Saltar al contenido
PTI LATAMExplorar Portal
Mantenimiento

Failure Rate

Number of failures per unit of operating time (λ = failures/hour). In the useful life period with exponential distribution it is constant; in infant mortality it is decreasing (Weibull β<1); in wear-out it is increasing (Weibull β>1). Relationship with MTBF: λ = 1/MTBF (for exponential distribution). Estimated from CMMS failure history or reliability databases such as OREDA (oil/gas) and IEEE Std 493. Enables calculation of failure probability during a period and sizing of preventive maintenance strategies.

What you need to know

  • Number of failures per unit of operating time (λ = failures/hour).
  • In the useful life period with exponential distribution it is constant; in infant mortality it is decreasing (Weibull β<1); in wear-out it is increasing (Weibull β>1).
  • Relationship with MTBF: λ = 1/MTBF (for exponential distribution).
  • Estimated from CMMS failure history or reliability databases such as OREDA (oil/gas) and IEEE Std 493.
  • Enables calculation of failure probability during a period and sizing of preventive maintenance strategies.

Full definition

Failure Rate (λ) is a critical metric in industrial reliability engineering, quantifying the number of failures that occur per unit of operating time, typically expressed in failures per hour. This metric is essential for understanding the reliability of machinery and systems, allowing for effective maintenance planning and resource allocation. Within the context of exponential distribution, which is commonly used for systems with a constant failure rate, the relationship between failure rate and Mean Time Between Failures (MTBF) is given by λ = 1/MTBF. This indicates that as the MTBF increases, the failure rate decreases, reflecting improved reliability. Conversely, in scenarios characterized by infant mortality (Weibull β < 1), the failure rate decreases over time as initial defects are identified and rectified, while in wear-out scenarios (Weibull β > 1), the failure rate increases as components reach the end of their useful life.

The estimation of failure rate can be derived from historical failure data stored in Computerized Maintenance Management Systems (CMMS) or specialized reliability databases such as OREDA for the oil and gas sector, as well as IEEE Std 493, which provides guidelines for the reliability of electrical power systems. By analyzing this data, organizations can predict failure probabilities over specific periods, which is invaluable for optimizing preventive maintenance strategies and reducing unplanned downtime. For instance, if a manufacturing plant operates machinery with a failure rate of 0.01 failures per hour, the expected MTBF would be 100 hours, allowing maintenance teams to plan interventions accordingly to ensure operational continuity.

Furthermore, understanding the failure rate enables companies to implement condition-based maintenance, where interventions are based on the actual condition of the equipment rather than a fixed schedule. This approach not only enhances efficiency but also extends the lifespan of critical components, ultimately leading to cost savings and increased productivity.

What you need to know

  • What you need to know: The failure rate (λ) is expressed in failures per hour, helping to monitor equipment reliability.
  • A constant failure rate indicates an exponential distribution, where λ = 1/MTBF.
  • Infant mortality and wear-out phenomena can affect the failure rate, represented by the Weibull distribution parameters.
  • Utilizing CMMS data, organizations can effectively estimate failure rates and optimize maintenance strategies.

Formula

λ = 1/MTBF

Industrial applications

  • 1Predictive maintenance scheduling in manufacturing to prevent unplanned downtime.
  • 2Reliability analysis of critical infrastructure in the oil and gas sector.
  • 3Condition-based maintenance in power generation facilities to enhance equipment lifespan.
  • 4Failure rate monitoring for aerospace components to ensure safety and reliability.

Common mistakes

  • Relying solely on historical data without considering changing operating conditions.
  • Failing to update failure rate estimates as new data becomes available.
  • Confusing average failure rates with specific component reliability metrics.
💡

Pro tip

Regularly review and update your failure rate calculations based on the latest CMMS data to improve maintenance accuracy.

Technical standards

  • IEEE Std 493 - This standard offers reliability guidelines for electrical power systems.
  • OREDA - A database providing reliability data for the oil and gas industry.

Suppliers of industrial maintenance in Mexico