Infant mortality failures

Fig. 9. Failure rate curve for r eal components. A, infant mortality B, period of approximately constant p. and C, old age.

Infant mortality period - Quality failures dominate and oeeur early in the life of the produet. In detail, these ean be deseribed as ... 

Equations 11-1 through 11-5 are valid only for a constant failure rate fi. Many components exhibit a typical bathtub failure rate, shown in Figure 11-2. The failure rate is highest when the component is new (infant mortality) and when it is old (old age). Between these two periods (denoted by the lines in Figure 11-2), the failure rate is reasonably constant and Equations 11-1 through 11-5 are valid. 

A considerable assumption in the exponential distribution is the assumption of a constant failure rate. Real devices demonstrate a failure rate curve more like that shown in Figure 9. For a new device, the failure rate is initially high owing to manufacturing defects, material defects, etc. This period is called infant mortality. Following this is a period of relatively constant failure rate. This is the period during which the exponential distribution is most applicable. Finally, as the device ages, the failure rate eventually increases. 

Early failures or infant mortality manufacturing defects, foreign particles, material defects... 

The total item population or a system generally exhibits a relatively high failure rate in the beginning, which decreases rapidly and stabilizes at some approximate time t. This initial period is generally called the bum-in, infant mortality, or debugging period. The item population has weak items, and these fail in the beginning. To understand the nature of these early failures, some of their causes are listed ... 

The lifetime of a population of units at the component, board, box, or system level can be divided into three distinct periods. This is most often defined by the so-called reliability bathtub curve (Fig. 6.16). The bathtub curve describes the cradle-to-grave failure rates or frequency of failures as a function of time. The curve is divided into three distinct areas early failure rate (also known as infant mortality), the useful life period, and the wearout failure period. The infant-mortality portion of the curve, also known as the early life period, is the initial steep slope from the start to... 

Infant-mortality period. In the early period of failures, accelerated stress tests such as burn-in, power cycling, temperature cycling, and vibration as well as highly accelerated stress and life testing are used. These methods are used to screen out parts that are inherently not reliable and prevent the delivery of dead on arrival parts to a customer. While this consumes some early life of an assembly by stress screening, the remaining population begins the useful life period or the flat area of the bathtub. 

Useful life period. After the weaker units die off in the infant-mortality period, the failure rate becomes nearly constant and the assemblies have entered the normal or useful life period. This period is characterized as a relatively constant failure rate and is also referred to as the system life of a product. Mean time between failures is calculated in this section of the curve. Mean time between failures (MTBF) is the predicted elapsed time between inherent failures of a system during operation. MTBF can be calculated as the arithmetic mean (average) time between failures of a system. Failure rates calculated from MIL-HDBK-217 and Telcordia-332 apply only to this period. 

Some components fail soon after they are placed in service. This phenomenon—sometimes referred to as infant mortality —usually results from problems in the manufacture or commissioning of the item. Not aU equipment types exhibit wear-in behavior. For example, pressure vessels that have been fabricated and inspected to the appropriate standards are not likely to suffer from early failures. 

The shape of the plot in Figure 3-2 is characteristic of many components and well known to reliability engineers. The shape is called the "bathtub curve." Three regions are distinct. In the early portion of the plot, failure rates are higher. This area is called "infant mortality." The middle portion of the curve is known as "useful Ufe." The final portion of the curve is called "end of tife" or "wearout region."... 

The transistor that was aged in accelerated tests had performed the burn-in test in order to eliminate the devices presenting infant mortality failures (an aging of 168h St Tj = 120°C, under the some operation conditions. For this reason, the performance of measurements based on very short times of exposure was abandoned. 

The observations made on Figs 5a and 5b can be found on Fig. 6. In addition, notice in the upper-left quadrant of Fig. 6 that the solar array deployment and TTC account respectively for 17% and 22% of the failures of the first 30 days on-orbit. Thus satellite infant mortality, as discussed in Castet Saleh (2009), is driven to a large extent by these two subsystems. 

The probability of failures due to infant mortalities can be considered negligible... 

If it can be demonstrated that an SIF device (e.g., a block valve) has dominant time-based failure mechanisms (i.e., they wear out), the random failure rate model can lead to erroneous conclusions and practices. For example, in calculating test intervals, a random model may lead to testing more frequently than actually required during the early life of the device and testing too infrequently during the later wear-out phase. Owners/operators should be aware that reliability models (e.g., Weibull) are available that divide failures into infant mortality, random, and wear-out modes. This guideline assumes failures are random. 

Burn-in Component testing where infant mortality failures (defective or weak parts) are screened out by testing at elevated voltages and temperatures for a specified length of time. 

A stress screen need not necessarily simulate the field environment, or even utilize the same failure mechanism as the one likely to be triggered by this defect in field conditions. Instead, a screen should exploit the most convenient and effective failure mechanism to stimulate the defects that would show up in the field as infant mortality. Obviously, this requires an awareness of the possible defects that may occur in the hardware and extensive familiarity with the associated failure mechanisms. 

Hardware does not always fimction perfectly. Hardware wears over time. Hardware may show failures directly after production (called infant mortality). Hardware can also show slightly different performance now and then, or behave differently when the environment (e.g. temperature) changes. The hardware needs to be verified to withstand these kind of conditions. This is out of the scope of this paper. 

The lifetime of an entire population of products often is graphically represented by a set of curves collectively called the bathtub curve. Bathtub curve has been depicted in Fig. VII/1.2.2-1. The bathtub curve consists of three periods. First is an infant mortality or burnt-in period with a decreasing failure rate showing early-life failure. These... 

D.J. Wilkins, The Bathtub Curve and Product Failure Behavior Part One - the Bathtub Curve, Infant Mortality and Burn-in, ReliaSoff Reliability Field Consultant, November 2002. Reliability hotwire e magazine issue 21. 

A plot of the failure rate of a product as a function of time typically takes the shape of a bathtub curve (see Fig. 57.2). This curve illustrates the three phases that occur during the lifespan of a product from a reliability perspective. In the first, infant mortahty phase, there is an initially high but rapidly declining failure rate caused by infant mortahty. Infant mortality is typically caused by manufacturing defects that went undetected during inspection and testing and lead to rapid failure in service. Burn-in can be used to remove these units before shipment. The second phase, the normal operating life of the product, is characterized by a period of stable, relatively low failure rates. 

Section I of a decreasing failure rate ( >< 1) represents early failures due to material or manufacturing defects. Quality control and initial component testing usually help to avoid this kind of failures. Actuarial statisticians call this section of the curve infant mortality . 

Burn-in is the process of operating components or devices for a predetermined length of time in order to identify and eliminate components susceptible to infant mortality. Early failures can be eliminated by bum-in whereby the equipment is operated at stress levels equal to the intended actual operating conditions. The equipment is then released for actual use only when it has successfully passed through the bum-in period. Not to be confused with debugging. 

Together these produce the line on Fig. 10.2 that looks like a longitudinal section through a bathtub. Infant mortality failures are usually attributable to inadequacies in manufacture, maintenance, or design. The inadequacies result in a reduction in the ability of a component, equipment, or system to survive the environment to which it is subjected. Infant mortality failures exhibit relatively high failure rates during early... 

The actual (practical) useful life ends when the increased failure rate during wear-out becomes unacceptable in terms of economic or functional considerations. By eliminating the infant mortality failures and replacing them before or soon after the wear-out failures start occurring, the useful life is subject only to the constant failure rate. This is often referred to as preventative maintenance and may fall under the category of Certification Maintenance Requirements (see definitions on page 325). 

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