.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present disclosure and refinements of the present disclosure will now be explained in greater detail by the following figures.

[0043] FIG. 1 shows a schematic view of a communication network of an automation system,

[0044] FIG. 2 shows a diagram of measurement characteristic over time of a field device being in an Okay state,

[0045] FIG. 3 shows a diagram of measurement characteristic over time of a field device being in a warning state,

[0046] FIG. 4 shows a diagram of measurement characteristic over time of a field device being in a warning state,

[0047] FIG. 5 shows a diagram of measurement characteristic over time of a field device being in a critical state, and

[0048] FIG. 6 shows a schematic view how the confidence level is determined.

[0049] FIG. 7 shows a schematic view of the confidence level of a message is generated.

DETAILED DESCRIPTION

[0050] FIG. 1 shows a schematic view of a communication network KN of an automation system including the most important components. Connected to a databus D1 are some computer units (workstations) WS1, WS2. Also, a portable service unit BT may be used, for example, a mobile phone, a PAD, or a Laptop, to visualize measurement data or to service the field devices FD.

[0051] Each of the computer units may serve as super-ordinated control unit for process visualization, process monitoring and for engineering, as well as for interacting with and monitoring field devices FD. Databus D1 works, for example, according to the Profibus DP standard or the HSE (High Speed Ethernet standard) of Foundation Fieldbus. Via a gateway G1 which acts as a linking device or segment coupler, databus D1 is connected to a fieldbus segment SM1. Fieldbus segment SM1 is composed of a plurality of field devices FD which are connected to a fieldbus FB. The field devices FD may include both sensors and actuators. The Fieldbus FB works according to one of the known fieldbus standards, Profibus, Foundation Fieldbus or HART. Wireless Communication is also possible. It is clear, that the present disclosure also relates to stand-alone field devices which communicate with a control unit via 4-20 mA, usually in combination with HART communication.

[0052] The inventive method determines/predicts the remaining time interval RTI until the measurement characteristic of a field device FD will have drifted outside of a predetermined maximum tolerance T and a service action is required. The field device FD is measuring or monitoring at least one process variable of a medium in a process specific application of automation technology. Usually, a field device FD installed in a process of automation technology must be serviced if the specified measuring accuracy of the field device FD can no longer be guaranteed. As already described before, the drift of the measurement characteristic of a field device FD can be slow or fast because of harsh environmental conditions or a creeping defect of a field device component, for example. According to the present disclosure the remaining time interval until the next service action is necessary, is predicted based on the behavior of the measurement characteristic of the field device FD in the past, especially during a predetermined lag time interval LTI. The lag time interval LTI is based on continuously registered data related or correlated to the measurement characteristic of the field device FD during a given time interval in the past. Alternatively, the lag time interval LTI of the field device may be determined based on expertise. This may be the case, for example, in special critical process specific applications.

[0053] n a next step the maximum tolerance of the measurement characteristic is predetermined, wherein the measuring performance of the field device FD in the process specific application is unacceptable if the measurement characteristic reaches or is outside the predetermined maximum tolerance. Next, the measurement characteristic of the field device is continuously registered. The lag time interval LTI is determined, wherein the estimated lag time interval LTI depends on the drift of the measurement characteristic of the field device FD during its previous operation in the process specific application;

[0054] Using a method of artificial intelligence to determine, at the end of the estimated lag time interval LTI the remaining time interval RTI until the measurement characteristic of a field device FD will have drifted outside the predetermined maximum tolerance T;

[0055] Generating a message informing of the remaining time interval RTI until the service action is required.

[0056] Furthermore, the proposes as a refinement that the operator is getting information about the reliability of the predicted remaining time interval RTI until the field device FD must be serviced. There are different status messages provided. These status messages are based on different drifting behavior of the measurement characteristic. Examples are shown in figures FIG. 2 to FIG. 5. In the figures there is always shown a positive drift. Of course, also a negative drift is possible.

[0057] FIG. 2 shows a diagram of a measurement characteristic over time of a field device FD being in an okay state. It is predicted that the remaining time interval RTI is 130 days. Assuming a factor k.sub.1=2 and a lag time of 20 days, the predicted remaining time RTI is beyond the subsequent time interval of 220 days=40 days. This leads to an Okay State and a confidence level CL of 95% for this prediction. The operator can safely let the field device FD run and wait until a measurement drift is confirmed in the future.

[0058] FIG. 3 shows a diagram of a measurement characteristic over time of a field device FD being in a warning state. It is predicted that the remaining time RTI interval is 24 days. Assuming the following set of factors k.sub.1=2; k.sub.2=0.1 and a lag time interval of 20 days, the predicted remaining time RTI is below the subsequent time interval of 220 days=40 days and beyond a portion of the subsequent time interval of 0.120 days=2 days. This constellation leads to a warning state with a calculated confidence level CL of 69%. Depending on the function the field device FD fulfills in the process, the operator can decide if the predicted remaining time interval RTI will be exhausted, or if a service action is initiated earlier.

[0059] FIG. 4 shows a diagram of a measurement characteristic over time of a field device being in a warning state. In this case a remaining time interval RTI of 2 days and 14 hours is predicted until the next service action must be done. Assuming the same settings as before, the field device FD still appears in a warning state, but there is now calculated a confidence level of 93%. This is a clear instruction to the operator to take immediately appropriate service measures.

[0060] FIG. 5 shows a diagram of a measurement characteristic over time of a field device FD which is in a critical state. Assuming still the same settings, the field device appears in a critical state as the remaining time interval RTI is 1 day and four hours an therefore less than a portion of the subsequent time interval defined as 0.120 days=2 days. The confidence CL is highabout 94%. A service action must be initiated immediately. In such a critical situation an alarm is set.

[0061] As already mentioned, the inventive method provides, besides the predicted remaining time interval RTI until the next service action is due, a confidence level CL providing information regarding the reliability of the predicted remaining time interval RTI. The determination of the confidence level CL is visualized in FIG. 6 and FIG. 7.

[0062] One of the non-linear transformations f(t), f.sub.1(t) is used to predict the confidence level CL of the remaining time interval RTI until the field device FD reaches the maximum tolerance T and therefore the limit of the measurement performance of the field device FD. The confidence level CF is preferably given as a percentage, wherein a percentage close to 0% indicates a low confidence level CF and a percentage close to 100% indicates a high confidence level CF. It is advantageous, if the confidence level CF is in the range of about 15% to 95%. The limits of the range can be arbitrarily set by the operator. In general, the lower range limit is related to the prediction method actually used in connection with the present disclosure, and the upper range limit is related to the measurement uncertainty of the confidence level CD.

[0063] As shown in FIG. 7, the application of the nonlinear transformation on the remaining time interval RTI has the effect that the confidence level CL decreases nearly linear in the initial range of the remaining time interval RTI, while it decreases disproportionately fast in the end range of the predicted remaining time interval RTI. The prognosis is much more difficult and uncertain the further it goes into the future.