Reliability Engineering
Reliatic's reliability module converts raw failure event data into quantitative metrics, Weibull models, and RCM maintenance strategies. Every calculation is traceable to source records and updates in real time as new failure events are logged.
Core Reliability Metrics
All metrics are calculated from structured failure event records. Inputs come from closed work orders, inspection findings, and operator-logged failure events—not from manual spreadsheet entries.
Primary metric for repairable systems. Reliatic computes MTBF per asset class, asset tag, and failure mode. Updated automatically on each failure event closure.
For non-repairable components. Used in spare parts analysis and replacement interval optimization. Sourced from manufacturer data or field failure history.
Repair efficiency metric. Reliatic captures repair start/end timestamps on work orders and calculates MTTR per asset type and maintenance team.
Operational availability derived from live MTBF and MTTR data. Displayed on the Reliability Dashboard and included in monthly KPI reports.
Weibull Analysis
Reliatic fits a two-parameter Weibull distribution to failure time data for each asset class with sufficient history (minimum 5 failure events). The shape parameter β characterizes the failure rate trend; the scale parameter η represents the characteristic life at which 63.2% of the population has failed.
asset_class: Centrifugal_Pump_Seal sample_size: 23 failure events shape_β: 2.4 // wear-out regime scale_η: 8,200 hrs // characteristic life B10_life: 3,640 hrs // 10% will fail by this time B50_life: 7,720 hrs // median failure time strategy_recommendation: TIME_DIRECTED suggested_interval: 6,500 hrs (0.79 × η)
Decreasing failure rate. Indicates design defects, poor installation, or substandard components.
Strategy: Review installation procedures, incoming QC, and burn-in testing.
Constant failure rate. Exponential distribution. Failures occur randomly, independent of age.
Strategy: PM strategy will not reduce failures. Focus on redundancy and fast detection.
Increasing failure rate with age. Classic wear, corrosion, or fatigue-driven degradation.
Strategy: Time-directed PM is effective. Optimize replacement interval from Weibull characteristic life η.
Reliability Centered Maintenance (RCM)
RCM analysis in Reliatic links each failure mode from the FMEA to an optimized maintenance task type. The task type is recommended based on the failure mode's β value, detectability, and consequence severity. All recommendations require engineer sign-off before being added to the PM schedule.
Performed at fixed intervals regardless of condition. Appropriate when β > 1 and replacement interval is known.
Triggered by measurement crossing a threshold (wall thickness, vibration, temperature). Appropriate when degradation is monitorable.
Verifies hidden functions (standby equipment, safety devices) are still operable. Required for protective devices under PSM.
Economically justified when failure consequence is low and restoration cost is less than prevention cost. Must be explicitly accepted in the FMEA.
Run-to-Failure discipline: RTF must be explicitly justified in the FMEA with a completed consequence evaluation. The platform will not allow RTF selection for assets where the failure consequence category is Safety or Environmental.
Failure Event Recording
All MTBF, MTTF, and Weibull calculations depend on structured failure event records. Each event must capture sufficient data for statistical analysis—free-text descriptions alone are insufficient.
{
asset_id: "V-2041",
failure_date: "2026-01-14T06:32:00Z",
failure_mode: "SEAL_LEAK", // from FMEA taxonomy
failure_cause: "CAVITATION", // root cause code
detection_method: "OPERATOR_OBSERVATION",
time_to_repair: 420, // minutes
parts_replaced: ["SEAL_KIT_P/N-44X"],
rca_required: true, // auto-set if MTBF threshold breached
rca_id: "RCA-2026-0041"
}Predictive Maintenance Triggers
Reliatic can generate governance events automatically when reliability data crosses configured thresholds. These triggers connect the reliability module directly to the Reliability-to-Action Loop.