Abstract:
A network-based method and system for analyzing and displaying reliability data from a user is provided. The method includes recording reliability data, obtaining unreliability plots, obtaining Weibull distribution parameters, creating control charts for those parameters over time, and obtaining hypothesis tests to ensure reliability has not changed due to process variation.

Description:
A portion of the disclosure of this patent document contains material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever. 
     BACKGROUND OF THE INVENTION 
     This invention relates generally to computer network-based systems and more particularly to a network-based method and system for collecting, analyzing, and reporting reliability data. 
     Superior product and system reliability is achieved when reliability tools are integral parts of development, design, manufacturing, and service processes. Historically quality control efforts have been directed toward minimizing the number of product units that do not meet dimensional and/or performance criteria before leaving the manufacturing plant. This limited approach does not suffice in ascertaining failure modes, to estimate the likely impact of a potential corrective action, or to follow the incidence and nature of product failures over time. It also makes it difficult to provide objectively determined product life expectancy data to prospective customers. 
     Therefore it would be desirable to provide a system and method to analyze reliability data for facilities running reliability tests to allow users to ascertain overall failure rates, to dissect those overall rates into failure rates for specified failure modes, and to obtain plots and parameters as a function of time. It would further be desirable if the reliability data were accessible at sites remote from the facility to minimize the time and effort necessary to compile and submit such data to a remote site. 
     BRIEF SUMMARY OF THE INVENTION 
     The present invention includes a tool that allows the user to record reliability data, obtain unreliability plots, obtain Weibull distribution parameters, create control charts for those parameters over time, and obtain hypothesis tests to ensure reliability has not changed due to process variation. The tool allows users to identify variations that could affect the overall reliability of products through control charts that serve as an early warning for changes in product and system life by plotting shape (β) and scale (η) parameters. The tool also allows users to obtain plots and statistics for specific failure modes that may appear. A system of failure mode codes facilitates filtering of the data. 
     The tool allows analysis of failure incidence and modes of failure over time, and provides an estimate of the likely impact of an action designed to improve the reliability of a given component of a product. It also provides objectively determined life expectancy data for a product, which confers a marketing advantage. Moreover, users can access the tool over the Internet and have access to reliability data for a plant located anywhere in the world. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 is a block diagram of a system in accordance with one embodiment of the present invention; 
     FIG. 2 is an expanded version block diagram of an exemplary embodiment of a server architecture of an alternative system; 
     FIG. 3 is a flow diagram of a network-based method for analyzing and displaying reliability data; 
     FIG. 4 is an exemplary embodiment of a Reliability Summary Report page for reporting purposes that includes a plurality of pull down menus to be used when supplying information to the system shown in FIG. 3; 
     FIG. 5 is an exemplary embodiment of a report downloaded and displayed by the system shown in FIG. 3 as Weibull &amp; Pareto plots when the user has selected the appropriate filters; 
     FIG. 6 is an exemplary embodiment of a report downloaded and displayed by the server system shown in FIG. 3 as control charts when the user has selected the appropriate filters; 
     FIG. 7 is an exemplary embodiment of a user interface for data entry downloaded and displayed by the server system (shown in FIG. 3) for the user to select the reliability instance (characteristic test) of the data to be entered; 
     FIG. 8 is an exemplary embodiment of a user interface downloaded and displayed by the server system (shown in FIG. 3) for the user to enter the data points; 
     FIG. 9 is an exemplary embodiment of a Reliability CTQ Setup page; 
     FIG. 10 shows a plot of F, the failure cumulative probability or rank, vs. T, the failure time or cycles with confidence bounds; 
     FIG. 11 shows a plot of F, the failure cumulative probability or rank, vs. T, the failure time or cycles with confidence bounds illustrating the concept of percentiles or L X% ; 
     FIG. 12 shows control charts of beta, the shape of the Weibull distribution, vs. fiscal weeks (FW). 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     FIG. 1 is a block diagram of a system  10  in accordance with one embodiment of the present invention. System  10  includes a server sub-system  12 , sometimes referred to herein as server  12 , and a plurality of user devices  14  connected to server  12 . In one embodiment, devices  14  are computers including a web browser, and server  12  is accessible to devices  14  via a network such as an intranet or the Internet. In an alternative embodiment, devices  14  are servers for a network of customer devices. 
