Service Life Ssd

[Marti Buday]

Aproduct's service life is its period of use in service. Several related terms describe more precisely a product's life, from the point of manufacture, storage, and distribution, and eventual use.Service life has been defined as "a product's total life in use from the point of sale to the point of discard" and distinguished from replacement life, "the period after which the initial purchaser returns to the shop for a replacement".[3] Determining a product's expected service life as part of business policy (product life cycle management) involves using tools and calculations from maintainability and reliability analysis. Service life represents a commitment made by the item's manufacturer and is usually specified as a median. It is the time that any manufactured item can be expected to be "serviceable" or supported by its manufacturer.[citation needed]

Service life is not to be confused with shelf life, which deals with storage time, or with technical life, which is the maximum period during which it can physically function.[3] Service life also differs from predicted life, in terms of mean time before failure (MTBF) or maintenance-free operating period (MFOP). Predicted life is useful such that a manufacturer may estimate, by hypothetical modeling and calculation, a general rule for which it will honor warranty claims, or planning for mission fulfillment. The difference between service life and predicted life is most clear when considering mission time and reliability in comparison to MTBF and service life. For example, a missile system can have a mission time of less than one minute, service life of 20 years, active MTBF of 20 minutes, dormant MTBF of 50 years, and reliability of 99.9999%.

Manufacturers will commit to very conservative service life, usually 2 to 5 years for most commercial and consumer products (for example computer peripherals and components). However, for large and expensive durable goods, the items are not consumable, and service lives and maintenance activity will factor large in the service life. Again, an airliner might have a mission time of 11 hours, a predicted active MTBF of 10,000 hours without maintenance (or 15,000 hours with maintenance), reliability of .99999, and a service life of 40 years.

The most common model for item lifetime is the bathtub curve, a plot of the varying failure rate as a function of time. During early life, the bathtub shows increased failures, usually witnessed during product development. The middle portion of the bathtub, or 'useful life', is a slightly inclined, nearly constant failure rate period where the consumer enjoys the benefit conferred by the product. As time increases further, the curve reaches a period of increasing failures, modeling the product's wear-out phase.

For an individual product, the component parts may each have independent service lives, resulting in several bathtub curves. For instance, a tire will have a service life partitioning related to the tread and the casing.

For maintainable items, those wear-out items that are determined by logistical analysis to be provisioned for sparing and replacement will assure a longer service life than manufactured items without such planning. A simple example is automotive tires - failure to plan for this wear out item would limit automotive service life to the extent of a single set of tires.

An individual tire's life follows the bathtub curve, to boot. After installation, there is a not-small probability of failure which may be related to material or workmanship or even to the process for mounting the tire which may introduce some small damage. After the initial period, the tire will perform, given no defect introducing events such as encountering a road hazard (a nail or a pothole), for a long duration relative to its expected service life which is a function of several variables (design, material, process). After a period, the failure probability will rise; for some tires, this will occur after the tread is worn out. Then, a secondary market for tires puts a retread on the tire thereby extending the service life. It is not uncommon for an 80,000-mile tire to perform well beyond that limit.[6]

It may be difficult to obtain reliable longevity data about many consumer products as, in general, efforts at actuarial analysis are not taken to the same extent as found with that needed to support insurance decisions. However, some attempts to provide this type of information have been made. An example is the collection of estimates for household components provided by the Old House Web[7] which gathers data from the Appliance Statistical Review and various institutes involved with the homebuilding trade.

Some Engine manufacturers, such as for example Navistar and Volvo, use a so-called B-life rating,[8]based on the durability data of the engine manufacturer,[9] B10 and B50 index for measuring the life expectancy of an engine.[10]

When exposed to high temperatures, the lithium-ion batteries in smartphones are easily damaged and can fail faster than expected, in addition to letting the device run out of battery too often. Debris and other contaminants that enter through small cracks in the phone can also infringe on smartphone life expectancy. One of the most common factors that cause smartphones and other electronic devices to die quickly is physical impact and breakage, which can severely damage the internal pieces.[11]

For certain products, such as those that cannot be serviced during their operational life for technical reasons, a manufacturer may calculate a product's expected performance at both the beginning of operational life (BOL) and end of operational life (EOL). Batteries and other components that degrade over time may affect the operation of a product. The performance of mission critical components is therefore calculated for EOL, with the components exceeding their specification at BOL. For example, with spaceflight hardware, which must survive in the harsh environment of space, the capacity to generate electricity from solar panels or radioisotope thermoelectric generator (RTG) is likely to reduce throughout a mission, but must still meet a specific requirement at EOL in order to complete the mission. A spacecraft may also have a BOL mass that is greater than its EOL mass as propellant is depleted during its operational life.

The surface community will continue to evaluate the service life of each surface ship based on combat relevance, reliability data, and material condition. Currently, the Navy has 73 Arleigh Burke-class destroyers in service and is continuing to modernize the class with the latest technologies and capabilities.

It is important to know how long your products will last, both on the shelf and in service. Therefore I have created a list of answers to the most frequently asked questions concerning shelf life and service life in rubber products. Feel free to visit our Rubber Storage Conditions page for more specific information on how to store your rubber products.

Rubber products should be stored in a dry cool place and should be protected from light, moisture, oxygen, heat, ozone, any chemicals and deformation. Storage temperature should be below 25C however, below 15C is preferable.

Shelf life does not guarantee the quality of a product; therefore, rubber products should remain in storage for as short time as possible. During storage, rubber products can undergo changes in physical properties and ultimately become unusable.

*The recommended shelf life pertains to vulcanized products and may vary based on polymer type and formulation.

Download The Polymer Shelf Life TableVisit Our Polymers Page For More Specific Information about Specific Polymers

The SLEP will extend the useful life of the MLB by 20 years; SLEP work will be performed on a minimum of 107 MLBs and a maximum of 117 MLBs. The main work will be on systems experiencing technical obsolescence: the main propulsion, electrical, steering, towing and navigation systems, as well as replacement of areas of the hull and structure that have demonstrated high failure rates. Additionally, efforts to enhance human system integration will be made where practical to do so. The original operational capabilities and characteristics of the 47-foot MLB will not change.

We understand the service life of an asset as the time during which an asset is operational for the company and works in perfect condition. In other words, during the useful life of an asset it is expected to operate at peak performance.

For this reason, it is possible that the service life of an asset has ended, but the asset continues to function. However, it will not be doing so at maximum efficiency, running the risk of unexpected breakdowns that can interrupt the production chain at any time.

During this stage, maintenance is a must. This will help to extend the service life of all the machinery involved in the production chain, reducing costs and preventing irreparable damage to the facilities.

In reality, there is no universal formula with which to calculate the service life of an asset, as there are a number of external factors that will influence its longevity. Frequency of use, environmental factors such as temperature, humidity or quality of maintenance are some of them.

Establishing a predictive maintenance plan will be the best guarantee for extending the service life of the machines and equipment of an industrial business to the maximum. Predictive maintenance will improve the availability of equipment, produce fewer losses and reduce the expense of replacing parts, stopping machinery to repair the asset, etc.

You will be able to connect all your assets and information systems, processing them through Big Data technology, obtaining a single operations framework for monitoring, management and analysis. In addition, with Nexus Integra you will be able to carry out an effective and automated maintenance strategy, which will allow you to considerably extend the life of your assets, increasing the rate of production without interruptions or reprocessing.

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