T h e J o u r n a l o f t h e R e l i a b i l i t y A n a l y s i s C e n t e r S e c o n d Q u a r t e r - 2 0 0 4 3 challenges?" This holds true not only at NASA. The author is confident that you have witnessed this, too, in your own organi- zation among all levels of practitioners. "Because professionalism is still mainly identified with technical expertise," writes Donald Schon in The Reflective Practitioner, "reflection-in-action is not generally accepted--even by those who do it--as a legitimate form of professional knowing" (Reference 6). We should appreciate Schon's insight here, because it may help to explain why it has been so difficult for other storytelling initia- tives to get off the ground, even where there are enthusiastic pro- ponents for storytelling within the organization. There is much to be said for consistency. Our work on ASK Magazine did not come crashing out of the gate with broad accept- ance throughout NASA. It was new, it was different, and there was already some cynicism, based on the ineffectiveness of the LLIS, about initiatives aimed at providing lessons to help practi- tioners to do their jobs better. That we arrive in NASA mailbox- es every two months with stories by the "best of the best practi- tioners" has gone a long way towards winning over skeptics. Like most fledgling initiatives, we began on a small scale, start- ing initially as a web-based publication and with a distribution list of a few hundred, mostly NASA project managers who were already happy customers of other APPL products. A print publi- cation followed for marketing purposes, and to address the most common observation about ASK's early issues: "I wish there was a way I could read these stories while I was on the plane." ASK was fortunate to have a sponsor in APPL Director Dr. Edward Hoffman, whose own work on storytelling with ASK Editor-in-Chief Dr. Alexander Laufer (including a book, Project Management Success Stories) (Reference 7), gave him faith and confidence that with time the magazine would find wide accept- ance, and it has. Testimonials about the efficacy of ASK lessons run the gamut from cog engineers to center directors and associ- ate administrators. The push is on throughout the Federal gov- ernment and across industry to capture knowledge, and find mechanisms like ASK to get that knowledge to the people who need it. And so that's our story. Are you reflecting on yours? References 1. ASK Magazine can be accessed at . 2. Schon, D.A., The Reflective Practitioner, Basic Books, New York, 1983; Schon, D.A., Educating the Reflective Practitioner, John Wiley & Sons, Inc., San Francisco, 1987. 3. D.A. Schon, (1983), p. 62. 4. D.A. Schon, (1987), p. 17. 5. . 6. D.A. Schon, (1983), p. 69. 7. A. Laufer and E. Hoffman, (2000) Project Management Success Stories: Lessons of Project Leaders, John Wiley & Sons, New York. About the Author Todd Post is the editor of ASK Magazine, and has published other articles about ASK in Knowledge Management (Dec. `01/Jan. '02), the Knowledge Management Review (March/April '02), and Program Manager (Jan./Feb. '03). He welcomes your comments on this article and about ASK Magazine at . Product Assurance Capability (PAC) Quantified By: Ananda Perera, Honeywell Engines Systems & Services Introduction The Product Assurance (Reliability, Maintainability, and Quality Assurance (RM&QA)) programs are an integral part of the contrac- tor (supplier) operations and, as such, are planned and developed in conjunction with other activities to attain the following goals: a. Recognize RM&QA aspects of all programs and provide an organized approach to achieve them. b. Ensure RM&QArequirements are implemented and com- pleted throughout all program phases of design, develop- ment, processing, assembly, test and checkout, and use activities. c. Provide for the detection, documentation, and analysis of actual and potential discrepancies, system(s) incompati- bility, marginal reliability, maintainability and quality, and trends that may result in unsatisfactory conditions. The RM&QA program provides for participation, by RM&QA personnel, in all phases of the design, development, and manu- facturing process. This effort should include reviews and assess- ments of: human factors, design, hazard analyses, failure mode and effect analyses, test plans, and procedures. Quantifying a single QA metric is difficult; however R&M together can be quantified and it is called Product Assurance Capability (PAC). Product Assurance Capability is defined as the combined Probability that an Item will perform its required functions for the duration of a specified mission profile and that the repair action under given conditions of use is carried out within a stated time interval. Many times, reliability is represented by MTBF and maintain- ability is represented by MTTR. These metrics are used to cal- culate Inherent Availability (Ai) which shows the capability of the end-unit for service. Inherent Availability is the probability that the system/equipment is operating satisfactorily at any point in time when used under stated conditions, where the time con- sidered is operating time and active repair time. MTTR MTBF MTBF Ai + = T h e J o u r n a