     Devices  14  are interconnected to the network, such as a local area network (LAN) or a wide area network (WAN), through many interfaces including dial-in-connections, cable modems and high-speed ISDN lines. Alternatively, devices  14  are any device capable of interconnecting to a network including a network-based phone or other network-based connectable equipment. Server  12  includes a database server  16  connected to a centralized database  18  containing reliability information. In one embodiment, centralized database  18  is stored on database server  16  and can be accessed by potential users at one of user devices  14  by logging onto server sub-system  12  through one of user devices  14 . In an alternative embodiment centralized database  18  is stored remotely from server  12 . 
     FIG. 2 is an expanded version block diagram of an exemplary embodiment of a server architecture of a system  22 . System  22  includes server sub-system  12  and user devices  14 . Server sub-system  12  includes database server  16 , an application server  24 , a web server  26 , a fax server  28 , a directory server  30 , and a mail server  32 . A disk storage unit  34  is coupled to database server  16  and directory server  30 . Servers  16 ,  24 ,  26 ,  28 ,  30 , and  32  are coupled in a local area network (LAN)  36 . In addition, a system administrator workstation  38 , a user workstation  40 , and a supervisor workstation  42  are coupled to LAN  36 . Alternatively, workstations  38 ,  40 , and  42  are coupled to LAN  36  via an Internet link or are connected through an intranet. 
     Each workstation  38 ,  40 , and  42  is a personal computer having a web browser. Although the functions performed at the workstations typically are illustrated as being performed at respective workstations  38 ,  40 , and  42 , such functions can be performed at one of many personal computers coupled to LAN  36 . Workstations  38 ,  40 , and  42  are illustrated as being associated with separate functions only to facilitate an understanding of the different types of functions that can be performed by individuals having access to LAN  36 . 
     In another embodiment, server sub-system  12  is configured to be communicatively coupled to various individuals or employees  44  and to third parties, e.g., users,  46  via an ISP Internet connection  48 . The communication in the exemplary embodiment is illustrated as being performed via the Internet, however, any other wide area network (WAN) type communication can be used in other embodiments, i.e., the systems and processes are not limited to being practiced via the Internet. In addition, and rather than a WAN  50 , local area network  36  could be used in place of WAN  50 . 
     In the exemplary embodiment, any employee  44  or user  46  having a workstation  54  can access server sub-system  12 . One of user devices  14  includes a workstation  54  located at a remote location. Workstations  54  are personal computers having a web browser. Also, workstations  54  are configured to communicate with server sub-system  12 . Furthermore, fax server  28  communicates with employees  44  and users  46  located outside the business entity and any of the remotely located user systems, including a user system  56  via a telephone link. Fax server  28  is configured to communicate with other workstations  38 ,  40 , and  42  as well. 
     FIG. 3 is a flow diagram  70  for a network-based method for analyzing and displaying reliability data. In one embodiment, a system administrator establishes  71  the reliability instances or test specifications. The user inputs information into a device (such as device  14  shown in FIG. 1) that transmits the information to a server (such as server  12  shown in FIG.  1 ). The data is received  72  through specified filters  73  via a graphical user interface, as will be described in greater detail below. 
     Server  12  performs  74  statistical tests on the received reliability information based on the filters (pull downs) selected. In one embodiment, the statistical tests are stored on server  12 . In an alternative embodiment, the statistical tests are stored on a computer remote from server  12 . 
     System  10  then generates  76  a report in accordance with the preferences selected by the user. Server  12  then displays  78  the generated report to user device  14  so that the user can view the report. 
     FIG. 4 is an exemplary embodiment of a Reliability Summary Report page, as depicted in screen shot  80 , which includes a plurality of pull down menus to be used when supplying information to system  10  (shown in FIG.  1 ). Screen shot  80  includes a Plant pull down menu  82 , a Product Line pull down menu  84 , a Catalog No. pull down menu  86 , a Type of Test pull down menu  88 , a Failure Mode pull down menu  90 , a Specification #1 display area  92 , and a Subgroups In pull down menu  94 . Screen shot  80  also includes a Data Set  1  area  96 , which includes pull down menus for specifying a data period and display fields for indicating a total number of units, a number of units failed, a number of units passed, a number of failure modes, a beta parameter (defined below), an eta parameter (defined below), an r 2  parameter (defined below) and an L 10  parameter (defined below). Screen shot  80  further includes a Data Set  2  area  98 , which includes pull down menus for specifying a data period and display fields for indicating a total number of units, a number of units failed, a number of units passed, a number of failure modes, a beta parameter (defined below), an eta parameter (defined below), an r 2  parameter (defined below) and an L 10  parameter (defined below). Screen shot  80  still further includes a Confidence Interval selection area  100 , a Control Charts radio button  102 , a New Test radio button  104 , a New Data radio button  106 , and an Exit radio button  108 . Selection of Control Charts radio button  102  replaces plots with control charts, as defined below, while selection of New Test radio button  104  takes the user to a data collection window with the current setup. Selection to New Data radio button  106  takes the user to a data collection window with a current setup, while selection of Exit radio button  108  allows the user to exit system  10  (shown in FIG.  1 ). 
