If you are in budget jail and have tried to get out by preaching reliability to the people above you but have made little headway, here is a plan to break you out. Point P on the P-F Curve is where a defect enters a machine. At some time in the future, this will cause a functional loss of some kind. As a defect lingers in a machine, the machine functionality decreases over time.
At some point in the future, Point F, total failure of the machine occurs. Create an equipment bill of materials. An equipment bill of material lists all the components of an asset, including its assemblies and subassemblies. With a reliable equipment bill of materials, a planner can determine exactly what parts are needed. And in an emergency, it provides valuable information to craftsmen and others to ensure that the right parts are identified and procured.
Use P-F intervals to map and avert failures. The P-F interval is a valuable piece of information for any maintenance team, and you don't need special education to use it. The use of P-F intervals in determining the right maintenance to perform at the right time need not be confined to RCM. Consider a continuous monitoring system. Continuous monitoring is the application of dedicated devices for collecting predictive maintenance-style data to aid in a condition monitoring program.
With each passing year, this technology gets cheaper, and the desire for more complex and more robust monitoring gets larger. Build a strong relationship with operations. To get better at maintenance, you must get better at building a positive relationship with operations. To achieve maintenance excellence, you must have an excellent relationship.
This means having maintenance in full alignment with the larger goals of your operations and your company. Quantify the cost of a functional failure mode.
What is the real cost of a failure? Unfortunately, we don't know until after the failure has occurred - and reliability is about avoiding the failure. Develop standard maintenance procedures. Plants often fail to see the importance of having well-written procedures for most tasks. This article discusses the importance of having good procedures and presents the details needed to develop well-written standard maintenance procedures.
Manage assets by criticality. Through proper construction of the criticality analysis model, reliability engineering will be able to illustrate what reliability enhancements must be made to manage criticality, thus improving their ability to manage assets by criticality. Operators in a reliability-focused culture should have a questioning attitude and be very observant. The inclusion of the S-A-5Whys tool in their skill set will benefit the organization by the early identification and resolution of problems, leading to increased asset reliability.
Get more out of your EAM. All EAM systems contain the same basic capabilities in support of your maintenance program. They are like any other software package — their success depends on how they are implemented and, more importantly, how they are used. Optimize outages with effective task planning.
Outages can have elaborate schedules, but often are unsuccessful due to ineffective advanced planning, which results in inefficient work execution and outage schedule overruns. Put multiple CBM tools to use.
It is essential to understand how equipment performs in a facility and to be able to predict and prevent failures before they happen. The results of the combination of condition-based monitoring technologies will give the reliability engineer an even greater confidence when communicating to management when an asset is approaching an impending failure. Apply the correct maintenance strategies. True reliability is achieved when the most cost-effective methods are applied to the assets in your plant, thereby maximizing reliability with the minimum total cost to the business.
Benchmark your lubrication program. Benchmarking provides a much-needed scorecard for areas of lubrication that may not be obvious or often considered for improvement. Detect machine problems early. Remove process bottlenecks. If your process bottlenecks are linked closely to the maintenance and reliability of your equipment, it is most likely you have a highly reactive maintenance organization. To move from a primarily reactive regime, significant focus must be placed on developing and deploying systems that move the organization toward being proactive.
Optimize PM tasks. Unfortunately, most preventive maintenance tasks lack the detail that will provide quantitative data for equipment history, and they are written without considering failure modes.
The solution is to practice Preventive Maintenance Optimization PMO , using all aspects to write PM procedures that are value added, comprehensive, repeatable, organized, and specify a correct duration and interval of execution. Create a lean and effective oil analysis program. This course is designed to teach introductory level skills in HRA and includes a broad introduction to HRA and its applications. A discussion of HRA strengths, limitations and results is also included.
Specific topics are HRA process, HRA standards, interface between HRA and probabilistic risk assessment PRA , human error probability estimates, error taxonomies, performance shaping factors, human performance measurement, HRA good practices, task analysis, task dependency, error recovery, uncertainty estimates, safety culture, effects of automation, human performance data, and "HRA-informed" understanding of noteworthy events.
The participants are expected to have prior knowledge in basic statistics, probability and the processes that lead to human failures. Nevertheless, some refresher topics will be briefly reviewed. This hour course explains some of the tools and techniques that can be used to design maintainability into a program or project to enhance the maintainability of a system or equipment.
The course begins with an overview of maintainability. Module 2 discusses the impact of maintainability on operations and cost. Modules 3 and 4 discuss managing for maintainability and setting maintainability requirements. Module 5 identifies specific maintainability considerations for a program or project. Module 6 explains the importance of testability and diagnostics. Module 7 describes the design tools used for maintainability. Module 8 discusses the methods used in Maintainability Analysis.
Module 9 describes the reasons maintainability testing is performed and basic methods of maintainability testing. Module 10 explains the importance of maintainability data collection and analysis. This course focuses on the application of data collection, methods and analysis.
It discusses data pedigree and underlying assumptions that need to be considered when collecting and using data for analysis and decision-making. It covers various data types and sources such as discrete, continuous, attribute, vendors, field, commercial, surrogate systems and so on. The eight-hour course focuses on the collection and analysis of reliability data to make informed decisions. Topics covered include beginner-to-advanced topics in the collection and analysis of reliability data, including.
A strategically planned and executed experiment gives a great deal of information about the effect on a response variable due to one or more factors.
