Data processing: measuring – calibrating – or testing – Measurement system – Statistical measurement
Reexamination Certificate
1998-11-12
2001-12-25
Hoff, Marc S. (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system
Statistical measurement
C702S182000, C700S108000
Reexamination Certificate
active
06334095
ABSTRACT:
FIELD OF INVENTION
This invention relates to methods for determining plant capacity, and in particular, to probabilistic methods to evaluate a plant's capacity.
PRIOR ART
Manufacturing plants have operational interruptions due to equipment failure and process and operating problems, which reduce production rates below that which is desired. Measurement of the negative impact that these problems have had on the production capability of the plant is important as a performance indicator and as a guide for production planning in the future. These interruptions to production continuity are functional failures. Some functional failures do not cause a total cessation of production, but only a reduction in manufacturing rate. These reductions in rate may be ignored in the measurement of plant availability in some manufacturing facilities but appropriately are converted to equivalent downtime by many plants. For example, operation at 50% of capacity for two days because of an operational problem is the equivalent of one day of complete downtime.
The maximum possible operating time is determined from the total time within a time period of interest, for example 8760 hours per year. Market-dictated downtime due to lack of sales or any other similar administrative downtime reduces the maximum operating time.
The ratio of total plant downtime to total time, as adjusted in the preceding paragraph, is a measure of plant unavailability. Its complement is plant availability. Plant availability is a common performance measure for a plant. Thus defined, plant availability is never greater than 100% and a reasonable range is 60% to 98%, for a range of industries. Downtime reduces the productivity of a manufacturing facility because reduced production may result in loss of sales and profits. Maintenance and Reliability Engineering organizations in plants have primary goals of optimizing downtime to economical levels to avoid production shortfalls and profit loss.
Plant reliability engineers and others involved in plant reliability have adopted the term “availability” from classical reliability engineering definitions. “Availability” is derived, in classical reliability, from “reliability” and “maintainability”. For repairable systems like plants, reliability is measured with Mean Time Between Failure (MTBF) and maintainability with Mean Time to Restore (MTTR). Availability is then defined as:
Availability=MTBF/(MTBF+MTTR)
This is identical to the ratio of uptime to total time or actual production to maximum possible production. However one makes the calculation, the term availability is commonly used. Availability translates into plant capacity, i.e. 95% plant availability means the plant is capable of producing 95% of maximum capacity if called upon to do so. Because of the traditional definitions discussed in the preceding paragraph, “availability” is often used when “capacity as a percent of maximum” better relates to the business purpose of such a measurement. The terms used are not so important as long they do not result in miscommunication. We will generally use the term “capacity”. This is a contraction of “capacity as a percent of maximum”. Furthermore, this may be indicated as a fraction instead of percent.
Current Practice
Historical plant performance is the basis for projecting future performance. Appropriate allowances are made to reflect changing physical and organizational conditions. The historical performance may be measured with availability, uptime (the complement of downtime), or simply the produced volumes measured over a time interval. When the measurements are rigorous, identical results are obtained. These measures are single-valued; that is, a single number describing the arithmetical mean is all that is known about the measure. The future plant capacity is actually a probability distribution. The actual plant capacity will vary due to chance, but this variability is undefined and not measured in current practice. Nonetheless, these single-valued averages are the foundation for production planning and setting reliability priorities.
Current State-of-the-Art
The current state-of-the-art for capacity and availability calculations for a plant is Monte Carlo computer simulation (or Monte Carlo-like simulations). One such use of Monte Carlo simulations was described in Plant Availability Measurement, Plotting and Simulation, by Jan Smith and Preston Rockhold, a paper presented at the International Conference on Process Plant Reliability-Europe, 1996, which is hereby incorporated by reference (this reference, however, investigated methods other than the use of detailed equipment data). This Monte Carlo simulation is not used in most plants because of accuracy and cost considerations. Distributions for time to failure and time to restore are determined for the individual equipment or subsystems that make up the plant. Random draws from these individual distributions allow the operating performance of the plant to be simulated numerous times. The composite simulations provide a probability distribution for plant capacity.
SUMMARY OF THE INVENTION
The approach to determining a probabilistic forecast for capacity as shown in this patent application is not based on Monte Carlo or Monte Carlo-like approaches. It is an improvement to the current state-of-the-art in the following ways:
1. The current state-of-the-art method requires the time to failure distributions and time to restore distributions for the individual equipment or subsystems that make up the plant; however, these are seldom known. In practice, these data are often obtained by estimates and there is no way to check the accuracy of the resulting distribution shape. When estimates are used, this approach is only appropriate for comparing the relative results of different equipment configurations or operating strategies. The new process eliminates the need for this particular data. The new process uses commonly available accounting quality data.
2. Current Monte Carlo approaches commonly assume independence between a random draw for time to failure and that for time to restore. Any attempt to account for dependency requires the relationship between time to failure and time to restore to be known. This is usually unknown. Furthermore, any thorough treatment to correct for dependence is difficult and costly. The dependency between failures and their restore times is reflected in the new process. Any failure, and its respective restore time, are treated as an inseparable set.
3. The net effects of the improvements in current state-of-the-art made by this process are: 1) plant management access to a new set of business decision information, and 2) engineers are provided probabilistic data that allows an unprecedented level of analysis for reliability purposes.
Accordingly, a method is provided to determine the capacity distribution of a plant over a zone of interest, the zone having a time length P, the method having the steps of
(1) gathering said plant's historical production data over a selected time period of length L, calculating TBF(k),TTR(k) for each failure (k=1, N failures) and creating the ordered cycle entries (TBF, TTR)(k), each of length TTR(k)+TBF(k), from said production data;
(2) calculating the capacity (C(i,1)) for each contiguous portion of length P of the data;
(3) storing that calculated capacities C(i,1);
(4) randomly shuffling the calculated cycle entries;
(5) calculating the capacities (C(i,2)) for each contiguous portion of length P of the shuffled data set;
(6) storing the calculated capacities C(i,2);
(7) repeating steps (4)-(6) until sufficient capacities are stored to produce a robust capacity distribution;
(8) Calculating a histogram from the capacities C(i,j).
REFERENCES:
patent: 4410950 (1983-10-01), Toyoda et al.
patent: 4827423 (1989-05-01), Beasley et al.
patent: 5634039 (1997-05-01), Simon et al.
patent: 5734567 (1998-03-01), Griffiths et al.
patent: 401088892A (1989-04-01), None
Locks, Mitchell O. Reliability, Maintainability, and Availability Assessment, 1
Barbee Manuel L.
Hoff Marc S.
Jones, Walker, Waechter, Poitevent, Carrere & Denegre, L.L.P.
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