     FIG. 5 is an exemplary embodiment of a Reliability Interface results page, as depicted in screen shot  120 , in which display area  122  and Confidence Interval selection area  124  show an exemplary choice of variables corresponding to items  82  through  100  in FIG.  4 . Screen shot  120  also includes a Weibull Cumulative Probability Function plot  126 , and a Pareto of Failure Modes display area  128 , as well as a set of radio buttons  130  that correspond to radio buttons  102 , 104 ,  106  and  108  in FIG.  4 . The Weibull Plot overlays the two data sets plot for comparison purposes. Also, the Failure Modes Pareto colors the failure mode(s) under study differently. 
     FIG. 6 is an exemplary embodiment of a second Reliability Interface results page, as depicted in screen shot  140 , in which display area  142  and Confidence Interval selection area  144  show another exemplary choice of variables corresponding to items  82  through  100  in FIG.  4 . Screen shot  140  also includes a Control Chart for Beta plot  146 , and a Control Chart for Eta plot  148 , as well as a set of radio buttons  150  that correspond to radio buttons  102 , 104 ,  106  and  108  in FIG.  4 . 
     FIG. 7 is an exemplary embodiment of a Reliability Data Collection page, as depicted in a screen shot  160 , which includes a set of pull down menus  162  for specifying a plant, a product line, a catalog number, a type of test, a number of units, a tester, and any specifications. Screen shot  160  also includes a pull down menu  164  for specifying a type of test, and a data table  166  that displays the date, identification number, failure time, failed/pass, and a failure mode columns. Screen shot  160  also includes a Generate Report radio button  168 , a New Test radio button  170 , a New CTQ radio button  172 , a Save radio button  174 , and an Exit radio button  176 . Selection of Generate Report radio button  168  causes system  10  (shown in FIG. 1) to display a report window, while selection of New Test radio button  170  restores this page with all default values. Selection of New CTQ radio button  172  causes system  10  to display a CTQ setup window (described below), while selection of Save radio button  174  saves input data and calculates parameters. Selection of Exit radio button  176  causes the user to exit system  10 . 
     FIG. 8 is an exemplary embodiment of a second Reliability Data Collection page, as depicted a screen shot  180 , which includes a set of pull down menus  182  that show an exemplary choice of a plant, a product line, a catalog number, a type of test, a number of units, a tester, and a specification. Screen shot  180  also includes a Reliability Data Collector area  184  that appears to all-out data entry when all required fields have been selected. Selection of an Enter button in area  184  causes any new data to appear in data table  186 , which corresponds to report area  166  in FIG.  7 . Screen shot  180  also includes a set  188  of buttons that allow a choice of Generate Report, New Test, New CTQ, Save, and Exit options. 
     FIG. 9 is an exemplary embodiment of a second Reliability CTQ Setup page, as depicted in a screen shot  190 , which includes a set of pull down menus  192  that show an exemplary choice of a plant, a product line, a catalog number, a type of test, a number of units, and a tester. Screen shot  190  facilitates creation of the new test description of a reliability instance. Screen shot  190  also includes a Specifications area  194  that includes a set of Specification text boxes for specifying an L 10 , a number of units, and a % Confidence Level for each of Specification # 1 , Specifications # 2 , and Specifications # 3 . Screen shot  190  also includes a plot  196  of Unreliability vs. Time or Cycles, to illustrate the concept of a reliability specification as well as a set  198  of radio buttons corresponding to radio buttons  168 ,  170 ,  172 ,  174 , and  176  shown in FIG.  7 . 
     The mathematical background of the statistical analysis of the data is described below. This method uses the Weibull function as the assumed distribution because of its flexibility in assuming various distribution profiles. 
     The life data for probability plotting has two axes: T, the actual failure time or cycles, and F, the failure cumulative probability or rank. Of the several methods of calculating F, median rank has been determined to be the best for skewed distributions. Medium rank has been used in the exemplary embodiment because: 1) Weibull distributions could be symmetrical or non-symmetrical; and 2) If the life data are normal (wearout failures) the mean, midpoint and the median should all be the same. The Weibull cumulative density function is given by:          F        (   t   )       =     1   -     e     -       (     t   η     )     β                                  
     where β and η represent the shape and scale parameters of the Weibull distribution respectively. Beta values less than one correspond to early failures, while those of about 2 or greater represent wearout failures. Beta values near unity indicate random failures that can be used to estimate useful life. Eta represents the point at which 1/e of the units fail that corresponds to the midlife of a unit. The T and F axes are transferred to the linear form of the Weibull expression through use of            ln        (   t   )       =         1   β          ln        (     -     ln        (     1   -     F        (   t   )         )         )         +     ln        (   η   )           ,                          
