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PM Interval Optimizer - Find the Cheapest Maintenance Frequency

Use Weibull Reliability Analysis and Cost-Risk Modeling to Set Optimal PM Intervals

Free preventive maintenance interval optimization calculator for reliability engineers, maintenance planners, and plant managers. Enter your equipment's failure history, PM cost, and corrective repair cost, and the calculator fits a Weibull distribution to find the optimal interval that minimizes total maintenance cost. Shows the cost curve, reliability at the current interval, and the savings from adjusting PM frequency. Works for any maintainable component: bearings, seals, belts, filters, motors, and more.

Pro Tip: The single biggest mistake in PM scheduling is using the OEM interval without questioning it. OEM intervals are conservative because the manufacturer has zero knowledge of your operating conditions and maximum liability exposure if their interval is too long. A pump bearing that the OEM says to replace every 12 months might have a Weibull shape parameter showing no wear-out until 24 months in your specific service. On the other hand, if you are running the pump at 110% speed in a dusty environment, the OEM interval might be too long. The data from your own failure history always trumps the OEM manual.

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Read the PM interval optimization guide

PM Interval Optimization Guide →
Start Using This Tool See How It Works

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PM Interval Optimizer
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How It Works

  1. Enter Failure Data

    Input time-to-failure records for the component in operating hours, calendar days, or cycles. You need at least 5-7 failure records for a reasonable Weibull fit. Include both failures that triggered corrective action and suspensions (components replaced preventively before failure).

  2. Set Cost Parameters

    Enter the cost of a planned PM replacement (parts + labor + scheduled downtime) and the cost of an unplanned corrective repair (parts + labor + unplanned downtime + consequential damage). The ratio of corrective to preventive cost drives the optimal interval.

  3. Review Weibull Analysis

    The calculator fits a two-parameter Weibull distribution (shape beta and scale eta) to your failure data. A beta greater than 1 indicates wear-out, which means PM is beneficial. A beta less than 1 indicates infant mortality, where PM is counterproductive. A beta near 1 indicates random failures where PM has no effect on failure rate.

  4. Find the Optimal Interval

    The calculator sweeps across possible PM intervals and plots total cost per unit time (PM cost + expected failure cost). The minimum of this curve is the cost-optimal interval. It also shows the reliability at that interval, which tells you what percentage of components will survive to the next PM.

  5. Compare Scenarios

    Adjust PM cost, failure cost, or Weibull parameters to see how the optimal interval changes. This sensitivity analysis helps you understand which cost assumptions drive the result and how much slack exists in the optimal interval.

Built For

  • Reliability engineers setting PM intervals for critical rotating equipment based on plant failure data
  • Maintenance planners justifying PM frequency changes to management with cost-based evidence
  • CMMS administrators populating PM task frequencies during initial system setup or periodic review
  • Plant managers challenging OEM maintenance intervals that seem too frequent or too infrequent
  • Condition monitoring teams validating that vibration-based PM triggers align with statistical failure patterns
  • Maintenance supervisors evaluating whether time-based PM or condition-based monitoring is more cost-effective
  • Engineering students learning Weibull reliability analysis and age replacement models

Features & Capabilities

Two-Parameter Weibull Fit

Fits a Weibull distribution to your failure data using maximum likelihood estimation. Shows shape parameter (beta), scale parameter (eta), mean time between failures (MTBF), and the B10 life (age at which 10% of the population has failed).

Cost-Optimal Interval

Uses the age replacement model to find the PM interval that minimizes total cost per unit time. Balances the cost of planned PM against the expected cost of unplanned failures, weighted by the probability of failure at each interval.

Cost Curve Visualization

Interactive chart showing total cost per unit time across the range of possible PM intervals. The minimum of the curve is clearly marked. Shows the cost penalty of both under-maintaining (too-long interval) and over-maintaining (too-short interval).

Reliability at Interval

Shows the probability of surviving to the PM interval without failure. An 85% reliability at the optimal interval means 15% of components will fail before the next PM. You can tighten the interval to increase reliability at the cost of more frequent PMs.

Suspension Data Support

Handles right-censored data (suspensions) where components were replaced preventively before failure. This is critical for accurate Weibull fitting because ignoring suspensions biases the analysis toward shorter life estimates.

Sensitivity Analysis

Vary PM cost, failure cost, or Weibull parameters with sliders to see real-time changes in the optimal interval. Useful for understanding how uncertain cost estimates affect the recommendation.

Frequently Asked Questions

The Weibull shape parameter beta describes the failure pattern of your component. A beta less than 1 means the failure rate is decreasing over time (infant mortality), which means replacing the component with a new one is more likely to cause a failure than leaving the old one in place. A beta equal to 1 means failures are random and time-independent, so PM based on age is ineffective. A beta greater than 1 means the failure rate is increasing (wear-out), which is the only situation where age-based PM reduces failures. Most mechanical components like bearings, seals, and belts have beta values between 1.5 and 4.0, making them good candidates for PM.
A minimum of 5-7 failure records produces a usable estimate. With 10-15 records, the confidence interval tightens considerably. Above 20 records, additional data points provide diminishing improvement. For critical equipment where the stakes are high, collecting more data before making interval decisions is worthwhile. If you have fewer than 5 failures, the Weibull fit will be unreliable and you should supplement with engineering judgment or manufacturer data.
If beta is genuinely less than 1, it means the failure rate decreases with age, which is the infant mortality pattern. In this case, replacing the component with a new one resets the failure rate to its highest point. PM is counterproductive. However, before accepting a beta less than 1, verify your data. Common data quality issues that produce false infant mortality patterns include mixing failure modes (different root causes), including installation-related failures, or using data from different operating conditions. Separate your data by failure mode and re-analyze.
The higher the ratio of corrective to preventive cost, the shorter the optimal PM interval. If a corrective repair costs 10 times more than a planned replacement, it pays to replace more frequently to avoid the expensive surprise. If corrective and preventive costs are similar, the optimal interval extends toward running to failure. A cost ratio below about 2:1 often means run-to-failure is the economically rational strategy, assuming the failure does not create safety or environmental consequences.
This calculator is designed for time-based (age replacement) PM optimization. Condition-based maintenance (CBM) using vibration analysis, oil analysis, or thermography is a complementary strategy. Many plants use this calculator to set a maximum time-based interval and then layer CBM on top to catch early failures before the scheduled PM. If your CBM program is mature and catches most failures in advance, the economic value of time-based PM decreases. The Weibull analysis can help quantify that value.
Disclaimer: This calculator provides maintenance interval estimates based on statistical analysis of failure data. Results depend on data quality, operating conditions, and cost assumptions. Maintenance decisions for safety-critical equipment should involve qualified reliability engineers and must comply with regulatory requirements (OSHA PSM, EPA RMP, etc.). ToolGrit is not responsible for maintenance planning decisions or outcomes.

Learn More

Shops & Outbuildings

Optimizing PM Intervals: Weibull Analysis and the Age Replacement Model

How to use Weibull reliability analysis and cost modeling to find the PM interval that minimizes total maintenance cost. Covers data collection, parameter interpretation, and when PM is counterproductive.

Read Guide →
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MRO Spare Parts Inventory: Reorder Points, Safety Stock, and Storeroom Strategy

How to calculate reorder points and safety stock for maintenance spare parts, handle slow-moving items with Poisson distribution, and build a storeroom strategy that balances cost against stockout risk.

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