l o f t h e R e l i a b i l i t y A n a l y s i s C e n t e r S e c o n d Q u a r t e r - 2 0 0 4 4 Ai becomes a useful term to describe combined reliability and maintainability characteristics. Since this definition of availabil- ity is easily measured, it is frequently used as a contract-speci- fied requirement; however it is not a good Product Assurance Capability metric. Reliability and Maintainability (R&M) Design Philosophy Reliable equipment has a high probability of performing its required function without failure for a stated period of time when subjected to specified operational conditions of use and environ- ment. The operational use and environment, therefore, need to be taken into account at the outset of the design process. The design should also be robust to expected variations in production processes and quality of materials and components. The ease with which the equipment can be returned to usable con- dition after failure and the time needed for preventive mainte- nance are important design criteria. Those items which need to be removed, adjusted, or inspected most often, for whatever reason, should have the easiest accessibility, so maintainability design is significantly reliability-driven but not reliability-dependent. R&M, then, are related activities that need to be fully integrated into all other project activities. Treating R&M subsequent to design can lead to a situation where the unreliability and inferior supportability are discovered at the end of development, with the consequent remedial action causing expense and delay. Reliability and maintainability drive the logistics support aspects and hence have a significant effect on the life cycle cost of the equipment/system. R&M General Considerations R&M design philosophy should be applied at all stages of the project life cycle, from initial conceptual studies through to the In-Service phase. R&M directly affect both operational effec- tiveness and life cycle cost and merit equal consideration with other parameters such as performance, acquisition cost, and proj- ect time scale. It requires that the contractor should integrate R&M aspects into each stage of the design activity. At the conceptual stage, the R&M requirements should be con- sidered at the same time as the performance parameters. They should be justified in terms of operational need (e.g.: probabili- ty of mission success, available maintenance manpower), so that they will receive due consideration in any subsequent trade-off. As the operational concept develops the R&M requirements should be reviewed. The design procedure should: a. Ensure that an analysis is conducted of the operating and environmental conditions, and also ensure that system and sub-system design specifications incorporate the results. b. Embody R&M design criteria, and evolve a system that is no more complex than is adequate to satisfy its perform- ance requirements. c. Ensure that the mechanisms of failure and their effects are thoroughly analyzed and understood, that critical features are identified and that the design process aims to reduce the effects of failure modes where possible. d. Utilize materials and components that are procured to approved quality standards and ensure that, in applica- tion, they are subject to stresses that are well within their strength/rating capabilities. e. Take producibility into account, ensuring that as far as pos- sible, the design is insensitive to the expected variability of the materials, components and production processes. f. Generate a system that is easy to test, for which failures are accurately diagnosed and isolated, with a configura- tion that facilitates easy maintenance and repair under field conditions, including the appropriate level of inte- grated diagnostic capability (Built-in-Test (BIT)). The Objective of Quality Assurance (QA) The objective of Quality Assurance is to provide adequate confi- dence to the customer that the end product or service satisfies the requirements. The Quality Assurance policy is to ensure, in conjunction with other integrated project and Product Assurance functions, that required quality is specified, designed-in and will be incorporat- ed, verified and maintained in the relevant hardware, software and associated documentation throughout all project phases, by applying a program where: ˇ Assurance is provided that all requirements are adequate- ly specified. ˇ Design rules and methods are consistent with the project requirements. ˇ Each applicable requirement is verified through a verifi- cation program that includes one or more of the following methods: analysis, inspection, test, review of design, and audits. ˇ Design and performance requirements including the spec- ified margin are demonstrated through a qualification process. ˇ Assurance is provided that the design is producible and repeatable, and that the specification of the resulting product can be verified and operated within the required operating limits. ˇ Adequate controls are established for the procurement of components, materials, software and hardware items, and services. ˇ Fabrication, integration, test and maintenance are con- ducted in a controlled manner such that the end item