     which is in the form of Y=bX+u. With the data transformed, the best linear unbiased estimate (abbreviated BLUE) can be obtained. In the exemplary embodiment, the method of least squares in X has been used. The method of least squares provides the lowest variance of all possible unbiased estimators of the regression parameters b and u. b and u are estimates of β and η by the relation shown in the following equations.                         SS   x     =       ∑     i   =   1     n            (       x   i     -     x   _       )     2                          b   =       SS   xy       SS   x                                SS   xy     =       ∑     i   =   1     n            (       x   i     -     x   _       )          (       y   i     -     y   _       )                            u   =       y   _     -     b                   x   _                                  SS   y     =       ∑     i   =   1     n            (       y   i     -     y   _       )     2                            r   2     =       b   *     SS   XY         SS   Y                                      
     As a means to verify accuracy of the model&#39;s prediction, the coefficient of determination (r 2 ) is interpreted as the proportion of the variation in Y that is explained by the regression of Y with X. With the linear estimate, transforming back to Weibull&#39;s original form, the percentiles (T) and probabilities (F) can be calculated. 
     The time by when 10% of the units are expected to fail or L 10  is another statistic of interest in this module. This time is the Reliability Critical-to-Quality (CTQ) criterion for any given product. The L 10  is estimated with a certain confidence level (CL). For example, by substituting 0.10 as F in the F(T) equation from the Weibull model, the L 10  at 50% confidence level can be calculated since the line represents the Median (or 50%) Rank. In most cases a 90% confidence level is used. The 90 confidence level can be obtained using the confidence bounds. A non-parametric method is discussed below. 
     The confidence intervals represent the range for the expected variation in F at any given T and vice versa. This range includes limits that contain a specified percentage of variation, for example a 90% confidence interval contains 90% of the variation. The line obtained from the regression represents median rank and therefore 50% of the variation at each side. Consequently, the upper and lower limits or bounds of this 90% confidence interval are called the α and 1-α confidence bounds where 2α-1 equals the specified confidence interval. FIG.  10  and FIG. 11 show how these bounds are used to make estimates at α and 1-α level. These limits are non-parametric curves that connect the α and 1-α ranks (R α  and R 1-α ) calculated for each failure k from a subgroup of size n as follows.              α   =       ∑     k   =   j     n            (         n           k         )              R     α                 %            (     1   -     R     α      %         )         n   -   k                   (     for                 lower                 rank     )               and                 1   -   α     =       ∑     k   =   j     n            (         n           k         )              R     1   -     α                 %              (     1   -     R     1   -     α      %           )         n   -   k                   (     for                 upper                 rank     )                   where                   (         n           k         )       =         n   !         k   !            (     n   -   k     )     !         .                            
     These ranks are plotted vertically along the median ranks of every failure point for the predicted time or cycles to failure. This means that the median rank value for every failure point is substituted in the F(T) expression as F, then the upper and lower ranks are plotted vertically along the t=T(F), as shown in FIG.  10  and FIG.  11 . With these ranks calculated, any percentile (T) can be obtained at the confidence level of the corresponding bounds where the ranks lies by interpolation/extrapolation methods. For example, if the L 10  10% is desired at 95% confidence level (CL), using the 95% confidence bound (a 90% confidence interval) an interpolation would have to be performed between the two data points with R 95%  above and below 10%. 
     FIG. 12 shows control charts of beta, the shape of the Weibull distribution, vs. fiscal weeks (FW). A control chart is a graphical display of the variation of any targeted statistic during an industrial process through time. In the exemplary embodiment, the β and η parameters have been monitored since an instability of these values provides an early alarm of variation in the processes that affect reliability of the product or system. For both parameters the control chart plots four quantities, historical parameter (β, η), upper control bound (UCB), lower control bound (LCB), and subgroup parameters. 
     The user selects a grouping period that determines the number of points used in every plot. The grouping periods are by one of fiscal week, month, quarter and year. For instance, if the user selects that the data are grouped by fiscal weeks, a subgroup will contain all data points recorded between Monday and Sunday of that week. The control chart plots up to 12 subgroups back in data. The subgroup parameters are those calculated through use of the data contained in the grouping period specified by the user. The historic parameters are those calculated through use of the data contained between the period when the report is requested and the preceding twelve months, if the data are available. The control bounds are calculated through use of the confidence intervals for the parameters β and η. These are calculated as follows: 
     
