MONITORING FOR INVALID DATA FROM FIELD INSTRUMENTS

• US 20120303321A1
• Filed: 05/26/2011
• Published: 11/29/2012
• Est. Priority Date: 05/26/2011
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Vass, Jiri, Schie, Ard Van
• Granted Patent: US 9,009,001 B2
• Time in Patent Office: Days
• US Class Current: 702/179
• CPC Class Codes: G05B 23/0221 Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
• CPC Class Codes: G05B 9/02 electric

Abstract

Processing systems and methods used to identify faults in the operation of one or more field instruments are described and shown. In one embodiment, a sensing system includes one or more field instruments and a processing component configured to process data from the field instruments. This processing may include identifying invalid samples of data using an algorithm; correlating invalid samples of data to specific field instruments; and determining a likelihood of a fault occurring on the specific field instruments. The processing may occur in real-time using an online processing technique, or with an offline processing technique on data maintained in a data store.

Claims

1. A sensing system comprising:

a field instrument;

a processing component operably coupled to the field instrument and configured to identify faults in operation of the field instrument, by;

processing data collected from the field instrument;

identifying an invalid sample of the data using an algorithm, the algorithm comprising;

performing a filtering operation on the data to generate filtered data;

computing a mean value M and standard deviation S from the filtered data;

computing an upper control limit and lower control limit using the mean value M and standard deviation S; and

identifying the invalid sample from the filtered data, the invalid sample providing one or more values greater than the upper control limit or lower than the lower control limit and satisfying a set of one or more empirical rules; and

determining a likelihood of a fault occurring in the field instrument from the invalid sample of the data.

2. The system of claim 1, further comprising:

a plurality of additional field instruments;

wherein the processing component is further configured to identify faults in operation of the field instrument or the plurality of additional field instruments by;

processing the data collected from the field instrument and the plurality of additional field instruments using an online process in real time; and

correlating the invalid sample of the data to one or more specific field instruments.

3. The system of claim 1, wherein the field instrument is selected from the group consisting of a product level gauge, temperature probe, water level probe, pressure transmitter, and flowmeter.

4. The system of claim 1, wherein the data measured by the field instrument includes one or more of product level, product temperature, product pressure, product flow, vapor temperature, vapor pressure, product density, water level, and weather data.

5. The system of claim 1, wherein the field instrument comprises an array of field instrument elements, and wherein the processing component is further configured for identifying a detected change and the likelihood of fault for a specific element in the array of field instrument elements by factoring one or more measurements obtained from field instruments external to the array of field instrument elements.

6. The system of claim 1, wherein the filtering operation is performed by one or more filters, the filters selected from a group consisting of a low-pass frequency filter, a high-pass frequency filter, a band-pass frequency filter, a band-stop frequency filter, a median-based non-linear filter, a percentile-based non-linear filter, a weighted averaging filter, a wavelet filter, a frequency-domain thresholding filter, a wavelet-domain thresholding filter, a cepstrum-domain thresholding filter, an adaptive linear filter, and an adaptive non-linear filter.

7. The system of claim 1, the algorithm further comprising computing one or more statistical indicators from the data and the filtered data;

wherein the set of empirical rules is applied to the data, the filtered data, and the one or more statistical indicators; and

wherein the set of empirical rules includes a rule requiring a difference between two or more successive samples of the data to exceed a threshold value, a rule requiring a difference between two or more successive samples of the filtered data to exceed a threshold value, a rule requiring one or more statistical indicators of the data to exceed a corresponding threshold value, and a rule requiring one or more statistical indicators of the filtered data to exceed a corresponding threshold value; and

wherein a threshold value is selected from the group consisting of;

a fixed threshold, a fuzzy threshold, a time-varying threshold, and an adaptive threshold.

8. The system of claim 1, the processing component further configured to:

report incidences of the invalid sample of the data to a user; and

notify a user after the likelihood of a fault exceeds a predefined threshold.

9. The system of claim 1, wherein determining a likelihood of a fault occurring in the field instrument includes factoring one or more of:

a status of the field instrument,

a previously determined likelihood of a fault for the field instrument, and

data collected from other field instruments located proximate to a location of the field instrument.

10. A processing system configured to process data collected from a plurality of field instruments, the processing system comprising:

a processor;

memory operably coupled to the processor;

instructions for execution within the processing system with use of the memory and the processor, the instructions configured to;

process data originating from a plurality of field instruments;

identify invalid samples of the data using an algorithm, the algorithm configured to detect data changes exceeding a maximum limit within a period of time;

correlate invalid samples of the data to one or more specific field instruments in the plurality of field instruments; and

determine a likelihood of a fault occurring in each of the one or more specific field instruments.