con- forms to the applicable baseline. ˇ A nonconformance control system is established and maintained to track non conformances systematically and to prevent reoccurrence. T h e J o u r n a l o f t h e R e l i a b i l i t y A n a l y s i s C e n t e r S e c o n d Q u a r t e r - 2 0 0 4 5 ˇ Quality records are maintained and analyzed to report and detect trends in a timely manner to support preventive and corrective maintenance actions. ˇ Inspection, measuring and test equipment and tools are controlled to be accurate for their application. ˇ Procedures and instructions are established that provide for the identification, segregation, handling, packaging, preservation, storage and transportation of all items. ˇ Assurance that the operations including post-mission and disposal are carried out in a controlled way and in accor- dance with the relevant requirements. R&M Engineering Functions and Tasks An essential task for an R&M Engineer is estimating the preci- sion of an estimate (say MTBF, MTTR). This is an important task leading to the use of Confidence Intervals. When we use two-sided confidence bounds (or intervals), we are looking at a closed interval where a certain percentage of the population indicating a result is likely to lie. For example, when dealing with 90% two-sided confidence bounds of (X, Y), we are saying that 90% of the population lies between X and Y. One-sided confidence bounds are essentially an open-ended ver- sion of two-sided bounds. A one-sided bound defines the point where a certain percentage of the population is either higher or lower than the defined point. Most of the time one-sided confi- dence bounds are used for MTBF and MTTR estimates. MTBF is calculated at lower (Why? Usually the upper boundary is not known; If the true MTBF is greater than the "lower", the cus- tomer will be "happy") one-sided limit and MTTR is calculated at upper (Why? Usually the lower boundary is not known; If the true MTTR is less than the "upper", the customer will be "happy") one-sided limit. The Chi-Square (2) Distribution can be used to find the confidence intervals of the MTBF or MTTR. When there are no failures in a time period, the Chi-Square Distribution is used to find the MTBF at the lower bound. 1. Reliability prediction is a process of mathematically combin- ing the parts and elements of a system to obtain a single numerical figure that represents the system's probability of success. In reliability prediction, we usually assume that all components are required for successful system operation, resulting in the use of a series reliability model for prediction of system reliability. Since we're using a series model, we can predict such parameters as MTBF (MTTF), but the model should not be used for operational reliability parameters such as MTBCF, unless the effect of redundant components is included in the calculation. The goal should be to try to pre- dict system behavior at least to the extent necessary to identi- fy possible risk areas or areas where the system reliability needs to improve to meet requirements. One method of reliability prediction still popular in the defense contractor community (popular because a lot of contractors are experienced with using it, not necessarily because it's particu- larly good) is the Parts Count and Parts Stress method of MIL- HDBK-217. In this method, each generic type of component is assigned a basic failure rate that depends on component type and operational environment. The basic failure rate can be adjusted by multiplying it by factors that account for pre- sumed component quality, manufacturing learning curve, etc. RAC findings have shown that failures also stem from non- component causes, namely design deficiencies, manufacturing defects, poor system management techniques etc. The RAC PRISM methodology determines an initial base failure rate based on PRISM component models. This failure rate is then modified with system level process assessment factors to give a truer failure rate prediction. 2. The mean time to repair (MTTR) is perhaps the most com- mon and most useful measure of maintainability. It is often included in system or product specifications because it's eas- ier to visualize an average than a probability distribution, and the mean is also easier to include in calculations than a distri- bution function would be. In general MTTR of a system is an estimated average elapsed time required to perform corrective maintenance, which consists of fault isolation and correction. For analysis purposes, fault correction is divided into disas- sembly, interchange, re-assembly, alignment, and checkout tasks. MTTR is a useful parameter that should be used early in planning and designing stages of a system. The parameter is used in assessing the accessibility and locations of system components, and it highlights those areas of a system that exhibit poor maintainability in order to justify improvement, modifications, or a change of design. The assessed or esti- mated (Estimating methods are available in MIL-HDBK- 472) MTTR helps in calculating the life cycle cost of a sys- tem, which includes cost of the average time technicians spend on a repair task. 3. Testability is a measure of the