When a safety system fails, the clock starts ticking toward potential disaster. Every passing minute without detection increases the risk of injury, environmental damage, or worse. Among the many mean time to failure metrics, it’s at this point (failing safety systems) that mean time to dangerous failure becomes your most critical metric.
Unlike standard reliability measurements that track all equipment breakdowns, mean time to dangerous failure focuses specifically on failures that create hazardous conditions. A conveyor motor that stops running causes production delays, but a safety interlock that fails to engage when someone enters a restricted area becomes a life-threatening problem. This distinction is the difference between a manageable maintenance issue and a catastrophic safety incident.
In this guide, we'll walk through everything you need to know about calculating, tracking, and improving MTTFd in your facility, including the data sources needed, common obstacles you'll face, and practical strategies that actually work on the shop floor.
What Is MTTFd
Mean Time to Dangerous Failure (MTTFd) is a reliability metric that calculates the average expected time until a component or system experiences a failure that could lead to hazardous conditions. Unlike standard failure metrics that track all breakdowns, MTTFd specifically focuses on failures that pose safety risks to personnel, equipment, or the environment.
The difference between these is important because not every equipment failure creates danger. A conveyor belt motor that stops running is a production issue, but a safety interlock that fails to engage when someone enters a hazardous area becomes a life-threatening problem. MTTFd helps quantify these critical risks by focusing on safety failures rather than general reliability.
When safety-critical systems fail, the consequences extend far beyond the costs of downtime. They can result in injuries, fatalities, environmental damage, and regulatory violations. This is precisely why MTTFd has become essential for industrial maintenance teams managing high-risk operations.
MTTFd serves three primary functions in industrial maintenance:
- Safety Assessment: Provides a quantitative foundation for evaluating risk in industrial settings, helping teams understand how long they can reasonably expect safety systems to function before dangerous failures occur
- Regulatory Compliance: Standards like ISO 13849 and IEC 62061 require MTTFd calculations to demonstrate that safety systems meet required performance levels.
- Maintenance Planning: Guides preventive maintenance schedules by identifying when safety-critical components need inspection or replacement before they reach dangerous failure thresholds
Understanding what MTTFd is becomes essential when your operation depends on safety systems that must function reliably under all conditions. This leads us to examine how this metric compares to other reliability measurements.
MTTFd Vs Other Reliability Metrics
Different reliability metrics serve different purposes. Understanding when to use each one prevents confusion and ensures you're measuring what actually matters for your specific application.
The general MTTF metric encompasses all failures regardless of their safety implications, while MTTFd specifically targets failures that create hazardous conditions. This focus makes MTTFd the preferred metric for safety applications where distinguishing between nuisance failures and dangerous failures is critical.
Consider a temperature sensor that may fail in two ways: it could provide inaccurate readings (a dangerous failure) or stop communicating entirely (a safe failure that triggers an alarm). Standard MTTF calculations would treat both equally, but MTTFd only considers the dangerous failure mode.
The choice between metrics depends on whether safety consequences drive your decision-making. Use MTTFd when evaluating safety-critical systems, MTBF and MTTR for general maintenance planning, and MTTF for overall reliability analysis. This brings us to the practical matter of accurately calculating MTTFd.
How To Calculate MTTFd
Calculating MTTFd requires a systematic approach that begins with properly identifying what constitutes a dangerous failure in your specific situation. The process involves several key steps that build upon each other.
1. Identify Dangerous Failures
A dangerous failure is any malfunction that either directly creates a hazardous condition or prevents a safety system from responding to one. In industrial settings, this might include a pressure relief valve that fails to open, a safety interlock that doesn't engage, or a gas detection system that fails to alarm.
The concept of performance level “d,” as defined in ISO 13849, provides guidance for classifying failures based on their safety impact. Systems operating at this level must maintain their safety function even when dangerous failures occur in individual components.
To classify failures properly, examine each potential failure mode and ask: Does this failure create immediate danger, or does it prevent the system from protecting against danger? If either answer is yes, it's a dangerous failure for MTTFd calculations.
2. Determine Operating Time
Operating time measurement requires precision because inaccurate time tracking can significantly skew MTTFd calculations. Count only the time when the equipment is actively performing its safety function or standing ready to do so.
