Monthly Archives: March 2013

Physical acceleration means that operating a unit at some higher stress level(s) (i.e, higher temperature, voltage, humidity or duty cycle, etc…) should produce similar failures as would occur at typical-use stresses, except that they are expected to occur much sooner.

Failures may be due to mechanical fatigue, corrosion, chemical reaction, diffusion, migration, etc. These are the same cause of failures under normal stress condition; the only difference is the time scale (the time to failure).

When there is true acceleration, changing stress is equivalent to transforming the time scale used to record when failures occur. The transformations commonly used are linear, which means that time-to-fail at high stress just has to be multiplied by a constant (the Acceleration Factor or AF) to obtain the equivalent time-to-fail at use stress.

For many engineers, this is where they encounter the biggest challenges: what is the preferred model? How do I fit the model to the condition, materiel and physical attributes of the UUT?

Too many knowledgeable users employ the Arrhenuis equation, but this is not always the preferred method, and care must be taken to apply the proper model.

What are the experiences with other models?

What are the success stories?

Reliability Curves

Plot of Observed Failure Rate with Infant Mortality, Constant, and Wear Out Failure Rates

Mechanical failure can occur at any point in a product life cycle. These can be divided into infant mortality, constant failure rate, and wear out. As shown in the diagram, which is a plot of failure rate as a function of time, the individual curves of these three different classes of failure mechanisms sum together to form the classic bathtub curve of observed failure rate.


Infant Mortality

Infant mortality occurs early during product use. The failure rate declines as a function of time, so reliability actually increases until a point is reached where the constant failure rate becomes dominant and the infant rate becomes negligible. The Weibull Distribution is used to model the infant mortality period. A wear-in or burn-in period may be used to screen out defective units. Infant mortality is typically caused by defects in manufacturing, handling, and storage. Examples of these causes are:

  • workmanship and assembly errors
    • misalignment of belts, pulleys, gears, shafts and bearings
    • over-tightening stresses parts, causes excessive friction
    • under-tightening leaves parts loose to fall off, vibrating shafts
  • parts are out of tolerance from design specifications
    • rubbing contact of moving parts
    • loose fits lead to vibration and galling
    • excess friction between mating, moving parts
  • excess flash from molding
    • similar problems to tolerance issues above
    • flash breaks off, contaminating system with debris
  • damaged parts from improper processing and handling
    • cracks from damage propagate under stress leading to failure
    • over-heating changes material properties
    • solvents and residues lead to stress corrosion cracking
    • environmental conditions cause swelling and warping

Constant Failure Rate

Most of the product life is spent in the random failure state, which has a constant failure rate. The reliability with a constant failure rate is predicted using the exponential function. Failures are caused by mechanisms inherent in the design.

It should be noted that a preventative maintenance program during the constant failure rate phase can actually reduce reliability by reintroducing infant mortality into the system. The scheduled replacement of parts presents an opportunity for errors in workmanship as well as adding the possibility of failure of the parts themselves.

This has led to the current practice of Reliability Centered Maintenance. RCM determines the maintenance requirements of individual components to replace only those components which actually need replacing while monitoring the condition of all components which are prone to wear and eventual failure. Not all components in a system follow the bathtub curve. Reliability centered maintenance identifies the reliability curve for a component and provides an applicable maintenance strategy to match.

Wear Out

The wear-out phase precedes the end of the component or product life. At this point, the probability of failure increases with time. While the parts have no memory of previous use during the random failure phase, yielding a constant failure rate, when they enter wear out, the cumulative effects of previous use are expressed as a continually increasing failure rate. The normal distribution is often used to model wear out. Weibull may also be used to approximate this and every other period. Scheduled preventative maintenance of replacing parts entering the wear-out phase can improve reliability of the overall system.

Examples of failure mechanisms in wear out are:

  • Fatigue – constant cycle of stress wears out material
  • Corrosion – steady loss of material over time leads to failure
  • Wear – material loss and deformation, especially loss of protective coatings
  • Thermal cycling – not only fatigue, but change in chemical properties, alloyed metals can migrate to grain boundaries, changing properties
  • Radiation – Ultraviolet, X-ray, nuclear bombardment in environment changes molecular structure of materials