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Case Study: Defense Firm Utilizes BQR Software for Safety Event Probability Calculations

Updated: Sep 29


Introduction:

A prominent defense company engaged BQR software to assess the probabilities of premature detonation throughout various mission phases of explosive devices. The objective was to demonstrate adherence to client safety requirements, which mandated a probability threshold lower than 5‧10-8. Given the multifaceted nature of explosive devices, encompassing mechanical, electrical, and electronic protections, potential safety events could arise from combinations of protection failures.

Analysis Steps:

MTBF Prediction

  1. Employ MTBF prediction software to calculate component failure rates in accordance with MIL HDBK 217F2 and NSWC standards.

  2. Utilize the MTBF software to generate predictions and assess main tree components.

FMEA

  1. Import MTBF data into BQR's Failure Mode, Effects, and Criticality Analysis (FMECA) software, automatically assigning component-level failure modes.

  2. Conduct FMECA analysis: a. Select the appropriate standard for analysis, with risk matrix, severities list, and criticality or probability groups automatically defined. b. Define the effects of component failures on higher-level assemblies up to the system level. c. Specify severities of system-level failure modes.

  3. Automatically generate FMECA reports, detailing the number of failure modes in each severity and risk level.

Fault Tree Analysis (FTA)

  1. Import the FMECA project into BQR's FTA module as the foundation for Fault Tree Analysis.

  2. Conduct FTA analysis: a. Assign the top safety event. b. Define logical relations (gates) between relevant failure modes, eliminating non-relevant failure modes through a top-down process. c. Incorporate "external" events such as probabilities of severe weather and operator error.

  3. Automatically calculate the probability of the top event, leading causes, and event combinations.

Results:

The assessment revealed that the probability of safety events complied with client requirements. Key drivers for safety events were identified, highlighting a significantly higher probability of mechanical failures compared to electronic failures. Consequently, future product development will prioritize enhancing mechanical reliability. The project files were efficiently updated to analyze the safety of product variants.


MTBF Prediction Main Tree

Risk Analysis Matrix

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