Poor data quality identification

• US 20040199368A1
• Filed: 04/28/2004
• Published: 10/07/2004
• Est. Priority Date: 05/24/2001
• Status: Abandoned Application

• Current Assignee: Simmonds Precision Products, Inc. (Rtx Corporation)
• Original Assignee: Simmonds Precision Products, Inc. (Rtx Corporation)
• Inventors: Bechhoefer, Eric Robert
• Application Number:
  US10/833,461
• Publication Number:
  US 20040199368A1
• Time in Patent Office: Days
• US Class Current: 703/7
• CPC Class Codes: G01H 1/003 of rotating machines G01H1/10 takes precedence
• CPC Class Codes: G01N 2291/2693 Rotor or turbine parts
• CPC Class Codes: G01N 2291/2696 Wheels, Gears, Bearings
• CPC Class Codes: G01N 29/11 by measuring attenuation of acoustic waves
• CPC Class Codes: G01N 29/12 by measuring frequency or resonance of acoustic waves measuring frequency or resonant frequency of mechanical vibrations or acoustic waves in general G01H1/06, G01H3/04, G01H13/00; acoustic resonators G10K11/04; vibration or shock testing of structures G01M7/00
• CPC Class Codes: G01N 29/4445 Classification of defects
• CPC Class Codes: G01N 29/449 Statistical methods not provided for in G01N29/4409, e.g. averaging, smoothing and interpolation
• CPC Class Codes: G01N 29/46 by spectral analysis, e.g. Fourier analysis or wavelet analysis spectral signal processing per se G06F17/14
• CPC Class Codes: G05B 23/0235 based on a comparison with predetermined threshold or range, e.g. "classical methods", carried out during normal operation; threshold adaptation or choice; when or how to compare with the threshold

Abstract

Detecting poor data quality for a sensor includes obtaining measurement data for the sensor, determining a plurality of data quality indicators using the measurement data, combining the data quality indicators into a single scalar value, and determining if the single scalar value exceeds a predetermined threshold. Combining the data quality indicators may include, for each of the data quality indicators, squaring a difference between the measurement data for the sensor and the mean for each of the data quality indicators and dividing the result thereof by the variance to provide a partial value, wherein the single scalar value is the sum of all of the partial values. Detecting poor data quality may include providing a 1×n array of mean values for the data quality indicators, wherein there are n data quality indicators. Detecting poor data quality may include providing an n×n array of covariance values, wherein an element in the ith row and jth column represents a covariance between an ith data quality indicator and a jth data quality indicator. The single scalar value may be determined using the formula (M−X)^TCOV⁻¹(M−X), where X represents a 1×n array corresponding to the measurement data for the sensor, M represents the 1×n array of mean values for the data quality indicators, COV represents the n×n array of covariance values, T represents a matrix transpose operation, and −1 represents a matrix inverse operation.

Claims

1. A method of detecting poor data quality for a sensor, comprising:

obtaining measurement data for the sensor;

determining a plurality of data quality indicators using the measurement data;

combining the data quality indicators into a single scalar value; and

determining if the single scalar value exceeds a predetermined threshold.

2. A method, according to claim 1, wherein combining the data quality indicators includes, for each of the data quality indicators, squaring a difference between the measurement data for the sensor and the mean for each of the data quality indicators and dividing the result thereof by the variance to provide a partial value, wherein the single scalar value is the sum of all of the partial values.

3. A method, according to claim 1, further comprising:

providing a 1×

n array of mean values for the data quality indicators, wherein there are n data quality indicators.

4. A method, according to claim 3, further comprising:

providing an n×

n array of covariance values, wherein an element in the ith row and jth column represents a covariance between an ith data quality indicator and a jth data quality indicator.

5. A method, according to claim 4, wherein the single scalar value is determined using the formula:

(M−

X)^TCOV<sup>−

1</sup>(M−

X)

where X represents a 1×

n array corresponding to the measurement data for the sensor, M represents the 1×

n array of mean values for the data quality indicators, COV represents the n x n array of covariance values, T represents a matrix transpose operation, and −

1 represents a matrix inverse operation.

6. A method, according to claim 1, wherein the data quality indicators include accelerometer SNR, accelerometer RMS, accelerometer clipping, accelerometer ADC bit use, and accelerometer dynamic range.

7. A method, according to claim 6, where the data quality indicators also include accelerometer low frequency intercept and accelerometer low frequency slope.

8. A method, according to claim 1, wherein the predetermined threshold is determined using a chi square statistic.

9. A method of providing measured sensor data, comprising:

determining a plurality of data quality indicators using the measured sensor data;

combining the data quality indicators into a single scalar value; and

providing the measured sensor data only if the single scalar value does not exceed a predetermined threshold.

10. A method, according to claim 9, further comprising:

in response to the single scalar value exceeding the predetermined threshold, providing measured sensor data from a previous iteration.

11. A method, according to claim 9, further comprising:

in response to the single scalar value exceeding the predetermined threshold, providing default data as the measured sensor data.

12. A method, according to claim 9, wherein the predetermined threshold is determined using a chi square statistic.

13. Computer software that detects poor data quality for a sensor, comprising:

executable code that obtains measurement data for the sensor;

executable code that determines a plurality of data quality indicators using the measurement data;

executable code that combines the data quality indicators into a single scalar value; and

executable code that determines if the single scalar value exceeds a predetermined threshold.

14. Computer software, according to claim 13, wherein executable code that combines the data quality indicators includes executable code that, for each of the data quality indicators, squares a difference between the measurement data for the sensor and the mean for each of the data quality indicators and divides the result thereof by the variance to provide a partial value, wherein the single scalar value is the sum of all of the partial values.

15. Computer software, according to claim 13, further comprising:

executable code that provides a 1×

n array of mean values for the data quality indicators, wherein there are n data quality indicators.

16. Computer software, according to claim 15, further comprising:

executable code that provides an n×

n array of covariance values, wherein an element in the ith row and jth column represents a covariance between an ith data quality indicator and a jth data quality indicator.

17. Computer software, according to claim 16, wherein executable code that determines the single scalar value uses the formula:

(M−

X)^TCOV<sup>−

1</sup>(M−

X)

where X represents a 1×

n array corresponding to the measurement data for the sensor, M represents the 1×

n array of mean values for the data quality indicators, COV represents the n×

n array of covariance values, T represents a matrix transpose operation, and −

1 represents a matrix inverse operation.

18. Computer software, according to claim 13, wherein the data quality indicators include accelerometer SNR, accelerometer RMS, accelerometer clipping, accelerometer ADC bit use, and accelerometer dynamic range.

19. Computer software, according to claim 18, where the data quality indicators also include accelerometer low frequency intercept and accelerometer low frequency slope.

20. Computer software, according to claim 13, wherein the predetermined threshold is determined using a chi square statistic.

21. Computer software that provides measured sensor data, comprising:

executable code that determines a plurality of data quality indicators using the measured sensor data;

executable code that combines the data quality indicators into a single scalar value; and

executable code that provides the measured sensor data only if the single scalar value does not exceed a predetermined threshold.

22. Computer software, according to claim 21, further comprising:

executable code that provides measured sensor data from a previous iteration in response to the single scalar value exceeding the predetermined threshold.

23. Computer software, according to claim 21, further comprising:

executable code that provides default data as the measured sensor data in response to the single scalar value exceeding the predetermined threshold.

24. Computer software, according to claim 21, wherein the predetermined threshold is determined using a chi square statistic.