1. Case Study of Expected Loss Failure Mode and Effect Analysis Model Based on Maintenance Data
- Author
-
Hyeonae Jang and Seungsik Min
- Subjects
Technology ,Computer science ,QH301-705.5 ,QC1-999 ,risk-evaluation ,EL-FMEA ,Field (computer science) ,expected loss ,maintenance record data ,General Materials Science ,Biology (General) ,Instrumentation ,QD1-999 ,Fluid Flow and Transfer Processes ,Downtime ,Product design ,Process Chemistry and Technology ,Physics ,General Engineering ,Absolute risk reduction ,Failure rate ,alternative coefficient ,Engineering (General). Civil engineering (General) ,Computer Science Applications ,Reliability engineering ,Chemistry ,TA1-2040 ,Risk assessment ,Failure mode and effects analysis ,Expected loss - Abstract
Failure mode and effect analysis (FMEA) is one of the most widely employed pre-evaluation techniques to avoid risks during the product design and manufacturing phases. Risk priority number (RPN), a risk assessment indicator used in FMEA, is widely used in the field due to its simple calculation process, but its limitations as an absolute risk assessment indicator have been pointed out. There has also been criticism of the unstructured nature and lack of systematicity in the FMEA procedures. This work proposes an expected loss-FMEA (EL-FMEA) model that organizes FMEA procedures and structures quantitative risk assessment metrics. In the EL-FMEA model, collectible maintenance record data is defined and based on this, the failure rate of components and systems and downtime and uptime of the system are calculated. Moreover, based on these calculated values, the expected economic loss is computed considering the failure detection time. It also provides an alternative coefficient to evaluate whether or not a detection system is installed to improve the expected loss of failure. Finally, a case study was conducted based on the maintenance record data, and the application procedure of the EL-FMEA model was presented in detail, and the practicality of this model was verified through the results.
- Published
- 2021