Wearout failure

Exponential Sometimes referred to as tlie negative exponential distribution. The distribution is characterized by a single parameter, X, the failure rate assumed constant over time Usually applied to data in tlie absence of other information tlius tlie most widely used in reliability work. Not appropriate for modeling bum-in or wearout failure rates... 

But aside from failure rate, which clearly applies to every component used in a power supply, there are also certain lifetime considerations that apply to specific components. The life of a component is stated to be the duration it can work for continuously, without degrading beyond certain specified limits. At the end of this useful life, it is considered to have become a wearout failure — or simply put — it is worn-out. Note that this need not imply the component has failed catastrophically — more often than not, it may be just out of spec. The latter phrase simply means the component no longer provides the... 

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... 

Formulas for MTTF are derived and often used for products during the useful life period. This method excludes wearout failures. This often results in a situation in which the MTTF is 300 years and useful life is only 40 years. Note that instruments should be replaced before the end of their useful life. When this is done, the mean time to random failures will be similar to the number predicted. 

In principle, it is possible to differentiate the damage behavior regarding the product life cycle into three superior damage phases Early failure phase, coincidence failure phase and wearout failure phase. The serial combination of these phases ( bathtube curve ) is shown in Fig. 1, focused on the failure rate 7.(t). Furthermore, Fig. 1 includes the range of the WeibuU form parameter b in reference to the failure phase. 

Bearing wearout based on large kilometrage (approx, wearout failure between 202 and 300 ... 

The hazard rate h(t) is another important reliability characteristic which can be used to describe the field failure behaviour when using a Weibull distribution. All three elements of the well known bath-tub-curve can be displayed by this theoretical distribution function. Thus it is possible to describe early failure, random failure and wearout failure. 

The analyzed system for this project is a telematics system which was delivered cross car to an Original Equipment Manufacturer (OEM). Approximately 10,000 unites were delivered in the manufacturing year which is analyzed in this project. In addition to several electronic parts (e.g. head unit or display) the system includes a cradle to charge and store the mobile phone of the user during his stay in the car and to provide undisturbed connection between head unit and mobile phone. This cradle is assumed to follow a wearout failure model in contrast to the other electronic devices. The as sumption is based on the fact that mobile phones are quite often inserted into and removed from the cradle during life time. Thus the telematics system is separated into electronic and mechanic devices which wiU be analyzed separately. Additionally the whole system is analyzed as one complex system to show the deviation between the two approaches. 

Mechanic failures occurred between 2,000 km and 87,000 km. Without regarding the correction by candidates the failure behaviour seems to follow random failures. Not until using the method of Pauli the random failure behaviour turns into wearout failure behaviour due to failures at greater mileage which will appear in the future. 

The IFR region is the wearout period, which is characterized by an increasing rate of failure as a result of equipment deterioration due to age or use. For example, mechanical components such as transmission bearings will eventually wear out and fail, regardless of how well they are made. Wearout failures can be postponed, and the useful life of equipment can be extended by good maintenance practices. The only way to prevent failure due to wearout is to replace or repair the deteriorating component before it fails. 

Figure 2.72 shows a load diagram. The expected load is the required design and the load capacity is the safety design. The gray area where the two load curves intersect is the area where stress is applied and wearout failure can be expected. 

The failure rate changes over the lifetime of a population of devices. An example of a failure-rate vs product-life curve is shown in Figure 9 where only three basic causes of failure are present. The quaUty-, stress-, and wearout-related failure rates sum to produce the overall failure rate over product life. The initial decreasing failure rate is termed infant mortaUty and is due to the early failure of substandard products. Latent material defects, poor assembly methods, and poor quaUty control can contribute to an initial high failure rate. A short period of in-plant product testing, termed bum-in, is used by manufacturers to eliminate these early failures from the consumer market. 

Example 3 illustrated the use of the normal distribution as a model for time-to-failure. The normal distribution has an increasing ha2ard function which means that the product is experiencing wearout. In applying the normal to a specific situation, the fact must be considered that this model allows values of the random variable that are less than 2ero whereas obviously a life less than 2ero is not possible. This problem does not arise from a practical standpoint as long a.s fija > 4.0. 