Include these elements when measuring operating time:
- Active production hours: Time when equipment is running and safety systems are engaged
- Standby time when equipment is energized: Periods when safety systems remain active even during production pauses
Exclude planned maintenance periods and facility shutdowns when safety systems are intentionally disabled. The goal is to measure the time when dangerous failures could actually occur and impact safety.
3. Apply The MTTFd Formula
The formula for MTTFd is straightforward: MTTFd = Total Operating Time / Number of Dangerous Failures. This MTTF formula focuses specifically on dangerous failure events rather than all failures.
Here's a practical example: A safety interlock system is designed to operate continuously throughout the year. Over a period of several years, there have been instances where the interlock failed to engage, resulting in dangerous failures. To calculate the mean time to dangerous failure, divide the total operating hours by the number of dangerous failures observed.
This MTTF calculation process applies the same mathematical approach but focuses specifically on dangerous failure events. The key difference lies in the careful selection of which failures to include in the denominator.
4. Validate With Real World Data
Statistical validation ensures your MTTFd calculations reflect actual performance rather than theoretical estimates. Compare calculated values against historical performance data and look for patterns that might indicate calculation errors.
Continuous monitoring refines MTTFd estimates over time as more failure data is collected. This validation process determines whether your dangerous failure classification is consistent and whether your operating time measurements accurately reflect the system's exposure to failure.
Where To Get Failure Data
Accurate MTTFd calculations depend on comprehensive failure data, which can be gathered from multiple sources and carefully validated. The quality of your data directly impacts the accuracy of your calculations.
1. Field Observations
Direct observation provides the most relevant failure data because it reflects the specific operating conditions, maintenance practices, and environmental factors that are unique to your system. Establish a systematic failure reporting system that captures not only when failures occur, but also their specific characteristics and safety implications.
Training technicians to recognize and consistently report dangerous failures becomes crucial. This training should include clear examples of what constitutes a dangerous failure versus a nuisance failure, along with standardized reporting procedures that capture essential details, such as operating conditions at the time of failure.
2. Manufacturer Databases
Manufacturer reliability data provides a starting point for calculating MTTF. But this information often requires adjustment for your specific application. Manufacturers typically provide data based on standard operating conditions that may not accurately reflect your specific environment.
Common manufacturer data sources include equipment manuals with reliability sections, technical bulletins that update reliability information, and industry databases that compile failure rate information across multiple manufacturers and applications.
3. Historical Logs
Maintenance records contain valuable failure information, but extracting usable data requires careful analysis and interpretation. Look for patterns in repair descriptions, parts replacement records, and incident reports that indicate dangerous failure modes.
Converting unstructured maintenance notes into quantifiable data requires establishing consistent criteria for failure classification. This process often reveals dangerous failures that weren't initially recognized as safety-critical events.
The annual failure rate calculation can help validate your MTTFd calculations by comparing predicted failure rates with actual experience over extended periods.
Factors That Affect Dangerous Failures
Understanding what drives dangerous failures helps you take targeted action to improve MTTFd values and enhance overall safety performance. Several key factors influence the frequency of dangerous failures.
Quality Of Components
Component quality directly correlates with MTTFd performance, but the relationship isn't always linear. Higher-quality components typically exhibit longer MTTFd values, but the improvement may not justify the additional cost in all applications.
Evaluate component quality during procurement by examining manufacturer reliability data, industry certifications, and field performance history. Quality assessment should also include compatibility with your specific application, as even high-quality components can fail prematurely when used outside their design parameters.
Maintenance Practices
Proactive maintenance strategies significantly extend MTTFd by preventing dangerous failures before they occur. The connection between maintenance quality and safety performance becomes evident when comparing facilities with different maintenance approaches.
Maintenance best practices that extend MTTFd improvements include precision lubrication to prevent premature bearing failures, alignment and balancing to reduce component stress, and condition monitoring for early detection of developing problems.
Environmental Conditions
Temperature extremes, humidity, vibration, and contamination accelerate component degradation and reduce MTTFd values. Harsh environments require more frequent maintenance and may necessitate upgraded components designed for severe service.
