Accendo Reliability

Your Reliability Engineering Professional Development Site

  • Home
  • About
    • Contributors
  • Reliability.fm
    • Speaking Of Reliability
    • Rooted in Reliability: The Plant Performance Podcast
    • Quality during Design
    • Way of the Quality Warrior
    • Critical Talks
    • Dare to Know
    • Maintenance Disrupted
    • Metal Conversations
    • The Leadership Connection
    • Practical Reliability Podcast
    • Reliability Matters
    • Reliability it Matters
    • Maintenance Mavericks Podcast
    • Women in Maintenance
    • Accendo Reliability Webinar Series
  • Articles
    • CRE Preparation Notes
    • on Leadership & Career
      • Advanced Engineering Culture
      • Engineering Leadership
      • Managing in the 2000s
      • Product Development and Process Improvement
    • on Maintenance Reliability
      • Aasan Asset Management
      • AI & Predictive Maintenance
      • Asset Management in the Mining Industry
      • CMMS and Reliability
      • Conscious Asset
      • EAM & CMMS
      • Everyday RCM
      • History of Maintenance Management
      • Life Cycle Asset Management
      • Maintenance and Reliability
      • Maintenance Management
      • Plant Maintenance
      • Process Plant Reliability Engineering
      • ReliabilityXperience
      • RCM Blitz®
      • Rob’s Reliability Project
      • The Intelligent Transformer Blog
      • The People Side of Maintenance
      • The Reliability Mindset
    • on Product Reliability
      • Accelerated Reliability
      • Achieving the Benefits of Reliability
      • Apex Ridge
      • Metals Engineering and Product Reliability
      • Musings on Reliability and Maintenance Topics
      • Product Validation
      • Reliability Engineering Insights
      • Reliability in Emerging Technology
    • on Risk & Safety
      • CERM® Risk Insights
      • Equipment Risk and Reliability in Downhole Applications
      • Operational Risk Process Safety
    • on Systems Thinking
      • Communicating with FINESSE
      • The RCA
    • on Tools & Techniques
      • Big Data & Analytics
      • Experimental Design for NPD
      • Innovative Thinking in Reliability and Durability
      • Inside and Beyond HALT
      • Inside FMEA
      • Integral Concepts
      • Learning from Failures
      • Progress in Field Reliability?
      • R for Engineering
      • Reliability Engineering Using Python
      • Reliability Reflections
      • Testing 1 2 3
      • The Manufacturing Academy
  • eBooks
  • Resources
    • Accendo Authors
    • FMEA Resources
    • Feed Forward Publications
    • Openings
    • Books
    • Webinars
    • Journals
    • Higher Education
    • Podcasts
  • Courses
    • 14 Ways to Acquire Reliability Engineering Knowledge
    • Reliability Analysis Methods online course
    • Measurement System Assessment
    • SPC-Process Capability Course
    • Design of Experiments
    • Foundations of RCM online course
    • Quality during Design Journey
    • Reliability Engineering Statistics
    • Quality Engineering Statistics
    • An Introduction to Reliability Engineering
    • Reliability Engineering for Heavy Industry
    • An Introduction to Quality Engineering
    • Process Capability Analysis course
    • Root Cause Analysis and the 8D Corrective Action Process course
    • Return on Investment online course
    • CRE Preparation Online Course
    • Quondam Courses
  • Webinars
    • Upcoming Live Events
  • Calendar
    • Call for Papers Listing
    • Upcoming Webinars
    • Webinar Calendar
  • Login
    • Member Home

by Carl S. Carlson 2 Comments

Understanding FMEA Detection Risk – Part 2

Understanding FMEA Detection Risk – Part 2

Can you find this common error in detection ranking in the intermediate problem in this article? In the advanced problem, the topic of an in-service detection scale will challenge the most experienced readers.

If you haven’t already read the article Understanding FMEA Detection Risk – Part 1, this may be a good time to read about the underlying fundamentals of assessing detection risk in FMEAs.

Beginner’s Problem

You are doing a Design FMEA and it is time to identify the detection ranking for a given failure mode/cause. One of your team members asks you to clarify the definition of detection. Identify which of the following are correct or incorrect responses to this question?

1. Detection ranking considers the likelihood that the current detection-type design controls will detect the failure mode/cause.
2. Detection ranking is associated with prevention-type design controls.
3. Detection is the likelihood that the effect of the failure mode will manifest sometime during the product life cycle.
4. Detection ranking is associated with detection-type design controls.

Beginner’s Solution

1. Detection ranking considers the likelihood that the current detection-type design controls will detect the failure mode/cause. Correct
2. Detection ranking is associated with prevention-type design controls. Incorrect.
3. Detection is the likelihood that the effect of the failure mode will manifest sometime during the product life cycle. Incorrect.
4. Detection ranking is associated with detection-type design controls. Correct

Intermediate Problem

You are doing a Design FMEA on the hand brake subsystem of an all-terrain bicycle. Review the following excerpt from this FMEA and determine the error in detection ranking.