11. The processing system of claim 10, wherein the algorithm comprises:

performing a filtering operation on the data to generate filtered data;

computing a mean value M and standard deviation S from the filtered data;

computing an upper control limit and lower control limit using the mean value M and standard deviation S;

computing one or more statistical indicators from the data and the filtered data; and

identifying the invalid samples of the data, the invalid samples of the data having values greater than the upper control limit or lower than the lower control limit and satisfying a set of empirical rules applied to the data, the filtered data, and one or more statistical indicators;

wherein the filtering operation is performed by one or more filters, the filters selected from a group consisting of;

a low-pass frequency filter, a high-pass frequency filter, a band-pass frequency filter, a band-stop frequency filter, a median-based non-linear filter, a percentile-based non-linear filter, a weighted averaging filter, a wavelet filter, a frequency-domain thresholding filter, a wavelet-domain thresholding filter, a cepstrum-domain thresholding filter, an adaptive linear filter, and an adaptive non-linear filter; and

wherein the set of empirical rules includes a rule requiring a difference between two or more successive samples of the data to be greater than a deadband parameter D, a rule requiring a difference between two or more successive samples of the filtered data to be greater than a deadband parameter, a rule requiring one or more statistical indicators of the data to be greater than a corresponding maximum threshold, and a rule requiring one or more statistical indicators of the filtered data to be greater than a corresponding maximum threshold; and

wherein the threshold value is selected from a group consisting of a fixed threshold, a fuzzy threshold, a time-varying threshold, and an adaptive threshold.

12. The processing system of claim 10, wherein determining a likelihood of a fault occurring in each of the one or more specific field instruments includes factoring one or more of:

a status of the specific field instrument,

a previously determined fault likelihood for the specific field instrument, and

a location of the specific field instrument in relation to other instruments in plurality of field instruments.

13. The processing system of claim 10, wherein at least one of the specific field instruments comprises an array of field instrument elements, and wherein the processing component is further configured for identifying a detected change and the likelihood of fault for a specific element in the array of field instrument elements by factoring one or more measurements obtained from field instruments external to the array of field instrument elements.

14. A method comprising:

obtaining data originating from a plurality of field instruments;

identifying an invalid sample of the data using an algorithm, the algorithm including the steps of;

performing a filtering operation on the data to generate filtered data;

computing a mean value M and standard deviation S from the filtered data;

computing an upper control limit and lower control limit using the mean value M and standard deviation S; and

identifying the invalid sample from the filtered data, the invalid sample having values greater than the upper control limit or lower than the lower control limit and satisfying a set of one or more empirical rules;

correlating the invalid sample of the data to one or more specific field instruments in the plurality of field instruments; and

determining a likelihood of a fault occurring in each of the one or more specific field instruments from the invalid sample of the data.

15. The method of claim 14, wherein at least one of the specific field instruments comprises an array of field instrument elements, and the method further comprises:

identifying a detected change and the likelihood of fault for a specific element in the array of field instrument elements by factoring one or more measurements obtained from field instruments external to the array of field instrument elements.

16. The method of claim 14, wherein the filtering operation is performed by one or more filters, the filters selected from a group consisting of a low-pass frequency filter, a high-pass frequency filter, a band-pass frequency filter, a band-stop frequency filter, a median-based non-linear filter, a percentile-based non-linear filter, a weighted averaging filter, a wavelet filter, a frequency-domain thresholding filter, a wavelet-domain thresholding filter, a cepstrum-domain thresholding filter, an adaptive linear filter, and an adaptive non-linear filter.

17. The method of claim 14, the algorithm further comprising computing one or more statistical indicators from the data and the filtered data;

wherein the set of empirical rules is applied to the data, the filtered data, and the one or more statistical indicators; and

wherein the set of empirical rules includes a rule requiring a difference between two or more successive samples of the data to exceed a threshold value, a rule requiring a difference between two or more successive samples of the filtered data to exceed a threshold value, a rule requiring one or more statistical indicators of the data to exceed a corresponding threshold value, and a rule requiring one or more statistical indicators of the filtered data to exceed a corresponding threshold value; and

wherein a threshold value is selected from the group consisting of;

a fixed threshold, a fuzzy threshold, a time-varying threshold, and an adaptive threshold.

18. The method of claim 14, further comprising one or both of reporting incidences of the invalid sample of the data to a user, and notifying a user after the fault likelihood exceeds a predefined threshold.

19. The method of claim 18, wherein the reporting includes transmitting one or more of an error message, specific data status, an indication of an fault of each of the one or more specific field instruments, or a likelihood of a fault of each of the one or more specific field instruments.

20. The method of claim 14, wherein determining a likelihood of a fault occurring in each of the one or more specific field instruments includes factoring one or more of:

a status of the specific field instrument,

a previously determined fault likelihood for the specific field instrument, and

a location of the specific field instrument in relation to other instruments in plurality of field instruments.