Patent Description:
It is known that system manufacturers have been subjected to instances of fabricated/falsified data associated with component parts they receive. This may particularly be a problem for flight critical components. The fabricated/falsified data may originate for example in a material controls laboratory, from material testing, dimensional measurements, chemical composition data, mechanical test data, production acceptance inspections, supplier quality organizations, et cetera. The difficulties and expenses caused by components accompanied by fabricated/falsified data has created a need for increasing quality control processes on behalf of the vendor receiving the component.

There is a need for a processing technique that increases the likelihood of detecting fabricated and/or falsified data associated with a component.

A prior art method having the features of the preamble to claims <NUM> and <NUM> is disclosed in <CIT>. <CIT> discloses a prior art method of pre-processing data to remove outliers. <CIT> discloses a prior art method of data clustering.

From one aspect, the present disclose provides a method of testing vendor supplied parts data to identify fabricated or false data entries in accordance with claim <NUM>.

<FIG> is a simplified block diagram illustration of processing <NUM> associated with detecting false or fabricated data associated with a delivered product. The delivered product may be a finished component to be assembled into a larger system, or material to be used to manufacture a product. For example, the finished component may be a part of an aircraft gas turbine engine, or material to be used to manufacture a component of an aircraft gas turbine engine. However, it is contemplated that processing techniques of the present disclosure may be used in quality control applications for many different components and materials to ensure quality of delivered components and materials.

Referring to <FIG>, in step <NUM> test data (e.g., mechanical test data) associated with a delivered component is input to a fabricated data detector <NUM>. The test data provided in step <NUM> may include for example ultimate tensile strength, yield strength, reduction in area, percent elongation, creep, et cetera, of the delivered component. The test data provides a quantitative description of the delivered component from which mechanical properties of the delivered component can be assessed.

In step <NUM>, chemical composition data of the delivered component may also be input to the fabricated data detector <NUM>.

In step <NUM> the fabricated data detector <NUM> may also be provided with net-inspect data, such as for example, raw collected data, key product characteristic (KPC) data, et cetera. Similarly, in step <NUM> inspection record data may also be provided to the fabricated data detector <NUM>. The inspection record data may include for example data indicative of the manufacturer of the delivered component, part number, description of the delivered component, specification limits, et cetera, associated with the delivered component. In step <NUM> dimensional measurement data is input to the fabricated data detector <NUM>,.

The fabricated data detector <NUM> receives the data provided in one or more of steps <NUM>, <NUM>, <NUM>, <NUM> and <NUM>, processes the received data, and identifies suspicious data <NUM> that can be further received.

<FIG> is a simplified flow chart illustration <NUM> of the processing illustrated in <FIG>. In step <NUM> data indicative of a component (e.g., data <NUM>, <NUM>, <NUM>, <NUM> and <NUM> illustrated in <FIG>) is received for subsequent processing in routine A <NUM>.

<FIG> is a flow chart illustration of processing steps <NUM> of routine A illustrated in <FIG>. The data <NUM> contains observations, which are quantitative values based upon test, measurement or observation of the component. For example, the observations may include a diameter value, a true position value, a tensile strength value, a chemical composition percentage, et cetera. The observations are processed to create associated explanatory variables that describe the observations, and then multivariate data analysis is performed on the explanatory variables to assess if specific data entries appear suspicious when compared collectively to all other data input to the fabricated data detector <NUM> (<FIG>). The variables identify attributes of a number/collection of numbers (e.g., multiple serial numbers for a part number or all of a supplier's measurement data regardless of characteristic type). The data is processed in a unit less manner - the explanatory variables are based upon the number data itself, and not the units of the data. The explanatory variable identify a number's attributes. The explanatory variables may describe, for example, the actual number, digits, pattern of digits, what other information you can get from a number itself, et cetera. So then when it gets to the clustering portion, the numbers that are similar are grouped together and ones that don't follow the "common trend" would be flagged as suspicious (different).

Referring still to <FIG>, the observations are processed in a frequency determination unit <NUM> that counts the number of observations and outputs a count number on line <NUM>. The observations are also processed in a unique observation ratio determination unit <NUM> that determines the number of occurrences of different quantitative values and outputs an index indicative thereof on line <NUM>. An inliers determination unit <NUM> also receives the observations and processes the observations, using for example a P-value from Mahalanobis Distance Chi-Squared Test for Inliers, to provide explanatory data indicative thereof on line <NUM>. In an alternative embodiment the inliers determination unit <NUM> may be moved to the processing to be discussed with respect to <FIG>,.

A standard deviation of observation unit <NUM> processes the observations to provide a standard deviation (e.g., based on the counter value on the line <NUM>), and provides the standard deviation data indicative thereof on line <NUM>.

An individual digits determination unit <NUM> also processes the observations. This unit may provide a number of different processing steps <NUM>, including for example, steps to determine:.

The resultant information from the determinations <NUM> is output on line <NUM>.

