REG NASA-LLIS-0829-2000 Lessons Learned Availability Cost and Resource Allocation (ACARA) Model to Support Maintenance Requirements.pdf

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1、Best Practices Entry: Best Practice Info:a71 Committee Approval Date: 2000-04-19a71 Center Point of Contact: GRCa71 Submitted by: Wil HarkinsSubject: Availability, Cost and Resource Allocation (ACARA) Model to Support Maintenance Requirements Practice: Employ statistical Monte Carlo methods to analy

2、ze availability, life cycle cost, and resource scheduling by using the Availability Cost and Resource Allocation (ACARA) program, which is a software tool developed at Lewis Research Center.Programs that Certify Usage: This practice has been used on the International Space Station Program and LeRC M

3、icro-gravity Experiments.Center to Contact for Information: GRCImplementation Method: This Lesson Learned is based on Maintainability Technique number AT-4 from NASA Technical Memorandum 4628, Recommended Techniques for Effective Maintainability.Benefit:The ACARA program is an inexpensive tool for c

4、onducting maintainability, reliability and availability simulations to assess a systems maintenance requirements over a prescribed time interval. Also, availability parameters such as equivalent availability, state availability (percentage of time at a particular output state capability), and number

5、 of state occurrences can be computed.Provided by IHSNot for ResaleNo reproduction or networking permitted without license from IHS-,-,-Implementation Method:The ACARA program models systems represented by reliability block diagrams comprising series, parallel, and M-of-N parallel redundancy blocks.

6、 A hierarchical description of the system is needed to identify the subsystems and blocks contained in the system. Given a reliability block diagram (RBD) representation of a system, the program simulates the behavior of the system over a specified period of time using Monte Carlo techniques to gene

7、rate block failure and repair intervals as a function of exponential and/or Weibull distributions. ACARA interprets the results of a simulation and displays tables and charts for the following:a71 Performance, i.e., availability and reliability of capacity statesa71 Frequency of failure and repair.a

8、71 Lifecycle cost, including hardware, transportation, and maintenance.a71 Usage of available resources, including maintenance man-hours.ACARA InputsA RBD must be prepared for ACARA to simulate a systems availability. The RBD depicts a system, and the arrangement of the blocks depicts a performed fu

9、nction.RBD does not necessarily depict physical connections in the actual system, but rather shows the role of each block in contributing to the systems function. The blocks are sequentially numbered as B1, B2, B3, etc. and subsystems are numbered as S1, S2, etc, which are defined from the inside ou

10、t. Figure 1 shows an example of a system with its corresponding blocks and subsystems.Provided by IHSNot for ResaleNo reproduction or networking permitted without license from IHS-,-,-refer to D descriptionD Figure 1: Diagram of Blocks and Subsystems Beginning with the innermost set of blocks, each

11、parallel or series set of blocks is partitioned into a subsystem which in turn may combined with other blocks or subsystems.The system shown in Figure 1 contains 6 subsystems:a71 Subsystems 1 and 2 are both variable M-of-N parallel arrangement of batteries. These subsystems respectively contain Bloc

12、ks 6 through 8 and Blocks 9 through 11.a71 Subsystem 3 consists of Subsystems 1 and 2 in parallel.a71 Subsystem 4 is a binary M-of-N parallel arrangement of diodes, Blocks 3 through 5.a71 Subsystem 5 is a parallel arrangement of two turbines, Blocks 1 and 13.a71 Subsystem 6 comprises the entire syst

13、em and is a series arrangement of Subsystems 3 through 5 and Blocks 2 and 12.Modeling Time-to-FailureThe ACARA program uses the Weibull distribution function to model the time-to-failure for the system. The shape and scale factors are adjusted to modify the form of the distribution. Uniform random n

14、umbers from 0 to 1 are generated and substituted for the reliability, R. ACARA uses the Provided by IHSNot for ResaleNo reproduction or networking permitted without license from IHS-,-,-early failure (i.e., infant mortality), random failure, and wearout failure (life-limiting failure) models. These

15、models are adjusted by user-defined parameters to approximate the failure characteristics of each block.Random failure is modeled by the Weibull distribution function where the shape factor is equal to 1 (equivalent to the exponential distribution) and the scale parameter is equal to the Mean Time B

16、etween Failure (MTBF).Wear out failure is also modeled by the Weibull function. The shape factor must be 1 or more. If the block with an initial age (i.e., it is not brand new) is installed, its initial age is subtracted from its first time-to-failure due to wear out. Likewise, if it undergoes a fai

17、lure-free period, this period is added to its first time-to-failure.ACARA generates time-to-failure events using one or a combination of these models and assigns the minimum resulting time for each block as its next failure event. The early failure model is canceled by assigning to the block type an

18、 early failure probability of zero; random failure, by an excessively large MTBF; and wearout failure, by an excessively large mean life.ACARA also simulates redundant pairs of active and standby blocks. A standby block is installed as dormant and its time-to-failure is initially modeled by random f

19、ailure, in which the MTBF is multiplied by its characteristic “Dormant MTBF Factor.“ Then, the corresponding active time-to-failure is modeled by early, random, and wearout failure until the active block is replaced.Modeling Down TimeThe downtime for a failed block depends in part upon the availabil

20、ity of spares and resources. These spares may be local spares, i.e., initially located at the site. If a local spare is available when the block fails, the block is immediately replaced and downtime will depend only on the mean-time-to-repair (MTTR). If no local spares are available, ACARA will sche

21、dule a replacement according to the schedule production quantities for that block type, the constraints on mass, volume, and delay associated with the manifesting and loading spares to the resupply vehicle. ACARA also checks the constraints on the maintenance agents to determine when the block can b

22、e replaced.Once all the above conditions are met to allow the block to be replaced, ACARA then estimates the time required to replace it. The time-to-repair depends upon the MTTRs for that block type. MTTRs may be specified for up to three separate maintenance agents. Examples of maintenance agents

23、are crew, equipment, and robotics. ACARA assumes that the maintenance actions occur simultaneously, so that the blocks repair time is determined by the maintenance agent having the maximum MTTR. During the simulation, the time-to-repair may either be set equal to the maximum defined MTTR or to be de

24、termined stochastically. Refer to Reference 1 for a complete guide on the use of ACARA and the explanation for entering data and the output of graphs and information. ACARA may be obtained from the Computer Software Management and Information Center (COSMIC) at the University of Provided by IHSNot f

25、or ResaleNo reproduction or networking permitted without license from IHS-,-,-Georgia, (706) 542-3265.References:1. Stalnaker, Dale K., ACARA Users Manual, NASA-TM-103751, February 1991.2. Hines, W.W. and Montgomery, D.C., Probability and Statistics in Engineering and Management Science, 2nd Ed., Jo

26、hn Wiley & Sons, 1980Impact of Non-Practice: The development of the Space Station and other space systems (i.e., Space Station payloads and experiments) requiring long-term maintenance support dictates maintenance planning with emphasis on an understanding of the level of support required over a giv

27、en period of time. The program is written specifically for analyzing availability, life cycle cost, and resource scheduling. A combination of exponential and Weibull probability distribution functions are used to model component failures, and ACARA schedules component replacement to achieve optimum

28、system performance. The scheduling will comply with any constraints on component production, resupply vehicle capacity, on-site spares, crew manpower and equipment.Related Practices: N/AAdditional Info: Approval Info: a71 Approval Date: 2000-04-19a71 Approval Name: Eric Raynora71 Approval Organization: QSa71 Approval Phone Number: 202-358-4738Provided by IHSNot for ResaleNo reproduction or networking permitted without license from IHS-,-,-