Failure rate is the frequency with which an engineered system or component fails, expressed for example in failures per hour. It is often denoted by the Greek letter λ (lambda) and is important in reliability engineering.
The failure rate of a system usually depends on time, with the rate varying over the life cycle of the system. For example, as an automobile grows older, the failure rate in its fifth year of service may be many times greater than its failure rate during its first year of service. One does not expect to replace an exhaust pipe, overhaul the brakes, or have major transmission problems in a new vehicle.
In practice, the Mean Time Between Failures (MTBF) is often used instead of the failure rate. The MTBF is an important system parameter in systems where failure rate needs to be managed, in particular for safety systems. The MTBF appears frequently in the engineering design requirements, and governs frequency of required system maintenance and inspections. In special processes called renewal processes, where the time to recover from failure can be neglected and the likelihood of failure remains constant with respect to time, the failure rate is simply the multiplicative inverse of the MTBF (1/λ).
A similar ratio used in the transport industries, especially in railways and trucking is 'Mean Distance Between Failure', a variation which attempts to correlate actual loaded distances to similar reliability needs and practices.
Failure rates and their projective manifestations are important factors in insurance, business, and regulation practices as well as fundamental to design of safe systems throughout a national or international economy.
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In words appearing in an experiment, the failure rate can be defined as
While failure rate λ(t) is often thought of as the probability that a failure occurs in a specified interval, given no failure before time t, it is not actually a probability because it can exceed 1. It can be defined with the aid of the reliability function or survival function R(t), the probability of no failure before time t, as:
where t_{1} (or t) and t_{2} are respectively the beginning and ending of a specified interval of time spanning Δt. Note that this is a conditional probability, hence the R(t) in the denominator.
By calculating the failure rate for smaller and smaller intervals of time , the interval becomes infinitely small. This results in the hazard function, which is the instantaneous failure rate at any point in time:
Continuous failure rate depends on a failure distribution, , which is a cumulative distribution function that describes the probability of failure prior to time t,
where T is the failure time. The failure distribution function is the integral of the failure density function, f(t),
The hazard function can be defined now as
Many probability distributions can be used to model the failure distribution (see List of important probability distributions). A common model is the exponential failure distribution,
which is based on the exponential density function.
For an exponential failure distribution the hazard rate is a constant with respect to time (that is, the distribution is "memoryless"). For other distributions, such as a Weibull distribution or a lognormal distribution, the hazard function may not be constant with respect to time. For some such as the deterministic distribution it is monotonic increasing (analogous to "wearing out"), for others such as the Pareto distribution it is monotonic decreasing (analogous to "burning in"), while for many it is not monotonic.
Failure rate data can be obtained in several ways. The most common means are:
Failure rates can be expressed using any measure of time, but hours is the most common unit in practice. Other units, such as miles, revolutions, etc., can also be used in place of "time" units.
Failure rates are often expressed in engineering notation as failures per million, or 10^{6}, especially for individual components, since their failure rates are often very low.
The Failures In Time (FIT) rate of a device is the number of failures that can be expected in one billion (10^{9}) devicehours of operation. (E.g. 1000 devices for 1 million hours, or 1 million devices for 1000 hours each, or some other combination.) This term is used particularly by the semiconductor industry.
Under certain engineering assumptions (e.g. besides the above assumptions for a constant failure rate, the assumption that the considered system has no relevant redundancies), the failure rate for a complex system is simply the sum of the individual failure rates of its components, as long as the units are consistent, e.g. failures per million hours. This permits testing of individual components or subsystems, whose failure rates are then added to obtain the total system failure rate.
Suppose it is desired to estimate the failure rate of a certain component. A test can be performed to estimate its failure rate. Ten identical components are each tested until they either fail or reach 1000 hours, at which time the test is terminated for that component. (The level of statistical confidence is not considered in this example.) The results are as follows:
Component  Hours  Failure 

Component 1  1000  No failure 
Component 2  1000  No failure 
Component 3  467  Failed 
Component 4  1000  No failure 
Component 5  630  Failed 
Component 6  590  Failed 
Component 7  1000  No failure 
Component 8  285  Failed 
Component 9  648  Failed 
Component 10  882  Failed 
Totals  7502  6 
Estimated failure rate is
or 799.8 failures for every million hours of operation.

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Failure rate is the frequency with which an engineered system or component fails. Usually it is expressed in a number of failzres per time period, like failures per hour. It is often written as the Greek letter λ (lambda) and is important in reliability theory. In practice, the closely related Mean Time Between Failures (MTBF) is more commonly expressed and used for high quality components or systems.
Failure rate is usually time dependent, and an intuitive corollary is that the rate changes over time versus the expected life cycle of a system. For example, as an automobile grows older, the failure rate in its fifth year of service may be many times greater than its failure rate during its first year of service—one simply does not expect to replace an exhaust pipe, overhaul the brakes, or have major transmission problems in a new vehicle. [Mean Time Between Failures (MTBF) is closely related to Failure rate. In the special case when the likelihood of failure remains constant with respect to time (for example, in some product like a brick or protected steel beam), and ignoring the time to recover from failure, failure rate is simply the inverse of the Mean Time Between Failures (MTBF). MTBF is an important specification parameter in all aspects of high importance engineering design— such as naval architecture, aerospace engineering, automotive design, etc. —in short, any task where failure in a key part or of the whole of a system needs be minimized and severely curtailed, particularly where lives might be lost if such factors are not taken into account. These factors account for many safety and maintenance practices in engineering and industry practices and government regulations, such as how often certain inspections and overhauls are required on an aircraft.
A similar ratio used in the transport industries, especially in railways and trucking is 'Mean Distance Between Failure', a variation which attempts to correlate actual loaded distances to similar reliability needs and practices.
Failure rates and their projective manifestations are important factors in insurance, business, and regulation practices as well as fundamental to design of safe systems throughout a national or international economy.