Intermediate Solution

The team has assigned a detection ranking of “2” for the cause “cable binds due to inadequate lubrication.” Remember, the definition of detection is “a ranking number associated with the best control from the list of detection-type controls, based on the criteria from the detection scale.” Since there is no detection-type control for this cause, the detection risk would be very high.

Advanced Problem

Company X is developing a next generation sub-sea drilling system. Although it is important to detect failure modes and their causes before the new system goes into operation, it is even more important that failures be detected once the system is operating, so that mitigating action can be taken to avoid a potential catastrophe. How can a detection scale be configured to assess detection risk during operation?

Advanced Solution

In-service detection techniques can be designed-in to system operations. An example is a warning system in a nuclear power plant in which sensors detect an emerging problem, alerting personnel who can then prevent the problem or avert it before an accident or serious consequence occurs.

It is possible to define criteria for the detection scale that assesses the likelihood of the monitoring-type control to detect the problem during system operation. The nature of the application should determine the specific criteria of this unique detection scale.

Figure 2 is an example of an in-service detection scale. This scale can be configured to the unique circumstances of the operations being assessed.

Figure 2

Next Article

What if a production worker uses a different cleaning method for an assembly operation than was outlined in the operation work instructions, and the result is customer complaints and field issues. How could this be addressed in a Process FMEA? This question is discussed and answered in the next FMEA Q and A article.

Check out he new FMEA Resources page. It has free downloadable FMEA information and aids.

Filed Under: Articles, Inside FMEA, on Tools & Techniques

About Carl S. Carlson

Carl S. Carlson is a consultant and instructor in the areas of FMEA, reliability program planning and other reliability engineering disciplines, supporting over one hundred clients from a wide cross-section of industries. He has 35 years of experience in reliability testing, engineering, and management positions, including senior consultant with ReliaSoft Corporation, and senior manager for the Advanced Reliability Group at General Motors.

« Defining Precision Maintenance
Margin Call »

Comments

  1. Francisco says

    July 9, 2020 at 11:15 AM

    Hi Carl,

    Thank you for your excellent book.

    I would like to ask one question in regards to Key characteristics (KC). My doubt is if Detection can participate in the designation of KC. My reasoning is the following: There are features of a product which are easier to detect the failure modes (or the root causes causing that failure mode), e.g.: a electrical measurement it is easy to detect in case of deviation if it is measured 100%, however other parameters for instance coplanarity are more complex to be measured and thus to be detected. Does it make any sense to consider Detection also in the designation criteria for KC in that case apart from Severity and Occurrence? If the detection is “easy” is not considered a a KC, however for “difficult” detection levels can be considered, so the final designation of KC is done considering Severity, Occurrence and Detection, especially for Significant characteristics. I hope you understand the question. Thank you so much!

    Reply
    • Carl Carlson says

      July 10, 2020 at 6:37 PM

      Hello Francisco,
      This is an excellent question.
      Although companies can have their own special characteristics policy, there are certain attributes of sp. char. that are important to consider. For example, they should be measurable characteristics of the finished product, and outputs of design that if nonconforming have a potential to impact the safe or proper use of the product. They can be derived from DFMEA. Some companies use Sev as the primary way to designate sp char, others use a combination of Sev and Occ. The reason Det usually does not enter into the policy is being able to measure the sp char is part of the definition. The important aspect of sp char is identifying characteristics that have the potential to impact safe and proper use of the product. If such characteristics can be addressed by robust design, that is best. If not, designating as sp char can help to control the outcome, provided they are followed up with good controls.
      Please let me know if this answers your question.
      Carl

      Reply

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Articles by Carl Carlson
in the Inside FMEA series

[popup type="" link_text="Logo Info" ]

Information about FMEA Icon

Inside FMEA can be visually represented by a large tree, with roots, a solid trunk, branches, and leaves.

- The roots of the tree represent the philosophy and guiding principles for effective FMEAs.
- The solid trunk of the tree represents the fundamentals for all FMEAs.
- The branches represent the various FMEA applications.
- The leaves represent the valuable outcomes of FMEAs.
- This is intended to convey that each of the various FMEA applications have the same fundamentals and philosophical roots.

 

For example, the roots of the tree can represent following philosophy and guiding principles for effective FMEAs, such as:

1. Correct procedure         2. Lessons learned
3. Trained team                 4. Focus on prevention
5. Integrated with DFR    6. Skilled facilitation
7. Management support

The tree trunk represents the fundamentals of FMEA. All types of FMEA share common fundamentals, and these are essential to successful FMEA applications.

The tree branches can include the different types of FMEAs, including:

1. System FMEA         2. Design FMEA
3. Process FMEA        4. DRBFM
5. Hazard Analysis     6. RCM or Maintenance FMEA
7. Software FMEA      8. Other types of FMEA

The leaves of the tree branches represent individual FMEA projects, with a wide variety of FMEA scopes and results. [/popup]

Join Accendo

Receive information and updates about articles and many other resources offered by Accendo Reliability by becoming a member.

It’s free and only takes a minute.

Join Today

Recent Posts

  • test
  • test
  • test
  • Your Most Important Business Equation
  • Your Suppliers Can Be a Risk to Your Project

© 2025 FMS Reliability · Privacy Policy · Terms of Service · Cookies Policy