Quantitative risk assessment methodology combining Probability of Failure and Consequence of Failure into actionable risk rankings with optimized inspection intervals.
Reliatic's risk assessment methodology is grounded in the principles of API 580 (Risk-Based Inspection) and ISO 31000 (Risk Management). At its core, risk is the product of two independent dimensions: the probability that an asset will fail and the consequence of that failure. By quantifying both dimensions separately and combining them in a structured matrix, reliability engineers can make objective, defensible decisions about where to focus inspection and maintenance resources. The platform supports both semi-quantitative assessments (where PoF and CoF are rated on ordinal scales) and fully quantitative assessments (where PoF is expressed as annual failure frequency and CoF as affected area or financial impact). The semi-quantitative approach is the default for organizations beginning their RBI journey, while the quantitative approach aligns with API 581 Annex 2A for mature programs.
The Probability of Failure (PoF) rating in Reliatic is determined by evaluating four primary factor groups. Degradation Rate: The measured or estimated rate at which the asset is losing material or structural integrity. For pressure equipment, this is typically the corrosion rate in mm/year derived from thickness measurements. Higher degradation rates increase the PoF rating. Mechanism Severity: Not all damage mechanisms progress at the same rate or with the same predictability. Localized mechanisms like pitting and stress corrosion cracking are inherently less predictable than general thinning, resulting in higher PoF ratings even at similar degradation rates. The platform maintains a damage mechanism library (aligned with API 571) where each mechanism has a default severity weighting. Inspection Effectiveness: The quality and coverage of prior inspections directly influences confidence in the current condition assessment. An asset that has received multiple A-level inspections (high probability of detection, full coverage) will have a lower PoF than an identical asset that has only received E-level inspections (visual-only, limited coverage). Reliatic tracks inspection history and automatically factors effectiveness into the PoF calculation. Data Quality: Sparse or outdated data increases uncertainty, which must be reflected as increased PoF. If an asset's last thickness reading is more than 5 years old, or if fewer than 3 data points exist for trend analysis, the platform applies a data quality penalty that conservatively increases the PoF rating.
The Consequence of Failure (CoF) rating evaluates the impact across five dimensions. Personnel Safety: The potential for injury or fatality based on the fluid hazard (flammable, toxic, high-pressure), the proximity of personnel to the equipment, and the available escape routes. Equipment in occupied process areas with toxic service receives the highest safety consequence rating. Environmental Impact: The potential for soil, water, or air contamination based on fluid toxicity, release volume, and proximity to environmental receptors (waterways, protected areas, populated zones). Regulatory fines and remediation costs are considered. Production Loss: The financial impact of unplanned downtime, considering the equipment's criticality in the production chain, the availability of redundancy or bypass, and the estimated repair duration. A single-train compressor driving an entire production unit carries higher production consequence than a redundant pump. Detectability: Some failures manifest gradually (e.g., a small leak that can be detected and isolated) while others are sudden and catastrophic (e.g., brittle fracture). Failure modes with lower detectability receive higher consequence ratings because there is less opportunity for intervention. Isolation Capability: The ability to safely isolate the equipment in an emergency. Equipment with remote-operated emergency isolation valves (EIVs) and well-documented isolation procedures receives a lower consequence rating than equipment requiring manual valve operation in hazardous locations.
The risk score is computed as Risk = PoF x CoF, where both factors are rated on a 1-5 scale. This yields a risk score between 1 and 25, mapped onto a 5x5 matrix. Reliatic assigns five risk rankings based on the matrix position. Ranking A (Very High Risk, score 20-25): Unacceptable risk requiring immediate action. Inspection must occur within 6 months or the asset must be derated or removed from service. Ranking B (High Risk, score 12-19): Elevated risk requiring near-term inspection. Typical response is to schedule inspection within 12 months and implement interim monitoring. Ranking C (Medium Risk, score 8-11): Moderate risk managed through standard inspection programs. Inspection intervals of 2-4 years are typical. Ranking D (Low Risk, score 4-7): Acceptable risk with routine oversight. Extended inspection intervals of 5-8 years may be appropriate, supported by condition monitoring. Ranking E (Very Low Risk, score 1-3): Negligible risk. Maximum code-allowable inspection intervals apply, with periodic review to confirm conditions have not changed.
The risk ranking directly drives inspection scheduling through interval optimization. The principle is simple: higher-risk equipment is inspected more frequently, and the inspection method must be effective against the identified damage mechanism. Reliatic implements interval optimization by calculating the date at which the asset's risk is projected to reach an unacceptable level (typically Ranking A or B) based on the degradation rate trend. The recommended inspection date is set to ensure the risk is reduced back to the target level before the projected threshold crossing. After each inspection, the PoF is recalculated using the updated condition data, and the next inspection interval is adjusted accordingly. This creates a dynamic, risk-informed inspection program where resources flow to the equipment that needs them most, rather than following rigid calendar-based schedules. Organizations implementing this approach typically achieve 20-40% reduction in total inspection cost while simultaneously reducing the number of assets operating at high risk.