Web-based system of product performance assessment and quality control using adaptive PDF fitting

• US 7,565,266 B2
• Filed: 02/14/2006
• Issued: 07/21/2009
• Est. Priority Date: 02/14/2006
• Status: Expired due to Fees

• Current Assignee: Seagate Technology LLC (Seagate Technology Holdings Plc)
• Original Assignee: Seagate Technology LLC (Seagate Technology Holdings Plc)
• Inventors: Yoap, Timothy ChinSoon, Lim, Teck Heng, Yam, Hongsee, Ng, Hwaliang
• Primary Examiner(s): TSAI, CAROL S W
• Application Number:
  US11/353,739
• Publication Number:
  US 20070192060A1
• Time in Patent Office: 1,253 Days
• Field of Search: 702/105, 702/181, 708/200, 700 28- 30, 700/95, 700/108, 700/110, 703/2, 703/6, 703/13, 703/3, 706/13
• US Class Current: 702/181
• CPC Class Codes: G05B 2219/1112 Bit addressing, handling
• CPC Class Codes: G05B 2223/02 Indirect monitoring, e.g. monitoring production to detect faults of a system
• 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: G06F 18/2321 using statistics or function optimisation, e.g. modelling of probability density functions
• CPC Class Codes: G06F 18/2433 Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection

Abstract

A data analysis system analyzes data sets that are characterized by a wide variety of probability density functions (PDFs), and also analyzes mixed populations, i.e., a single data set containing subsets of data that each fit a different distribution. The data analysis system receives quality control data from a product manufacturing or product testing floor, determines whether the data is a mixed population, and fits the data with a variety of PDFs. The data analysis system selects the best fitting PDF based on statistical characteristics calculated for each fitted PDF. The data analysis system generates performance capability parameters based on the best fitting PDF, and may generate reports illustrating the performance capability parameters. The data analysis system also adjusts statistical control limits to provide a more reliable process trigger and control plan. In this manner, the data analysis system may more accurately report product performance.

Claims

1. A method comprising:

receiving data for a product;

fitting the data with a plurality of probability density functions (PDFs);

selecting a PDF that best fits the data;

generating one or more performance capability parameters for the product based on the best fitting PDF;

generating a report that presents at least one of the performance capability parameters; and

adjusting statistical control limits for the product based on the data, wherein adjusting statistical control limits comprises;

generating a first set of data samples using the selected PDF,

calculating new control limits from the first set of data samples,

generating a second set of data samples,

determining an average run length (ARL) value from the second set of data samples and the new control limits, and

comparing the determined ARL value to an acceptable ARL value.

2. The method of claim 1, further comprising:

providing a user interface for allowing a user to separate the data into two or more data sets, wherein one of the data sets is a main distribution data set;

fitting the main distribution data set with the plurality of PDFs; and

selecting a PDF that best fits the main distribution data set.

3. The method of claim 1, wherein receiving data for a product further comprises receiving real-time data from a product testing floor.

4. The method of claim 1, wherein selecting the PDF comprises comparing an actual defect rate with an estimated defect rate from each of the PDFs;

and computing a validity rating for the data and each of the PDFs.

5. The method of claim 4, wherein comparing the actual defect rate with the estimated defect rate comprises computing a χ

² indicator for each of the PDFs and selecting the smallest χ

² indicator.

6. The method of claim 4, wherein computing the validity rating comprises computing based on a p-value, kurtosis, and skewness of the data.

7. The method of claim 4, further comprising adjusting a specification limit when the actual defect rate is zero.

8. The method of claim 1, wherein fitting the data with a plurality of PDFs comprises fitting the data with at least two of the group consisting of a normal distribution, an exponential distribution, a Weibull distribution, a 3-Parameter Weibull distribution, a uniform distribution, a Laplace distribution, a power transformation, a logistic distribution, or a 3-parameter LogLogistic distribution.

9. The method of claim 1, further comprising:

obtaining parameters associated with the selected PDF;

adjusting statistical control limits based on the parameters;

triggering a user when a predetermined amount of the received data is outside of the adjusted statistical control limits;

using a one-sided pre-control chart when one of the parameters is at or above a threshold; and

using a confidence interval control chart when the parameter is below the threshold.

10. The method claim 9, wherein the parameter is Z_ST and the threshold is 10.

11. The method of claim 9, wherein using a one-sided pre-control chart comprises:

obtaining a specification limit;

determining a lower pre-control limit using the specification limit; and

generating a report that shows the received data relative to the specification limit and the lower pre-control limit.

12. The method of claim 9, wherein using a confidence interval control chart comprises:

determining an upper confidence level and a lower confidence level; and

generating a report that shows the received data relative to the upper confidence level and the lower confidence level.

13. The method of claim 1, wherein the at least one of the performance capability parameters comprises at least one of a defects per opportunity (DPO) parameter, a C_pk parameter, a Z-score parameter, and a Z-margin parameter.

14. The method of claim 1, wherein receiving the data for the product comprises receiving testing data for the product, wherein fitting the data with the plurality of PDFs comprises fitting the testing data with the plurality of PDFs, and wherein selecting the PDF that best fits the data comprises selecting the PDF that bests fits the testing data.

