Industry Failure Databases Challanges
Several industry failure databases compile failure records, estimate operational time, and calculate failure rates. This data is then published in books or stored in computer databases. The primary benefit of these databases is their reliance on real-world field failure information, providing valuable insights into equipment reliability. The data is much valuable and is applied to calculate the SIL and RRF in Layers of Protection Analysis.
Challenges in Failure Rate Estimation
1. Missing Data
There are several challenges in obtaining accurate failure rate data using this method. Key information about failures is often missing, including total operational time, failure confirmation, technology classification, failure cause, and stress conditions. As a result, the calculated failure rate is often significantly higher than what is required for probabilistic Safety Instrumented Function (SIF) verification. This discrepancy arises due to:
- Failure to differentiate between random and wear-out failures,
- Lack of distinction between systematic and random failures,
- Merging of different technology classes,
- Incomplete fault isolation, and
- Other contributing factors.
When total operational time is not recorded, it becomes impossible to distinguish between wear-out and random failures occurring during the useful life of a component. If these failures are grouped together, analysts typically assume all failures are random, leading to an inflated failure rate. Additionally, this omission eliminates the opportunity to determine the actual useful life period of the component.
2. Overestimated Failure Rates
- Random vs. Wear-Out Failures: If total operational time is missing, wear-out failures are treated as random, leading to higher failure rate estimates.
- Systematic vs. Random Failures: Maintenance errors, calibration issues, and other systematic faults are often misclassified, further inflating failure rates.
- Multiple Failure Reports: When failure confirmation is skipped, multiple components may be replaced unnecessarily, recording more failures than actually occurred.
- Technology Class Mixing: Older, less reliable technologies are often included in failure rate calculations alongside modern technology, skewing data.
3. Impact on Safety Instrumented Functions (SIF)
- Safety Integrity Verification: Probabilistic calculations for Safety Instrumented Functions (SIFs) aim to determine the Probability of Failure on Demand (PFDavg) due to random failures.
- Preventative Maintenance Consideration: IEC 61508 requires preventative maintenance to replace instruments before the end of their useful life, meaning failure rate calculations should exclude wear-out failures.
- Consequence of High Failure Rates: If failure rates are overestimated, the probability of failure increases, leading to more stringent safety designs. While this may result in excessive safety margins, it remains an acceptable trade-off.
While industry failure databases provide valuable insights, they should be used with caution. Analysts must ensure proper data collection, differentiate between failure types, and account for technology variations to derive realistic failure rates for reliability assessments and safety function verification.
Available Databases
One of the most popular failure rate databases is the OREDA database OREDA stands for “Offshore Reliability Data.” This book presents detailed statistical analysis on many types of process equipment. Many engineers use it as a source of failure rate data to perform safety verification calculations. It is an excellent reference for all who do data analysis. Other industry failure database sources include:
- FMD-97: Failure Mode / Mechanism Distributions. Reliability Analysis Center, 1997.
- Guidelines for Process Equipment Reliability Data, with Data Tables. Center for Chemical Process Safety of AIChE, 1989.
- NPRD-95. Nonelectronic Parts Reliability Data. Reliability Analysis Center, 1995.
- IEEE Std. 500. IEEE Guide To The Collection and Presentation Of Electrical, Electronic, Sensing Component, And Mechanical Equipment Reliability Data For Nuclear-Power Generating Stations. IEEE, 1984.