This course covers basics of Design of Experiment, steps for designing and conducting effective experiments, and statistical and graphical tests for significance. It teaches how to set up, conduct and analyze factorial-designed experiments. For more information, contact a PoC at your center. John W. Evans has over 30 years of experience and has held positions in industry and academia, in addition to various to positions at NASA. He has served as a consultant in reliability engineering to major companies worldwide, having worked throughout Asia and Europe.
Evans holds a doctorate degree in materials science and engineering from Johns Hopkins University, a master's degree in chemical and materials engineering from the University of Iowa, and a Bachelor of Science degree in mechanical engineering from the University of Nebraska. He has authored or co-authored more than 60 technical publications and three books including Product Integrity and Reliability in Design. He also has been serving as an adjunct professor in the Department of Industrial and Systems Engineering at the Morgan State University in Baltimore, Maryland, where he has developed courses for the Systems Engineering program.
Reliability and Maintainability News. Cornford and Martin S. Learn More More. Print Version. She believes in outreach and mentoring the next generation of engineers.
In general, PRA is a process that seeks answers to three basic questions: What kinds of events or scenarios can occur i. What are the likelihoods and associated uncertainties of the events or scenarios?
What consequences could result from these events or scenarios? See NPD See NPR Key topics include Fault Tree Analysis, Reliability Block Diagram RBD and simulation modeling Standard classical analyses and equivalent Bayesian RBDs, fault trees, math models and simulation models Using a simulation to drive system design and ultimately positively affect probability of mission success Optimization techniques and tools e. Key topics include Reliability growth management and [lanning Developing a planned growth curve Estimating the initial reliability, maximum reliability or growth potential Assigning reliability growth fix effectiveness factor in planning stage Reliability growth analysis, implementation and strategy management Army Materiel Systems Analysis Activity reliability growth model Reliability growth confidence interval, goodness-of-fit test, fix effectiveness factor, growth rate Evaluating the reliability using operational test data or field data Course Number: SMA-RM-EXTI Length: Key topics include Terms and definitions Requirements for test and understanding requirements Test plans and planning Different types of test to verify RAM performance Accelerated test methods Closing the loop — feeding back test results to the engineering process Test analyze fix Design of experiments Test plan examples Data collection methodologies Test plan development exercise Test data analysis exercise Course Number: SMA-RM-WBT Length: Topics covered include beginner-to-advanced topics in the collection and analysis of reliability data, including Understanding of variation Probability concepts Rules of probability Probability distributions Discrete distributions Continuous distributions Statistical confidence Statistical hypothesis testing Non-parametric inferential methods Goodness of fit Series of events point processes Computer software for statistics Reliability data collection and analysis Methods of modeling systems for reliability analysis Mathematical techniques used to analyze and solve reliability problems Probabilistic life models for components with both time-independent and time-dependent loads Data analysis, parametric and nonparametric estimation of basic time-to-failure distributions Data analysis for systems Repairable systems modeling Course Number: SMA-RM-WBT Length: 8.
This course presents concepts, principles, methods and tools to improve experiment design. NPR NPD See Paper. View University of Maryland Site. For example, software "malfunctions" are often recoverable with a reboot, and the time for reboot may be bounded before a software failure is declared. Another issue to consider is frequency of occurrence even if the software reboot recovers within the defined time window as this will give an indication of software stability. User perception of what constitutes a software failure will surely be influenced by both the need to reboot and the frequency of "glitches" in the operating software.
One approach to assessing software "fitness" is to use a comprehensive model to determine the current readiness of the software at shipment to meet customer requirements.
Such a model needs to address quantitative parameters not just process elements. In addition, the method should organize and streamline existing quality and reliability data into a simple metric and visualization that are applicable across products and releases.
A novel, quantitative software readiness criteria model [2] has been developed to support objective and effective decision making at product shipment.
The model has been "socialized" in various forums and is being introduced to MITRE work programs for consideration and use on contractor software development processes for assessing maturity. The model offers:. Using this approach with development test data can measure the growth or maturity of a software system along the following five dimensions [2]:. Many U. The Department of Defense DoD has been the initial proponent of systematic policy changes to address these findings, but similar emphasis has been seen in the Department of Homeland Security DHS as many government agencies leverage DoD policies and processes in the execution of their acquisition programs.
As evidenced above, the strongest government support for increased focus on reliability comes from the DoD, which now requires most programs to integrate reliability engineering with the systems engineering process and to institute reliability growth as part of the design and development phase [4].
The scope of reliability involvement is further expanded by directing that reliability be addressed during the Analysis of Alternatives AoA process to map reliability impacts to system LCC outcomes [5]. Elevation of these RAM requirements to a KPP and supporting KSAs will bring greater focus and oversight, with programs not meeting these requirements prone to reassessment and reevaluation and program modification.
Subject matter expertise matters. Consistent RAM requirements. The upper level RAM requirements should be consistent with the lower level RAM input variables, which are typically design related and called out in technical and performance specifications. A review of user requirements and flow down of requirements to a contractual specification document released with a Request For Proposal RFP package must be completed.
If requirements are inconsistent or unrealistic, the program is placed at risk for RAM performance before contract award. Ensure persistent, active engagement of all stakeholders. RAM is not a stand-alone specialty called on to answer the mail in a crisis, but rather a key participant in the acquisition process. The RAM discipline should be involved early in the trade studies where performance, cost, and RAM should be part of any trade-space activity.
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