ability to detect system faults and to isolate them at the lowest replaceable component(s). The speed with which faults are diagnosed can greatly influ- ence downtime and maintenance costs. As technology advances continue to increase the capability and complexity of systems, use of automatic diagnostics as a means of Fault Detection Isolation and Recovery (FDIR) substantially reduces the need for highly trained maintenance personnel and can decrease maintenance costs by reducing the erro- neous replacement of non-faulty equipment. FDIR systems include both internal diagnostic systems, referred to as built- in-test or built-in-test-equipment (BITE), and external diag- nostic systems, referred to as automatic test equipment (ATE). BIT Effectiveness (BITEFF) is the probability of obtaining the correct operational status of the system using BIT. It is a function of: Total System Failure Rate (), Fault Detection Capability (FDC), False Alarm Probability (FAP), and The operating time (T) required to conduct BIT. BITEFF is expressed by the following mathematical function: Minimum [Worst Case, when T ] BITEFF = FDC/(1 + ( ) [ ] ( ) [ ] FAP 1*T*-FAP 1*T*- e - 1 * FAP 1 FDC e ++ + + T h e J o u r n a l o f t h e R e l i a b i l i t y A n a l y s i s C e n t e r S e c o n d Q u a r t e r - 2 0 0 4 6 FAP). If BITEFF is high, Repair Times and MTTR will be reduced. Of potential concern is the fact that false alarms and removals create a lack of confidence in the BIT system to the point where maintenance or operations personnel may ignore fault detection indications. Product Assurance Capability (PAC) Model Description The PAC metric is a combination of Reliability and Maintainability Functions based on the Weibull Distribution. The R&M equations are shown in Figures 1 and 2. In these equations (1/ + 1) is the Gamma Function evaluated at the value of (1/ + 1). The "Mathcad" software is used to calculate R&M values and the PAC values shown in the figures and sum- marized in Table 1. Figure 1. Calculation of Reliability Metrics Figure 2. Calculation of Maintainability Metrics and Product Assurance Capability The Weibull, distribution has gained popularity as a time-to-fail- ure distribution. The Weibull distribution is characterized by two parameters, a scale parameter, the characteristic life, , and a shape parameter, . The characteristic life, , is the same as the mean time to failure when = 1. Often is replaced for compu- tational convenience by its inverse, = 1/, which can be defined as the failure rate. The two-parameter Weibull distribution is given by f(t) = / (t/)-1exp-(t/), t 0. The reliability func- tion is R(t) = exp-(t/). One reason for the popularity of the Weibull distribution is that times to failure are better described by the Weibull distribution than the exponential. For physics of failure approaches to relia- bility, the Weibull distribution is preferred. An advantage of the Weibull distribution is that it represents a whole family of curves, which, depending on the choice of , can represent many other distributions. For example, if = 1, the Weibull distribu- tion is exactly the one-parameter exponential distribution. A of approximately 3.3 gives a curve that is very close to the normal distribution. The infant mortality and wear-out portions of the bathtub curve can often be represented by the proper Weibull dis- tribution. In the three-parameter Weibull distribution, a location parameter, , is used to account for an initial failure-free operat- ing period or prior use (e.g., burn-in). In the R&M Functions given in Figures 1 and 2, = 1 and = 3.3 are selected, and can be assumed for new equipment. For equipment already in use, Weibull analysis of the failure and repair data needs to be performed to obtain true values. The calculations of the reliability metrics and maintainability metrics are shown in Figures 1 and 2, respectively. Table 1 summarizes the Ai and PAC metrics. Table 1. Comparison of Ai, and PAC Metrics Using Different Combinations of MTBF, MTTR, t, and T Notes: 1. In hours. 2. In minutes. 3. This means in the given operational environment, 9,989 out of 10,000 systems are available for service at any time in the useful life period. 4. For this combination of MTBF, MTTR, t & T the differ- ence between Inherent Availability and Product Assurance Capability is high. Summary To achieve high operational effectiveness with low life cycle cost, the RM&QA of systems should be given full consideration at all stages of the procurement cycle. This process should begin at the concept stage of the project and be continued in a disci- plined manner as an integral part of the design, development, production, and testing process and subsequently into service. MTBF1 MTTR2 Ai PAC t2 T1 30,000 20 0.999989 0.9989063 40 2 10,000 20 0.999967 0.930108 30 1.5 5,0004 204 0.9999334 0.5026274 204 14 30,000 15 0.999992 0.999933 40 2 30,000 10 0.999994 0.999950 30 1.5 30,000 5 0.999997 0.999966 20 1 T 2 Specified Mission Time (Hours) MTBF 10000 12000 , 30000 .. Mean Time (Hours) Between Failure Range 1 Weibull Shape ( Exponential ) Parameter Reliability (R) Function RMTBF e T MTBF 1 1 . RMTBF MTBF 1 104 1.4 104 1.8 104 2.2 104 2.6 104 3 104 0.99975 0.99979 0.99983 0.99987 0.99991 0.99995 MTBF 1 104 . 