       
         β L =1/( D*exp (1.049 K   γ   /n   ½ )) (lower beta limit) 
       
     
     and 
     
       
         β U =1/( D/exp (1.049 K   γ   /n   ½ )) (upper beta limit) 
       
     
     
       
         η L   =exp ( L −1.081 K   γ ( D/n   ½ )) (lower eta limit) 
       
     
     and 
     
       
         η U   =exp ( L +1.081 K   γ ( D/n   ½ )) (upper eta limit) 
       
     
     where K γ =the [100(1+γ)/2]th standard normal percentile 
     
       
           D =0.7797*standard deviation of the subgroup 
       
     
     
       
           L =subgroup mean+0.5772 *D   
       
     
     
       
           n =subgroup size 
       
     
     Any point falling out of those limits is an indicator that this point is from a different population than the collective group with a least a γ% confidence. These control bounds are to be recalculated after four new subgroups of data have been recorded to reduce sensitivity of the limits. 
     In use, system  10  (shown in FIG. 1) provides the user with a way of analyzing and displaying reliability data. This reliability module establishes a data collection system for manufacturing plants and facilities performing reliability testing. It provides easy data entry windows and complete reports that includes Weibull plots, failure mode Pareto plots, control charts for distribution parameters, and other life predictors. 
     While the invention has been described in terms of various specific embodiments, those skilled in the art will recognize that the invention can be practiced with modification within the spirit and scope of the claims.