System Reliability in .NET

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EXAMPLE 50.1
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A car should have four tires and a spare in a quali ed condition prior to its driver leaving on a trip. During travel if a at occurs, the mission can continue, though with some level of elevated risk due to having to use the spare until the tire is repaired. Tires on a car irrefutably are mission critical items. The signi cance of a tire failure event depends on criteria such as the condition of the tires prior to the trip, trip distance of 1 mile or 1000 miles, and the circumstances required for emergency applications.
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50.3 System Reliability
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Reliability Philosophy Precepts
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Reliability is founded on a multi-faceted philosophy that includes the following precepts: 1. Every EQUIPMENT element has a probability of success that is determined by its premission operational health status, mission duration, and prescribed set of OPERATING ENVIRONMENT conditions, uses cases, and scenarios. 2. Systems have an inherent failure rate at delivery due to latent defects from design errors and aws, component/material properties, and workmanship processes and methods. 3. The characterization and modeling of component RAM can be approximated with various mathematical distributions. 4. With proper attention, latent defects within a speci c system are eliminated via corrective maintenance actions over time when identi ed. 5. By eliminating potential fail points, the system failure rate decreases, thereby improving the reliability, assuming the system is deployed, operated, and supported according to the System Developer s instructions. 6. At some point during a mission or useful service life of a SYSTEM, failure rates begin to increase due to the combinational and cumulative effects of component failure rates resulting from physical interactions and mass property degradation due to operating and environmental stresses. 7. These effects are minimized and the component s useful service life extended via a proactive program of system training, proper use, handling, and timely preventive and corrective maintenance actions at speci ed operating intervals. To better understand these precepts, let s explore the service life pro le of a elded system.
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System Service Life Pro les
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Humans and human-made systems exhibit service life pro les characterized by a level of infant mortality early followed by a growth and stability and nally aging. Textbook discussions on reliability often introduce the concept of service life pro les via a model referred to as the Bathtub Curve. Conceptually, the Bath Tub Curve represents a plot of failure rates over the active service life of the equipment. The name is derived from the characteristic Bathtub Curve hazard rate pro le as illustrated in Figure 50.2. We will delineate hazard rate versus failure rate later. The Bathtub Curve consists of three distinct service life pro le regions: 1) a period of decreasing hazard rates, 2) a period of stabilized hazard rates characterized by random failures, and 3) a period of increasing hazard rates. Each of these periods is characterized by different types of exponential distributions. The three service life pro le regions are often referred to by a variety of names. The period of decreasing failures is also referred to as the Infant Mortality, Burn-In, and Early Failure Period. The period of stabilized failures is often referred to as the Useful Service Life period. The period of increasing failures is sometimes referred to as the Wearout Period. The Bathtub Curve is actually a paradigm, a model for thinking. Unfortunately, like most paradigms, the Bathtub has become ingrained as a one size mindset that it applies universally to all systems and components. This is a fallacy! Most experts agree that eld data, though limited, suggest that the failure rate pro les vary signi cantly by component type. This is illustrated in Figure 50.2 by the dashed lines in the Period of Decreasing Failures and the Period of Increasing Failures. Smith [1992] notes the origin of the Bathtub Curve dates back to the 1940 s and 1950 s with the embryonic stages of reliability engineering. Early attempts to characterize failure rates of elec-
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