15. The method of claim 9, further comprising:

determining whether the parameter is at or above the threshold; and

selecting between using the one-sided pre-control chart and using the confidence interval control chart based on whether the parameter is at or above the threshold.

16. A system comprising:

a data acquisition module that acquires data for a product;

a probability density function (PDF) fitting module that fits the data with a plurality of PDFs, selects a PDF that best fits the data, and generates one or more performance capability parameters for the product based on the best fitting PDF;

a report generator that generates a report that presents at least one of the performance capability parameters; and

a control limit determination module that generates a first set of data samples using the selected PDF, calculates new control limits from the first set of data samples, generates a second set of data samples, determines an average run length (ARL) value from the second set of data samples and the new control limits, and compares the determined ARL value to an acceptable ARL value.

17. The system of claim 16, further comprising:

a database that stores the data; and

a PDF repository that stores the plurality of PDFs.

18. The system of claim 16, further comprising a user interface for allowing a user to separate the data into two or more data sets, wherein one of the data sets is a main distribution data set, wherein the PDF fitting module fits the main distribution data set with the plurality of PDFs and selects a PDF that best fits the main data set.

19. The system of claim 16, wherein the data acquisition module acquires real-time data from a product testing floor.

20. The system of claim 16, wherein the control limit determination module adjusts statistical control limits for the product based on the data.

21. The system of claim 16, wherein the plurality of PDFs include at least two of the group consisting of a normal distribution, an exponential distribution, a Weibull distribution, a 3-Parameter Weibull distribution, a uniform distribution, a Laplace distribution, a power transformation, a logistic distribution, or a 3-parameter LogLogistic distribution.

22. The system of claim 16, further comprising a trimming module that automatically separates the data into two or more data sets when the data comprises two or more data distributions, wherein one of the data sets is a main distribution data set, and wherein the PDF fitting module fits the main distribution data set with the plurality of PDFs and selects a PDF that best fits the main data set.

23. The system of claim 16, wherein the at least one of the performance capability parameters comprises at least one of a defects per opportunity (DPO) parameter, a C_pk parameter, a Z-score parameter, and a Z-margin parameter.

24. The system of claim 16, wherein the data acquisition module acquires testing data for the product, and wherein the PDF fitting module fits the testing data with the plurality of PDFs and selects the PDF that bests fits the testing data.

25. A computer-readable medium comprising instructions for causing a processor to:

receive data for a product;

fit the data with a plurality of probability density functions (PDFs);

select the PDF that best fits the data;

generate one or more performance capability parameters for the product based on the best fitting PDF;

generate a report that presents at least one of the performance capability parameters;

generate a first set of data samples using the selected PDF;

calculate new control limits from the first set of data samples;

generate a second set of data samples;

determine an average run length (ARL) value from the second set of data samples and the new control limits; and

compare the determined ARL value to an acceptable ARL value.

26. The computer-readable medium of claim 25, further comprising instructions to cause the processor to:

provide a user interface for allowing a user to separate the data into two or more data sets, wherein one of the data sets is a main distribution data set;

fit the main distribution data set with the plurality of PDFs; and

select a PDF that best fits the main distribution data set.

27. The computer-readable medium of claim 25, further comprising instructions to cause the processor to receive real-time data from a product testing floor.

28. The computer-readable medium of claim 25, further comprising instructions to cause the processor to adjust statistical control limits for the product based on the data.

29. The computer-readable medium of claim 25, further comprising instructions to cause the processor to fit the data with at least two of the group consisting of a normal distribution, an exponential distribution, a Weibull distribution, a 3-Parameter Weibull distribution, a uniform distribution, a Laplace distribution, a power transformation, a logistic distribution, or a 3-parameter LogLogistic distribution.

30. The computer-readable medium of claim 25, wherein the at least one of the performance capability parameters comprises at least one of a defects per opportunity (DPO) parameter, a C_pk parameter, a Z-score parameter, and a Z-margin parameter.

31. The computer-readable medium of claim 25, wherein the instructions for causing the processor to receive the data for the product comprise instructions for causing the processor to receive testing data for the product, wherein instructions for causing the processor to fit the data with the plurality of PDFs comprise instructions for causing the processor to fit the testing data with the plurality of PDFs, and wherein instructions for causing the processor to select the PDF that best fits the data comprise instructions for causing the processor to select the PDF that bests fits the testing data.

32. A method comprising:

receiving data for a product;

fitting the data with a plurality of probability density functions (PDFs);

selecting a PDF that best fits the data;

generating one or more performance capability parameters for the product based on the best fitting PDF;

generating a report that presents at least one of the performance capability parameters;

automatically separating the data into two or more data sets when the data comprises two or more data distributions, wherein one of the data sets is a main distribution data set, wherein automatically separating the data into the two or more data sets comprises automatically separating the data into three data sets when the data comprises three data distributions, wherein the data sets comprise a main distribution, an upper tail section, and a lower tail section;

fitting the main distribution data set with the plurality of PDFs;

selecting a PDF that best fits the main distribution data set;

determining a proportion of each of the lower tail section, the main distribution, and the upper tail section with respect to the data; and

computing defects per opportunity (DPO) rates for each of the lower tail section and the upper tail section.