The ha2ard function is a constant which means that this model would be appHcable during the midlife of the product when the failure rate is relatively stable. It would not be appHcable during the wearout phase or during the infant mortaHty (early failure) period. 

From equation 8 it was shown that the chance of surviving the mean life was 36.8% for the exponential distribution. However, this fact must be used with some degree of rationaHty in appHcations. For example, in the above situation the longest surviving MPU that was observed survived for 291.9 hours. The failure rate beyond this time is not known. What was observed was only a failure rate of A = 1.732 x lO " failures per hour over approximately 292 hours of operation. In order to make predictions beyond this time, it must be assumed that the failure rate does not increase because of wearout and... 

Wearout period - Failure occurs when the product reaches the end of its effective life and begins to degenerate and wear out. In detail, these can be described as ... 

One of tlte principal applications of the normal distribution in reliability calculations and liazard and risk analysis is tlte distribution of lime to failure due to wearout. Suppose, for example, tliat a production lot of a certain electronic device is especially designed to withstand liigh temperatures and intense vibrations lias just come off the assembly line. A sample of 25 devices from tlie lot is tested under tlie specified heal and vibration conditions. Time to failure, in hours, is recorded for each of the 25 devices. Application of Eqs. (19.10.1) and... 

As described in Problem HZA.7, the failure rate of equipment frequently exhibits three stages a break-in stage with a declining failure rate, a useful life stage characterized by a fairly constant failure rate, and a wearout period characterized by an increasing failure rate. Many industrial parts and components follow this path. A failure rate curve exhibiting these three phases is called a bathtub curve. 

Wearout period increasing failure rate in the last stages of life due to growing defects and/or material fatigue... 

A common example of lifetime considerations in a commercial power supply design comes from its use of aluminum electrolytic capacitors. Despite their great affordability and respectable performance in many applications, such capacitors are a victim of wearout due to the steady evaporation of their enclosed electrolyte over time. Extensive calculations are needed to predict their internal temperature ( core temperature ) and thereby estimate the true rate of evaporation and thereby extend the capacitor s useful life. The rule recommended for doing this life calculation is — the useful life of an aluminum electrolytic capacitor halves every 10°C rise in temperature. We can see that this relatively hard-and-fast rule is uncannily similar to the rule-of-thumb of failure rate. But that again is just a coincidence, since life and failure rate are really two different issues altogether. 

Wearout period. Near the end of the curve, faflures occur at increasing rates as components begin to wearout or fatigue. In this final area of the curve, the mean time between failures calculated in the flat area of the curve no longer applies. Products with longer MTBFs can still wearout in just a few years. 

STAMP not only allows consideration of more accident causes than simple component failures, but it also allows more sophisticated analysis of failures and component failure accidents. Component failures may result from inadequate constraints on the manufacturing process inadequate engineering design such as missing or incorrectly implemented fault tolerance lack of correspondence between individual component capacity (including human capacity) and task requirements unhandled environmental disturbances (e.g., electromagnetic interference or EMI) inadequate maintenance physical degradation (wearout) and so on. 

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."... 

Field data may include failures occurring during the wearout portion of the bathtub curve... 

The hazard rate is suitable to describe the failure behaviour (early, random, wearout) of a product. The reliability characteristics mentioned above can be transformed directly into each other. If one of these is known all other characteristics can be determined easily. Due to using warranty data empirical reliability characteristics are applied in the following model. They are defined analogously to the theoretical reliability characteristics. 

Statistical values can also be used to determine expected periods of optimum performance in the life cycle of products, systems, hardware, or equipment. For example, if the life cycle of humans were plotted on a curve, the period of their lives that may be considered most useful, in terms of productivity and success, could be represented as shown in Figure 5.4. This plotted curve is often referred to as the bathtub curve because of its obvious shape. A similar curve can be used to determine the most productive period of a product s life cycle according to the five known phases of that life cycle, as discussed in Chapter 3. The resultant curve, known as a product s reliability curvey would resemble the curve that appears in Figure 5.5. During the breakin period, failures in the system may occur more frequently, but decreasingly less frequently as the curve begins to level toward the useful life period. Then, as the system reaches the end of its useful life and approaches wearout, more frequent failure experience is likely until disposal. 

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