1.2 104 . 1.4 104 . 1.6 104 . 1.8 104 . 2 104 . 2.2 104 . 2.4 104 . 2.6 104 . 2.8 104 . 3 104 . RMTBF 0.99980002 0.99983335 0.99985715 0.99987501 0.9998889 0.9999 0.9999091 0.99991667 0.99992308 0.99992857 0.99993334 t 40 Required Restoration Time (Minutes) MTTR 14 16 , 32 .. Mean Time (Minutes) To Repair Range 3.3 Weibull Shape ( Normal ) Parameter Maintainability (M) Function M MTTR 1 e t MTTR 1 1 . M MTTR MTTR 10 14.4 18.8 23.2 27.6 32 0.7 0.76 0.82 0.88 0.94 1 M MTTR 1 0.99999943 0.99994122 0.99897267 0.99342131 0.97694776 0.94469247 0.89635823 0.8355667 0.76752148 MTTR 14 16 18 20 22 24 26 28 30 32 Product Assurance Capability with MTBF=30000 Hrs & MTTR = 20 Minutes is ------> P R30000 M 20 . P 0.99890608 = T h e J o u r n a l o f t h e R e l i a b i l i t y A n a l y s i s C e n t e r S e c o n d Q u a r t e r - 2 0 0 4 7 Introduction This article discusses the reliability aspects of several emerging types of human-machine interfaces. These new interfaces are sub- stantially different from the now common interfaces of keyboards, mice, touch pads, and touch screens and the less common voice- driven interfaces. Readers who desire to acquaint or re-acquaint themselves with the fundamentals of the current common types of interfaces are encouraged to consult the RAC guide entitled "A Practical Guide to Developing Reliable Human-Machine Systems and Processes," (Order No. RAC-HDBK-1190, HUMAN). Those who desire a more interactive discussion of the fundamentals and a more extensive discussion of the new technologies should con- sider the RAC Human Factors (reliability-oriented) short course. Information on the referenced guide and course can be found at the RAC web site at . EEG-Based Computer Control One of the most exciting developments in human-machine inter- faces is implementing the control of computers by human thought. Based on the fact that the brain prepares for a moving a limb a full half-second before the limb actually is moved, com- puter scientists at the Fraunhofer Institute for Computer Architecture and Software Technology and the Benjamin Franklin University Clinic, both in Berlin, and the University of British Columbia (Reference 1) among others have been investi- gating controlling computers by thought alone. The long-term objective of this research is to create a multi-position, brain-con- trolled switch that is activated by signals measured directly from an individual's brain. By fitting subjects with an electroen- cephalograph (EEG) and training the students for approximately 200 hours, the scientists have been able to get the students to move simple objects on a computer screen. The scientists rec- ognize that the interface must be able to determine the intention of the human in a single reading of brain waves. This requires filtering out noise produced by both the brain and the EEG equipment. Two current disadvantages of the current EEG approach are that the EEG equipment is too expensive for com- mercial use yet and that a conductive gel is required to ensure a good electrical interface. Figure 1 illustrates the configuration for EEG-based control of computers. Figure 1. EEG-Based Control of Computers (Continued on page 10) By: Kenneth P. LaSala, Ph.D., KPL Systems Operational (Mission & Restoration) Success R&M parameters relate to the probability of failures occurring during a Mission Time that would cause an interruption of that Mission and to the probability of correcting these failures during the required Restoration Time. The PAC Metric represents the overall Operational Success and can be calculated using predicted and/or estimated MTBF & MTTR Data. If there is an Inherent Availability requirement, it is recommended that the PAC Metric be used for accuracy and good customer satisfaction. Glossary of Terms Reliability is the probability that an item can perform its function under stated conditions for a given amount of time without failure. Maintainability is the probability that an item can be retained in, or restored to, a specified condition when maintenance is per- formed by personnel having specified skill levels, using prescribed procedures and resources, at each prescribed level of maintenance and repair. The term is also used to denote the discipline of study- ing and improving the maintainability of products, (e.g., by reduc- ing the amount of time required to diagnose and repair failures). MTTF stands for Mean Time To Failure and is represented by the mean life value for a failure distribution of non-repairable units. MTBF stands for Mean Time Between Failure and is represented by the mean life value for a failure distribution of repairable units. MTBCF stands for Mean Time Between Critical Failure, and is the average time between failures which causes a loss of a sys- tem function defined as "critical" by the user. MTTR stands for Mean Time To Repair and is represented by the mean life value for a distribution of repair times. Availability is a performance criterion for repairable systems that accounts for both the reliability and maintainability properties of a component or system. It is defined as the probability that a system is not failed or undergoing a