The Tools of RES
Weibull Analysis
In the 1930’s Swedish Engineer and Mathematician Waloddi Weibull invented the Weibull formula for use
in analyzing life failure data. The formula describes, with minor adjustments each section of the familiar
“Bathtub” curve. The importance of Weibull is that it allows the analyst to draw relatively good inferences
with a small number of data points.
The three Weibull parameters of particular interest are eta, beta, and gamma. Eta represents the
characteristic life of a component, or the point where 63.2% of the items in service will have failed. Gamma
represents the beginning of each phase of the bathtub curve. Beta is the shape factor and represents
whether the analyzed failures are displaying infant failure, random failure, or wear out failure
characteristics. The ability to interpret these three parameters allows us to set appropriate maintenance
strategies based on the failure mechanisms that are present.
Paul Barringer invented a special purpose plot based on Weibull that yields an overall view of process
capability, variation, and losses caused by variation and unreliability. The Barringer plot is an especially
useful management tool for determining the relative gains that can be made through removing variability
and improving reliability. Barringer plots help managers determine where best to spend their limited
resources.
Reliability Block Diagrams
Reliability Block Diagrams (RBD’s) are a block representation of the path of success through a facility. The
analyst uses the RBD to visualize how plant equipment is put together to enable the plant to produce its
desired output. When used with plant failure statistics and modern Monte Carlo Simulation programs the
RBD can be used to identify plant bottlenecks.
Reliability Centered Maintenance (RCM)
RCM is a systematic way of identifying failure modes within equipment and determining appropriate
maintenance tasks to combat the failures. The Failure Modes Effects and Criticality Analysis (FMECA) is
the heart of the RCM process. This systematic approach when coupled with plant information about plant
failures, costs, safety impacts, environmental impacts, and operational criticality allows maintenance
professionals to set appropriate tasks and maintenance intervals to generate a strategy that is optimized to
the needs of the business.
Root Cause Failure Analysis
Root Cause Failure Analysis is an ordered way of thinking that aids in finding the causes for problems and
determining appropriate actions to prevent recurrence. It comes in many forms from the 5 Why associated
with TQM/TPM to the regimented fault tree methods associated with PROACT and Apollo Root Cause.
Analyzing failures to determine cause, while a rigorous exercise, is usually very easy compared to
managing the various solutions arising from the analysis.
Most facilities do a good job of analyzing major failures due to Lovelace’s First Law of Assets. To
paraphrase Lovelace, An asset will draw whatever resources are required to put it back in service once it
goes out of service. In the case of RCFA, the asset will draw whatever resources are required to insure
that the black eye caused by the big failure never occurs again.
|
Return On Asset Reliability (ROAR™), is derived from the common asset investment metrics return on capital and return on assets. ROAR™ identifies investment potential from a company’s current capital expenditure budget and utilizes proprietary benchmark data to produce a realistic return on investment opportunity. |
|
