Failure Modes & Effects Analysis Use Case

Learn how to conduct failure methods and effects analysis using Innoslate's simulation capabilties.

Introduction

Failure Modes and Effects Analysis (FMEA) is a proactive technique used to identify potential failure modes, their causes, and the effects of those failures on system performance. It involves analyzing the various components, subsystems, and processes of a system to anticipate and prevent failures. 

FMEA helps identify and prioritize areas where improvements can be made to enhance reliability, safety, and maintainability. It uses a structured approach to evaluate the severity, occurrence, and detectability of potential failure modes, resulting in a risk priority number (RPN) that aids in prioritizing actions for mitigation. Through this structured approach engineers are capable of analyzing the impact each failure mode has to cost, schedule, and performance of tasks and project elements.

* To follow along and try out the features mentioned in this walkthrough, click here to instantly download the FMEA Project .INNO File and import it into a new Innoslate project.

Walkthrough Use Case

Navigate to the Diagrams Dashboard and select the 'F Vehicle Failure Process' diagram.

The action diagram represents a decision-making process for vehicle operation, involving a continuous monitoring loop (F.1) set at 200 iterations for this analysis. The process starts with determining whether to continue operating the vehicle. If the decision is to continue, it proceeds to check for any failures (F.2). If a failure is detected (10% likelihood), the system executes Failure Mode and Effects Analysis (FMEA) actions to address the issue. If no failure is detected (90% likelihood), the vehicle continues normal operations (F.11).

The decomposed action diagram for "FMEA Actions" details specific responses to different vehicle failures identified in the initial action diagram. The process begins with determining the type of failure mode (F.3). There are three primary failure scenarios considered:

• Flat Tire (50%):

  • F.4: Change Tire - The process involves changing the flat tire and then proceeding to normal operations.
    • Duration: triang.dist(0.5, 1, 0.75) hours
    • Cost: New Tire ($150 Fixed)

• Out of Gas (30%):

  • F.5: Walk to/from Gas Station - If out of gas, the process involves walking to a gas station to get fuel.
    • Duration: triang.dist(0.5, 3, 1.5) hours
  • F.6: Drive to Gas Station and Fill Up - After acquiring fuel, the vehicle is driven to a gas station to fill up, then normal operations resume.
    • Duration: triang.dist(0.25, 1, 0.5) hours
    • Cost: Fill-up Gas ($50 Fixed)

• Engine Failure (20%):

  • F.7: Call Tow Truck - In the event of an engine failure, the process involves calling a tow truck.
    • Duration: 5 minutes
    • Cost: Tow Truck - Assuming no insurance ($200 Fixed)
  • F.8: Wait for Tow Truck - Waiting for the tow truck to arrive.
    • Duration: triang.dist(0.5, 1.5, 1) hours
  • F.9: Ride to Repair Station - After the tow truck arrives, the vehicle is taken to a repair station.
    • Duration: triang.dist(0.25, 0.75, 0.5) hours
  • F.10: Wait for Fix or Call Uber - Waiting for the vehicle to be fixed or calling an Uber for alternative transportation.
    • Duration: triang.dist(1, 3, 2) hours
    • Cost: Repair Engine - triang.dist($200, $1000, $500) 

Each failure mode triggers specific actions to resolve the issue, including duration and cost considerations where relevant, ensuring a structured and systematic approach to addressing various vehicle failures. By incorporating quantitative parameters such as Time and Cost into the model, engineers can effectively simulate their models using Innoslate's Discrete Event and Monte Carlo Simulators to meet various FMEA needs.

Discrete Event Simulation (DES) and Monte Carlo Simulation (MCS) offer different insights for FMEA. DES focuses on the sequence and timing of events, allowing engineers to analyze the impact of process changes over time, making it useful for operational efficiency and scheduling. MCS, on the other hand, uses random sampling to model uncertainty and variability, helping to assess the probability and impact of different failure scenarios. 

Discrete Events Simulation Results

The Discrete Event Simulation (DES) was ran to calculate the cost, cost breakdowns, resource utilizations, and how the two parameter's usage increased overtime. Some of the notable findings were:

• Total Cost: The one-time simulation incurred a Total Cost of $3695.57 over 2.06 years.
• Flat Tire: This failure mode shows the highest resource usage and cost over time, indicating frequent occurrence and significant impact.
• Out of Gas: Moderate resource usage and cost, with consistent increments reflecting periodic refueling needs.
• Engine Failure: Least frequent but still impactful, with notable costs for towing and repairs.
• Cost Distribution: The pie chart reveals that changing tires and waiting for fixes/Using Uber were the two most significant cost drivers.

The DES offers valuable insights into the operational challenges and cost implications of vehicle failures. By identifying the most frequent and costly issues, such as flat tires and the need for alternative transportation, the analysis highlights areas for potential improvements and resource optimization, all while identifying the failure modes and mitigation methods to follow. 

Monte Carlo Simulation Results

The Monte Carlo Simulation (MCS) provided a comprehensive analysis of cost and time variations associated with vehicle failure modes.

• The status chart indicates a 100% completion with a mean duration of 2.06 years and a standard deviation of 17.77 days. The mean cost is $4,644.20 with a standard deviation of $1,370.29.
• The cost tree map highlights that waiting for fixes or using Uber (F.10) and calling a tow truck (F.7) are the major cost drivers.
• The time tree map shows that continuing normal operations (F.11) is the predominant activity.
• Bar charts for cost and time reveal distributions across different simulation scenarios, emphasizing the variability in outcomes. 

One notable utility of the MCS is the creation of distribution bar charts that allow engineers to create a range of cost and time evaluation. In this simulation results we can conclude that the owner of this vehicle can expect to spend an average of $3,000-$4,000. However, the owner could spend as much as $8,000-$9,000 over two years.

The MCS effectively highlights the cost and time variability associated with vehicle failure modes, offering valuable insights for risk management using sampling simulation over 100 iterations. By visualizing the distribution of potential outcomes, it helps engineers understand the range of costs and possible higher expenses over two years. This comprehensive analysis aids in making informed decisions about maintenance and operational strategies, ensuring better preparedness and optimized resource allocation to handle vehicle failures efficiently.

In conclusion, FMEA serves as a valuable tool for proactively managing risks and efficiently utilizing resources to address system failures. Through the application of Innoslate's modeling and simulation methods and the analysis of failure modes, engineers are enabled to make well-informed decisions that enhance system performance and reliability.

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