Failure-Data Analysis
Statistical analysis of historical failure and repair records to characterize how equipment actually behaves.
Definition
Failure-data analysis is the statistical analysis of historical failure and repair records — field data, warranty claims, inspection logs, maintenance history — to characterize how a population of equipment actually fails and is restored to service. It is the evidentiary foundation beneath reliability and RAM modeling: a model is only as credible as the failure data it is built on.
Working With Imperfect Data
Field failure data is rarely clean. Records are often censored — a component may still be in service with no failure recorded, or may have been replaced for a reason unrelated to the failure mode being studied. Weibull analysis and other life-data techniques exist specifically to extract a valid failure-rate characterization from this kind of incomplete, real-world data, rather than requiring a controlled test environment.
The output of this analysis — a failure distribution with defined shape and scale parameters — is what feeds directly into reliability and RAM models downstream.
How This Connects to Knar Global's Work
Failure-data analysis underpins Knar Global's reliability profile and sparing work, where historical failure and repair records are analyzed to justify spare-parts inventory levels and reliability assumptions used in RAMgen and other Industrial Digital Asset models — replacing rule-of-thumb sparing decisions with evidence drawn from actual equipment history.