Reliability and Survival Methods
Introduction to Reliability and Survival
Lifetime and Failure Analysis
Reliability and Survival Methods describes a number of methods and tools that are available in JMP to help you evaluate and improve reliability in a product or system and analyze survival data for people and products:
• The Life Distribution platform enables you to analyze the lifespan of a product, component, or system to improve quality and reliability. This analysis helps you determine the best material and manufacturing process for the product, thereby increasing the quality and reliability of the product. See Life Distribution.
• The Fit Life by X platform helps you analyze lifetime events when only one factor is present. You can choose to model the relationship between the event and the factor using various transformations, or create a custom transformation of your data. See Fit Life by X.
• The Cumulative Damage platform enables you to analyze an accelerated life test where the stress levels might have changed over time. See Cumulative Damage.
• The Fatigue Model platform enables you to analyze fatigue data, which is also known as S-N (strain or stress versus number of cycles) curve modeling. See Fatigue Model.
• The Recurrence Analysis platform analyzes event times where the events can recur several times for each unit. Typically, these events occur when a unit breaks down, is repaired, and then put back into service after the repair. See Recurrence Analysis.
• The Repeated Measures Degradation platform uses a hierarchical Bayesian modeling approach to analyze measurements of observational units that can be measured without being destroyed. You can analyze observations with or without an acceleration factor. See Repeated Measures Degradation.
• The Destructive Degradation platform models failure data for product characteristics whose measurement requires that the product be destroyed. This results in a single observation per product unit. You can also include an acceleration factor. A wide range of common degradation models is available. See Destructive Degradation.
• The Reliability Forecast platform helps you predict the number of future failures. The analysis estimates the parameters for a life distribution using production dates, failure dates, and production volume. See Reliability Forecast.
• The Reliability Growth platform models the change in reliability of a single repairable system over time as improvements are incorporated into its design. See Reliability Growth.
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The Reliability Block Diagram platform displays the reliability relationship between a system's components and, if reliability distributions are given to the components, analytically obtains the reliability behavior. See Reliability Block Diagram.
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The Repairable Systems Simulation platform enables you to interactively define the relationships between the components of a repairable system. It can also simulate the down time of the system. See Repairable Systems Simulation.
• The Survival platform computes survival estimates for one or more groups. It can be used as a complete analysis or is useful as an exploratory analysis to gain information for more complex model fitting. See Survival Analysis.
• The Fit Parametric Survival platform fits the time to event variable using linear regression models that can involve both location and scale effects. The fit is performed using several distributions. See Fit Parametric Survival.
• The Degradation platform analyzes degradation data to predict pseudo failure times. These pseudo failure times can then be analyzed by other reliability platforms to estimate failure distributions. You can include an explanatory factor. You can perform stability analysis to set product expiration dates. You can also fit custom destructive degradation models. See Degradation.
• The Fit Proportional Hazards platform fits the Cox proportional hazards model, which assumes a multiplying relationship between predictors and the hazard function. See Fit Proportional Hazards.