Patent Application
20080065465
The foregoing and other exemplary purposes, aspects and advantages will be better understood from the following detailed description of an exemplary embodiment of the invention with reference to the drawings, in which:
Referring now to the drawings, and more particularly to
Certain aspects of the present invention focus on data corresponding to time-managed lifetime data streams. Such streams are typical, for example, when analyzing warranty data, where data for consecutive vintages corresponds to vintage-by-vintage lifetime tests.
For example, a particular item of interest may correspond to a hard drive produced by a vendor A that is used on a machine of type B. For such a machine-type/component combination, the invention calls for specifying a parameter that reflects the shape of the hazard curve and further specifying acceptable and unacceptable levels of this parameter.
In addition, the acceptable rate of false alarms must be specified. This information is used to transform the data to the evidence curve (using a specific algorithm), which, in turn, is compared to a threshold to decide whether the combination should be flagged as one that shows evidence of an unfavorable change in the hazard rate, such as wear-out.
Application of this method to a plurality of components and machine types enables one to filter out (efficiently and with a low rate of false alarms) components that exhibit wear-out conditions with respect to various groups of machine types.
The method 100 includes specifying (e.g., step 110) acceptable and unacceptable levels of the shape parameter (c) and interpreting the shape parameter as a shape parameter of a Weibull distribution. An assumption that the underlying distribution is actually distributed as a Weibull distribution is not made, however, decisions are based on an estimation carried out under this assumption. The acceptable and unacceptable values are represented as c0 and c1, respectively. It is noted that c0>c1 and values of c0 are typically about 1.
Next, the rows of the data table are summarized (e.g., step 120) by pre-specified criterion (e.g., by vintage month) and the data table is consolidated into a less refined block representation. Note that the basic structural shape of Table 1 is still preserved, however, now the numerators and denominators in each cell correspond to months, not days. The number of blocks resulting from this summarization is denoted as N.
Then, bias-adjusted estimates of the shape parameter c for every block (e.g., for every month worth of shipments) are computed (e.g., step 130). This results in a set {ĉi, i=1, 2, . . . N} of bias-adjusted estimators of the Weibull shape parameter, based on successive blocks.
Next, weights {wi, i=1, 2, . . . , N} corresponding to the estimators {ĉi} obtained above are computed (e.g., step 140). In general, weights wi will decrease as the variance of ĉi increases. They could be chosen, for example, to be equal to the actual estimated variances of the bias-adjusted estimators {ĉi}. Another possibility is to define the weight wi to be equal to the number of failures based on which the estimator ĉi was computed. Note that if there were no failures in the i-th period, then we set ĉi=1 and wi=0, effectively eliminating this period from the decision procedure.
Next, the threshold h to be applied to the set {s1, s2, . . . , sN} defined by the equation
s
0=0, si=max[0, si-1+wi (ĉi−k)], I=1,2, . . . ,N
is computed (e.g., step 150) so as to achieve the following condition:
probability {max [s1, s2, . . . , sN]>h, when c=c0}=P, where the assumed distribution is Weibull and its scale parameters are either assumed to be known, are estimated separately for each block (and then it is assumed that the actual scale parameters are the estimated ones), or are estimated based on segments involving several blocks.
Once the threshold is computed, the computed threshold is applied (e.g., step 160) to the set {s1, s2, . . . , sN} that was actually observed and the method establishes whether smax>h, where by definition smax=max [s1, s2, . . . , sN]. If smax>h, an alarm is triggered (e.g., 170). In addition, the probability of the set {s1, s2, . . . , sN} to exceed the observed value of smax under the assumption that the shape parameter is acceptable (i.e., c=c0) may be computed and the scale parameters are obtained via one of the methods discussed above. This probability enables one to evaluate the degree of deviation from the acceptable level of the shape parameter, c0.
If, however, smax is not greater than h, then at the next data updating period, the data table is recomputed (e.g., 180) and the method is repeated starting with summarizing the rows of the data table (e.g., step 120).
In this example, the detection and diagnosis of wear-out conditions for a particular machine type, with respect to a given component (Field Replaceable Unit=FRU) is demonstrated. The analysis is based on the application of the method 100 described above. The analysis results in alarm conditions and a diagnostic chart depicted in
On the bottom plot of
The evidence curve increases for vintages corresponding to three consecutive months. As a result, the severity of wear-out conditions reaches the highest level, 1, as indicated by the vertical text on the right. This text also indicates that the acceptable and unacceptable levels of the Weibull shape parameter are c0=1 and c1=1.2, respectively.
The CPUs 311 are interconnected via a system bus 312 to a random access memory (RAM) 314, read-only memory (ROM) 316, input/output (I/O) adapter 318 (for connecting peripheral devices such as disk units 321 and tape drives 340 to the bus 312), user interface adapter 322 (for connecting a keyboard 324, mouse 326, speaker 328, microphone 332, and/or other user interface device to the bus 312), a communication adapter 334 for connecting an information handling system to a data processing network, the Internet, an Intranet, a personal area network (PAN), etc., and a display adapter 336 for connecting the bus 312 to a display device 338 and/or printer 339 (e.g., a digital printer or the like).
In addition to the hardware/software environment described above, a different aspect of the invention includes a computer-implemented method for performing the above method. As an example, this method may be implemented in the particular environment discussed above.
Such a method may be implemented, for example, by operating a computer, as embodied by a digital data processing apparatus, to execute a sequence of machine-readable instructions. These instructions may reside in various types of signal-bearing media.
Thus, this aspect of the present invention is directed to a programmed product, comprising signal-bearing media tangibly embodying a program of machine-readable instructions executable by a digital data processor incorporating the CPU 311 and hardware above, to perform the method of the invention.
This signal-bearing media may include, for example, a RAM contained within the CPU 311, as represented by the fast-access storage for example. Alternatively, the instructions may be contained in another signal-bearing media, such as a magnetic data storage diskette 400 (
While the invention has been described in terms of several exemplary embodiments, those skilled in the art will recognize that the invention can be practiced with modification within the spirit and scope of the appended claims.
Further, it is noted that, Applicants' intent is to encompass equivalents of all claim elements, even if amended later during prosecution.
