Statistically verify whether wear-out is setting in or maintenance is improving reliability.
How does the Laplace Trend Test detect system aging or improvement?
Standardized under IEC 60605-6, the Laplace test statistic U evaluates whether failure event arrivals follow a stationary Poisson process (HPP): U = (∑ ti - n·T / 2) / (T · √(n / 12)). If U > +1.96 (p < 0.05), the asset is experiencing statistical wear-out (deterioration). If U < -1.96, reliability is improving. Between -1.96 and +1.96, constant MTBF (HPP) holds.
Operating hours or cycles from baseline
| Event # | Cumulative Time (h) | Inter-Arrival Time (h) |
|---|---|---|
| 1 | 45 | 45 |
| 2 | 120 | 75.0 |
| 3 | 240 | 120.0 |
| 4 | 390 | 150.0 |
| 5 | 580 | 190.0 |
| 6 | 810 | 230.0 |
| 7 | 1090 | 280.0 |
| 8 | 1420 | 330.0 |
Fitting classical Weibull distributions to repairable systems without testing for renewal trend is a critical error. Repairable systems undergo repeated interventions: if the failure rate is non-constant, an NHPP Power Law (Crow-AMSAA) or Renewal Process must be selected instead of single-life models.
Track MTBF growth and project cumulative test hours required to reach target MTBF.
Availability & PerformanceDerive verified reliability metrics directly from maintenance history CSV exports.
Maintenance Planning & StrategyFind the mathematically optimum PM interval that minimizes total maintenance cost per unit time.