The Reliability Analytics Toolkit is a collection of on-line tools for performing calculations and analyses commonly used in system reliability analysis. The tools include a variety of easy-to-use calculators such as redundancy calculators, Weibull analysis, reliability growth testing planning and tracking, spare parts requirements, component life-time purchase analysis, environmental impact on system MTBF, field MTBF calculations, confidence intervals, and common probability distributions.
Specific tools include the ability to perform the following:
• Calculate effective MTBF for active and standby redundant configurations.
• Calculate effective MTBF for scenarios where the unit failure rates are not constant.
• Calculate system reliability and possible system states by enumeration of all possible states.
• Estimate the impact on system MTBF form changes in operating environment, temperature and/or part quality levels.
• Calculate a range of MTBFs and MTTRs that will meet a specific system availability requirement.
• Calculate the number of spare parts required to support a system, with the ability to vary confidence level and turn-around-time (TAT) by part
• Calculate the lower one-sided MTBF at a given confidence limit based on the number of unit-hours accumulated and the total number of failures.
• Plan and track reliability growth test using the AMSAA model and methods described in MIL-HDBK-189, Reliability Growth Management.
• Calculate confidence limits for equipment failing in accordance with the exponential distribution.
• Calculate exact one-sided or two-sided confidence intervals based on the binomial distribution.
• Calculate the time required to demonstrate an MTBF with a specified confidence level, assuming an exponential failure distribution.
• Calculate the probability of test sample acceptance using the cumulative binomial distribution.
• Calculate reliability for items failing in accordance with the normal distribution.
• Calculate reliability for items failing in accordance with the Weibull distribution.
• Given time-to-failure data, perform a Weibull data analysis to determine the Weibull shape parameter (β) and characteristic life (η).
• Given time-to-repair data, calculate the mean, median, and maximum corrective time-to-repair, assuming a lognormal distribution.
• Given a Weibull shape parameter (β), characteristic life (η) and a planned maintenance interval, estimate the average failure rate for an item.
Standards: NASA-STD-8729.1 Planning, Developing and Managing an Effective Reliability and Maintainability (R&M) Program
Life-Cycle Cost Analysis for Sustainability & Logistical Support
William R. Wessels, Daniel Sillivant
- Provides practical applications for transferring knowledge to a specific project
- Equips the practicing engineer and manager with tools for meeting reliability requirements at optimal cost
- Includes examples on how to run reliability simulations and surveys available software tools
The world is becoming more aware of the problems of potable water shortages in many countries,
due mainly to scarcity. Even though over 75% of the world is covered in oceans, seas and other
bodies of water, only 2.5% of this water is fresh water.
In countries such as China, poor resource
management has resulted in their waterways becoming contaminated with toxic waste streams,
which has aggravated the problem of obtaining potable water.
Africa and the Middle East are touted as the most pressing areas requiring remediation for water scarcity.
This paper will focus
on the following Middle East countries: Jordan, Syria, Palestine (Gaza and the West Bank), and
The “Handbook of Reliability Prediction Procedures for Mechanical Equipment” has been developed by the Logistics Technology Support Group, Carderock Division, Naval Surface Warfare Center (CDNSWC) in Bethesda, Maryland. The handbook presents a new approach of determining the reliability and maintainability (R&M) characteristics of mechanical equipment. It has been developed to help the user identify equipment failure modes and potential causes of unreliability in the early design phases of equipment development, and then to quantitatively evaluate the design for R&M and determine logistics support requirements.
DoD NEWS ANNOUNCEMENT:
On May 8, 2013, the Department of Defense released a major update to the Defense Acquisition Guidebook (DAG) Chapter 4, Systems Engineering. The DAG Chapter 4 is the primary reference on the use of systems engineering throughout the system life cycle.
With this release, the chapter has been restructured to provide Program Managers and Systems Engineers with life cycle phase and systems engineering technical review expectations, including a knowledge-based, technical-maturity table for key events. The chapter provides details on systems engineering technical and technical management processes and includes links to relevant policy, standards, and detailed guidance on key topics. The Office of the Deputy Assistant Secretary of Defense for Systems Engineering developed Chapter 4 working closely with contributors from 24 different organizations across the Department. The update reflects recent policy changes and Better Buying Power initiatives. It emphasizes the role of systems engineering in providing balanced solutions (managing the system's cost, schedule, performance, and risk) to deliver Warfighter capability needs.
View the DAG Chapter 4: https://acc.dau.mil/dag4
Review the Overview Briefing: http://www.acq.osd.mil/se/docs/2013_05_07_DAGC4_Update_Briefing_Final.pdf
with any questions, comments, or suggestions for future updates.