METHOD AND APPARATUS FOR IDENTIFYING ERRONEOUS SENSOR OUTPUTS

• US 20100228512A1
• Filed: 03/04/2009
• Published: 09/09/2010
• Est. Priority Date: 03/04/2009
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Gruer, Mike, Rowe, Eric
• Granted Patent: US 8,359,178 B2
• Time in Patent Office: Days
• US Class Current: 702/85
• CPC Class Codes: G01D 1/00 Measuring arrangements giving results other than momentary value of variable, of general application G01D3/00 takes precedence; in tariff metering apparatus G01D4/00; transducers not specially adapted for a specific variable G01D5/00
• CPC Class Codes: G01D 15/00 Component parts of recorders for measuring arrangements not specially adapted for a specific variable
• CPC Class Codes: G01D 21/00 Measuring or testing not otherwise provided for

Abstract

A method for identifying erroneous sensor outputs is provided. The method includes obtaining a first plurality of samples from a sensor over a period of time. A mean of the plurality of samples is determined. A first additional sample from the sensor at a time subsequent to times that the first plurality of samples were obtained is also received. A divergence of the first additional sample from the mean is determined. The first additional sample is identified as erroneous when the divergence is greater than a threshold.

Claims

1. A method for identifying erroneous sensor outputs, the method comprising:

obtaining a first plurality of samples from a sensor over a period of time;

determining a mean of the plurality of samples;

obtaining a first additional sample from the sensor at a time subsequent to times that the first plurality of samples were obtained;

determining a divergence of the first additional sample from the mean;

identifying the first additional sample as erroneous when the divergence is greater than a threshold; and

controlling a mechanism based on non-erroneous samples obtained from the sensor.

2. The method of claim 1, further comprising:

discarding the first additional sample when the first additional sample has been identified as erroneous.

3. The method of claim 1, further comprising:

flagging the first additional sample when the first additional sample has been identified as erroneous.

4. The method of claim 1, wherein the first additional sample is a sample immediately subsequent to a sample obtained last in time of the first plurality of samples.

5. The method of claim 4, further comprising:

determining a second mean of a second plurality of samples, the second plurality of samples including each of the first plurality of samples, except for a sample obtained first in time of the first plurality of samples, the second plurality of samples including the first additional sample;

obtaining a second additional sample from the sensor at a time subsequent to a time that the first additional sample was obtained;

determining a second divergence of the second additional sample from the second mean; and

identifying the second additional sample as erroneous when the second divergence is greater than the threshold.

6. The method of claim 1, wherein the first plurality of samples are consecutive in time.

7. The method of claim 1, wherein determining a divergence determines a coefficient of variance for the first additional sample with respect to the mean.

8. A system comprising:

a sensor;

a processing device communicatively coupled to the sensor and configured to;

determine a mean of a plurality of samples, each of the plurality of samples comprising data obtained by the sensor;

determine a divergence between a first sample subsequent in time to the plurality of samples and the mean; and

identify the first sample as erroneous when the divergence is greater than a threshold.

9. The system of claim 8, wherein the sensor is an acoustic proximity sensor.

10. The system of claim 8, further comprising:

a sample and hold circuit coupled between the sensor and the processing device, the sample and hold circuit configured to convert analog outputs from sensor to digital samples for the processing device.

11. The system of claim 8, wherein the processing device is further configured to:

discard the first sample when the first sample has been identified as erroneous.

12. The system of claim 8, wherein the processing device is further configured to:

flag the first sample when the first sample has been identified as erroneous.

13. The system of claim 8, wherein the processing device is further configured to:

determine a second mean of a second plurality of samples, the second plurality of samples including each of the first plurality of samples, except for a sample obtained first in time of the plurality of samples, the second plurality of samples including the first sample;

obtain a second sample from the sensor at a time subsequent to a time that the first sample was obtained;

determine a second divergence of the second sample from the second mean; and

identify the second sample as erroneous when the second divergence is greater than the threshold.

14. The system of claim 8, further comprising:

a memory device having software embodied thereon, the software operable to instruct the processor to;

determine a mean of the plurality of samples;

determine a divergence between a first sample subsequent in time to the plurality of samples and the mean; and

identify the first sample as erroneous when the divergence is greater than a threshold.

15. The system of claim 8, wherein the processing device is a microprocessor.

16. A program product comprising a processor-readable medium on which program instructions are embodied, wherein the program instructions are operable to:

for each new sample obtained from a sensor, determine a mean of a portion of the samples using a sliding window;

for each new sample obtained from the sensor, determine a divergence between the new sample and the mean of the samples within the sliding window; and

when the divergence is greater than a threshold for a sample, discard the sample.

17. The program product of claim 16, wherein for each new sample, the sliding window contains samples immediately preceding the new sample.

18. The program product of claim 16, wherein program instructions are further operable to:

determine a divergence by determining a coefficient of variance for the new sample with respect to the mean.

19. The program product of claim 16, wherein the sliding window contains a plurality of samples that are consecutive in time.

20. The program product of claim 16, wherein the sensor is an acoustic proximity sensor and each sample comprises data relating to a distance from the sensor to an object.