repair action when it needs to be used. Mission Time is the portion of the up time required to perform a specified mission profile. Restoration Time is the time taken to restore the delivery of service, when the repair is carried out by an adequately skilled The Reliability Implications of Emerging Human Interface Technologies (Continued on page 23) T h e J o u r n a l o f t h e R e l i a b i l i t y A n a l y s i s C e n t e r S e c o n d Q u a r t e r - 2 0 0 4 23 Electronic Design Reliability This intensive course is structured for all key participants in the reliability engineering process. Included are systems and circuit design engineers, quality engineers and members of related dis- ciplines having little or no previous reliability training. The course deals with both theoretical and practical applications of reliability; all considerations related to the design process includ- ing parts selection and control, circuit analysis, reliability analy- sis, reliability test and evaluation, equipment production and usage, reliability-oriented trade-offs, and reliability improve- ment techniques. Reliability Engineering Statistics The Reliability Statistics Training Course is a three-day, applica- tions-oriented course on statistical methods. Designed for the practitioner, this course covers the main statistical methods used in reliability and life data analysis. The course starts with an overview of the main results of probability and reliability theory. Then, the main discrete and continuous distributions used in reli- ability data analysis are overviewed. This review of reliability principles prepares the participants to address the main problems of estimating, testing and modeling system reliability data. Course materials include the course manual and RAC's publica- tion "Practical Statistical Tools for the Reliability Engineer." Weibull Analysis This three-day hands-on workshop starts with an overview of best practice Weibull analysis techniques plus a quick illustrative video of three case studies. The entire New Weibull HandbookŠ by Dr. Abernethy, the workbook provided for the class, is cov- ered beginning with how to make a Weibull plot, plus interpreta- tion guidelines for "good" Weibulls and "bad" Weibulls. Included are failure prediction with or without renewals, test planning, regression plus maximum likelihood solutions such as WeiBayes, and confidence calculations. All students will receive WinSMITHTM and VisualSMITHTM Weibull software and will get experience using the software on case study problems from industry. Computers are provided for the class. Related tech- niques Duane/AMSAA Reliability Growth, Log-Normal, Kaplan-Meier and others will be covered. This class will prepare the novice or update the veteran analyst to perform the latest probability plotting methods such as warranty data analysis. It is produced and presented by the world-recognized leaders in Weibull research. For more information . Date: November 2-4, 2004 Location: Orlando, FL Upcoming November Training repairman who has the necessary tools, equipment and spare parts, etc. Restoration Time is denoted as the active repair time. FDC is the ratio of "BIT Detectable System Failure Rate" and the "Total System Failure Rate". FAP is the ratio of the "BIT False Alarm Rate" and the "Total System Failure Rate" excluding "Failure Rate of BIT Circuitry" For Further Study 1. Def Stan 00-40 (Part 4) "Reliability and Maintainability Part 4: Guidance for writing NATO R&M Requirements Documents" (Issue 2 Publication Date 13 June 2003). 2. Def Stan 00-41 "Reliability and Maintainability Mod Guide to Practices and Procedures (Issue 3 Publication Date 25 June 1993). 3. SSP 50182 "NASA/ASI Bilateral Safety and Product Assurance Requirements" (Publication Date 2 May 1996). 4. ECSS-Q-00A "Space Product Assurance: Policy and Principles" (Publication Date 19 April 1996). 5. NASA PLLS Database ­ Lesson 0827: Quantitative Reliability Requirements Used as Performance-Based Requirements for Space Systems. 6. NASA PLLS Database ­ Lesson 0831: Maintainability Program Management Considerations. 7. NASA PLLS Database ­ Lesson 0835: Benefits of Implementing Maintainability on NASA Programs. 8. NASA PLLS Database ­ Lesson 0837: False Alarm Mitigation Techniques. 9. NASA PLLS Database ­ Lesson 0841: Availability Prediction and Analysis. About the Author Ananda Perera has 25 years of North American experience in Reliability/Maintainability/Safety Engineering. He is presently employed at Honeywell Engines Systems & Services, Ontario, Canada as a Reliability/Maintainability Engineer for 21 years. Mr. Perera has a Bachelor of Science in Production Engineering (1972) from the University of Aston, Birmingham, England. He is a Professional Engineer (1976 to present) and a member of the Association of Professional Engineers of Ontario. He is also a Certified Reliability Engineer (American Society for Quality) (1983 to present). He is Honeywell Six Sigma Plus Green Belt Certified (2001) and Design For Six Sigma Certified (2003). His published papers are: Adaptive Environmental Stress Screening ˇ Reliability of Mechanical Parts ˇ Optimum Cost Maintenance. Product Assurance ... (Continued from page 7)