Environmental factors affect different components in different ways. Electronic systems may be sensitive to temperature and humidity, while mechanical components might be more affected by vibration and contamination. Understanding these relationships helps predict how environmental conditions will impact your specific calculations.
Improving Mean Time To Dangerous Failure
Extending MTTFd requires systematic approaches that address the root causes of dangerous failures rather than just their symptoms. The annual failure rate formula helps track improvement by comparing the frequencies of dangerous failures before and after implementing improvements.
1. Implement Proactive Maintenance
Preventive and predictive maintenance strategies extend MTTFd by identifying and correcting problems before they become dangerous failures. The ROI of maintenance investments becomes clear when calculated in terms of avoided safety incidents and their associated costs.
Proactive maintenance programs should prioritize safety-critical components based on their Mean Time To Failure (MTTF) values and the consequences of their potentially hazardous failures. Components with shorter MTTFd values or higher safety consequences require more frequent attention.
2. Enhance Diagnostic Coverage
Diagnostic coverage refers to the ability to detect dangerous failures before they create hazardous conditions. Improved diagnostic coverage effectively extends the useful MTTFd by providing early warning of impending dangerous failures.
Technologies that improve failure detection include vibration monitoring for mechanical systems, thermal imaging for electrical components, and process parameter monitoring for control systems. These tools help identify developing problems while there's still time to take corrective action.
3. Raise Redundancy Levels
Redundant systems improve overall safety even when individual component MTTFd values remain unchanged. The key is designing redundancy that addresses dangerous failure modes specifically, not just general reliability.
Creating a new SRP/CS (Safety-Related Parts of Control Systems) with improved redundancy involves careful analysis of failure modes and their interactions. Simple redundancy may not be sufficient if common-mode failures can simultaneously affect multiple redundant components.
Common Obstacles In Managing MTTFd
Real-world challenges often prevent teams from accurately calculating or effectively improving MTTFd, but understanding these obstacles helps develop strategies to overcome them.
1. Gaps In Failure Data
Incomplete failure data creates the most significant obstacle to accurate MTTFd calculations. Many facilities lack systematic failure reporting, making it difficult to distinguish between dangerous and non-dangerous failures or to track failure frequencies accurately.
Strategies for dealing with data gaps include combining manufacturer data with limited field experience, using industry benchmarks as starting points, and implementing systematic data collection for future calculations.
2. Complex Assets
Systems with multiple components present challenges in determining system-level MTTFd from individual component values. The mean time between failure calculation process for complex systems requires fault tree analysis or similar techniques to understand how individual component failures propagate through the system.
3. Limited Skilled Labor
Workforce constraints impact both the calculation and improvement of MTTFd. Accurate failure classification requires trained personnel who understand the difference between dangerous and non-dangerous failures.
Strategies for maintaining safety with limited resources include prioritizing safety-critical systems, implementing automated diagnostic systems that reduce skill requirements for failure detection, and developing standardized procedures that help less experienced personnel make consistent decisions.
Next Steps For Reliable Safety
MTTFd provides a quantitative foundation for managing safety-critical systems. Yet its value depends on accurate calculation, systematic improvement, and integration with broader safety management practices. The challenge is moving from concept to execution in a way that actually improves safety performance.
Digital maintenance management systems can automate much of the data collection and analysis required for effective MTTFd management. These systems track failures systematically, classify them consistently, and calculate reliability metrics automatically as new data becomes available.
Without proper tracking and analysis tools, even the best MTTFd calculations remain academic exercises. You need systems that capture the right data, classify failures correctly, and provide actionable insights that drive real improvements in safety performance.
Tractian CMMS provides the systematic failure tracking and analysis capabilities needed to manage MTTFd effectively. The platform captures detailed failure information, classifies failures by type and safety impact, and automatically calculates reliability metrics. With integrated condition monitoring capabilities, Tractian helps identify potential failures before they become dangerous, effectively extending MTTFd through early intervention.
The system's automated reporting ensures you always have current MTTFd values for critical safety systems, supporting both regulatory compliance and continuous improvement efforts. Rather than managing spreadsheets and manual calculations, you get real-time visibility into the metrics that matter most for safety.
