Data processing: generic control systems or specific application – Generic control system – apparatus or process – Optimization or adaptive control
Reexamination Certificate
1999-02-02
2002-04-09
Grant, William (Department: 2121)
Data processing: generic control systems or specific application
Generic control system, apparatus or process
Optimization or adaptive control
C700S051000, C706S062000
Reexamination Certificate
active
06370437
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates a computer system and method for predicting a future value of a series of product data.
2. Description of the Prior Art
Previously the approach of statistical process control (SPC) has been used to analyse manufacturing and other processes. Data about products produced in a manufacturing process are analysed in order to make inferences about the manufacturing process itself. For example, if the manufacturing process was for making confectionery, samples of confectionery would be drawn off at certain time intervals and analysed. Measurements for various parameters would be taken, for example, the weight of the confectionery items, the sugar content or other factors. Data from the samples would then be used to make inferences about the whole population of manufactured products and the manufacturing process. Typically, statistics such as the mean and standard deviation or range were calculated for the sample data for each parameter, and these statistics compared for different samples. For example, if the mean was observed to move outside a certain threshold range an “out of control” flag would be triggered to alert the factory staff to a problem in the manufacturing process. If trends were observed in the data, for example, an increase in the mean, the user could be alerted to this fact and then an investigation carried out.
Several problems with these statistical approaches to process control are known. For example, an inference is made that the data sets fit a standard type of distribution, such as a normal or Poisson distribution. However, this is rarely the case for process control data in which many outlying values are typically observed and which are often bimodal or show other irregular distributions. Also, data is obtained from a small sample of the manufactured products and used to make inferences about the whole population of manufactured products. This means that the statistics calculated using SPC type methods often are not an accurate reflection of the manufacturing process being analysed. Where products exhibit a high degree of consistency of performance then statistical examination of data is adequate, however, some products such as electric circuits have been found to exhibit performance results that do not fit statistical distributions, even though the data from these products fall within predetermined performance margins.
Process control is a difficult problem for manufacturers; it involves analysing the state of the manufacturing process and knowing how to adjust the manufacturing process in the light of the analysis in order to achieve efficiency and desired outputs. The manufacturer is faced with the problem of producing products that are within certain “tolerance” limits with respect to various parameters (for example, weight of confectionery bars) whilst at the same time reducing waste. For example, in order to manufacture confectionery bars that are all of a given minimum stated weight, the majority of the bars have to be produced with a weight that is greater than that minimum. If the manufacturer were able to produce confectionery bars all with a particular weight a great cost saving could be made. However, because of the limitations of current methods of process control this cannot be achieved. Often factors to do with the manufacturing process itself are too difficult to be measured practically and so measurements from the product themselves are taken. These measurements are sometimes analysed statistically and by making simple comparisons but information about the process is not provided quickly enough and with enough precision to enable the manufacturing process to be adjusted. The information provided about the process is about the “recent past” behaviour of that process and this means that there is always a “time lag” between receiving data about the process and taking any corrective action.
Another problem is that test data that is routinely collected in production tests on the factory floor are often not suitable for statistical analysis. This is because the data sets are often small, incomplete, discontinuous and because they contain outlying values. However, this type of data is typically all that is available for process control. Many manufacturers measure their products against a predetermined test regime and hence a wealth of data is routinely generated. Often because no suitable method for analysing this kind of data is available, the data is simply stored away “for the record” and this is a waste of resources. Methods that can be used to analyse this type of data are typically time consuming and do not allow the data to be reviewed in close to real time.
Another problem in process control is being able to deal with the fact that the inputs to the process vary. For example, if components are supplied to a manufacturer for assembly into a final product, those components may vary from batch to batch and from supplier to supplier. However, it is very difficult to analyse how the components vary and this is time consuming and expensive. Also, it is difficult to determine what effect variations in the components may have on the manufacturing process that is being controlled. These problems increase for more complex products that involve many components, such as circuit boards. For this reason, many manufacturers aim to limit variability by attempting to strictly control all the initial build conditions which includes the supply base. This is often not possible if it is necessary to vary the supplier for other reasons, for example to attain a good price or to achieve continuity of supply. Many manufacturers of electronic systems rely heavily upon their suppliers to ensure that materials and components used in the fabrication of products are compliant to specification. Often, electronic components are not examined before they enter factories. Investment programmes for test equipment at the component level have shown that it is not practical to distinguish between batches of components and also that the instances of non-compliant components are negligible. For these reasons many manufacturing companies have wound down their incoming component inspection processes. Instances do occur where manufactured products exhibit changes in performance that are attributed to changes in the components but no effective way of dealing with this problem has been found.
A particular problem in process control involves the situation where a manufacturing process is set up in a particular location, such as the USA, and it is required to set up the same process in a new location, say Canada, in order to produce the same quality of product with the same efficiency. It is typically very difficult to set up the new process in such a way that the same quality of product is produced with the same efficiency because of the number of factors that influence the process.
Failure mode effect analysis is another problem in process control. In this case, a failure occurs in the manufacturing or other process and it is required to analyse why this has occurred and what corrective action should be taken. Current methods for dealing with failure mode effect analysis include schematic examination and fault injection techniques but these are not satisfactory because of the problems with the data mentioned above.
JP8314530 describes a failure prediction apparatus which uses chaos theory based methods. A physical quantity, such as an electrical signal, showing the condition of a single installation is measured repeatedly at regular intervals in order to collect a time series of data. This time series of data is then used to reconfigure an attractor which is used to predict future values of the time series. These predicted values are compared with observed values in order to predict failure of the installation. This system is disadvantageous in many respects. The input data must be repeated measurements from a single apparatus taken at regular intervals. However, in practice it is often not possible to obtain m
Carter Malcolm Edward
Dodson Michael Maurice
Fojt Otakar
Grant William
Hartman Jr. Ronald D
Lee Mann Smith McWilliams Sweeney & Ohlson
Nortel Networks Limited
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