Unit <NUM> further processes the observations to extract the last digit and reported last digit, and units <NUM> and <NUM> determine the proportion of odd digits and the proportion of even digits, respectively, and results are output on lines <NUM>, <NUM>, respectively. The extracted last digit and reported last digit data is processed in a unit <NUM>, which includes a number of sub-units 236a-236d. For example, the extracted last digit data may be processed to perform a chi-square goodness-of-fit test summands for i=<NUM>,<NUM>,. ,<NUM> where i is the bin and each bin corresponds to reported last digit from <NUM>,<NUM>. Where reported last digit removes trailing zeros. For last digit and reported last digit, a Chi-Square goodness-of-fit test routine may provide reference values, summands that describes the weighted squared difference between expected frequency and observed frequency for each possible digit. The sum of the weighted difference for each possible value of last digit (<NUM>, <NUM>,. , <NUM>) provides a Chi-Square goodness-of-fit test comparison value. This comparison value is then quantified as a p-value using degrees of freedom equal to number of bins minus one and a p-value using degrees of freedom equal to number of bins minus number of estimated parameters.

Upon the completion of the processing steps illustrated in <FIG>, processing returns to test <NUM> illustrated in <FIG>. Referring again to <FIG>, if specification limits are defined in the received data file, then processing proceeds to routine B <NUM>. However, if the specification limits are not defined in the received data filed, then processing proceeds to routine C <NUM>.

<FIG> is a flow chart illustration of a processing steps <NUM> of routine B illustrated in <FIG>. In this case since the data file includes specification limits, the observation value and the specification limits are provided for subsequent processing. For example, the data provided may include observations and specification limits containing a lower and upper bound <NUM>, observations and an associated minimum requirement/limit <NUM>, and observations and an associated maximum requirement/limit <NUM>. The observations having specification limits containing a lower and upper bound <NUM> are input to processing <NUM> that includes determination units <NUM>-<NUM> to determine for example:.

Referring still to <FIG>, the data provided from observations and <NUM> sided lower specification only <NUM> are input to processing <NUM> that includes determination units <NUM>-<NUM> to determine for example:.

The data provided from observations and <NUM> sided upper specification only <NUM> are input to processing <NUM> that includes determination units <NUM>-<NUM> to determine for example:.

Upon the completion of processing <NUM>, <NUM> and <NUM> the processing proceeds to routine C <NUM> (<FIG>).

<FIG> is a flow chart illustration of a processing steps <NUM> of routine C illustrated in <FIG>. A K-means clustering unit <NUM> receives a combination of applicable explanatory variables from routine A <NUM> (<FIG>), routine B <NUM> (<FIG>) and the date file <NUM> (<FIG>). K-means clustering is a known method of vector quantization, originally from signal processing, which is popular for cluster analysis in data mining. Explanatory variables (or appropriate subset of explanatory variables) are input into an iterative cluster based outlier detection procedure. K-Means clustering is used to group observations by unique identifiers and similarity of explanatory variables. The procedure may begin with k=<NUM> clusters and determines if outliers are present based on extreme distances to the center of assigned cluster and multivariate T2 values greater or less than threshold values. If a unique identifier is considered an outlier, it is removed from the data set and number of clusters is set to k=<NUM> and the evaluation for outliers is repeated. The cluster size is incremented until the average distance to cluster centers remains constant when k is increased by one or until no outliers are detected.

<FIG> is a pictorial illustration of a processing system <NUM> that may execute the processes set forth in <FIG>. The system includes a server <NUM> having a memory <NUM> that contains executable program instructions and a processor <NUM> that executes the executable program instructions. The system also includes a database <NUM> that may be located in the memory device <NUM> or located remotely. The server <NUM> may include a display and input/output devices (e.g., keyboard, mouse, printer, flash drives, et cetera). The system <NUM> may also include work stations <NUM>, <NUM> located at vendor sites for them to transfer component test data. Each of the work stations <NUM>, <NUM> includes a work station having a computer (e.g., a PC, laptop, tablet, handheld computing device, et cetera). Each work station <NUM>, <NUM> communicates over a packet switched network <NUM> (e.g., the internet) with the server <NUM>.

<FIG> are flow chart illustrations of alternative embodiment processing steps of routines A, B, C illustrated in <FIG>, respectively.

It should also be understood that although a particular component arrangement is disclosed in the illustrated embodiment, other arrangements will benefit herefrom.

The foregoing description is exemplary rather than defined by the features within.

Claim 1:
A method of testing vendor supplied parts data to identify fabricated or false data entries, the method comprising:
receiving test data indicative of a component received from a vendor, the test data comprising at least one of tensile strength, a diameter, position and yield strength, and storing the test data in a non-volatile memory device (<NUM>); characterized by
processing the test data in a processor (<NUM>) to create a plurality of explanatory variables of the test data, wherein said test data is processed in a unit less manner such that the explanatory variables are based upon number data of the test data, and not units of the test data, and wherein the explanatory variables identify attributes of the number data of the test data;
performing multivariate data analysis on the plurality of explanatory variables, where the multivariate data analysis includes an iterative cluster based outlier detection procedure; and
generating a confidence indicator value indicative of the likelihood that the test data includes at least one fabricated or false data entry, where the performing multivariate data analysis using the plurality of explanatory variables comprises performing K-means clustering and identifying cluster outliers to generate the confidence indicator.