MTBF Montréal is the Center for Reliability, Availability, Maintainability and Safety (RAMS) Engineering at Concordia University and the Montréal’s RAMS Expert's HUB.
Industry 4.0, public transport, e-commerce, intelligent cities or any other competitive Industry requires both extremely safe and top availability performance, at controlled costs. In order to achieve these high standards, successful engineers must apply the RAMS concepts all along the lifecycle of a product; but most importantly, they must actively contribute to a major mentality change: RAMS from concept to culture.
If you want to improve reliability and reduce risk, to reduce the life cycle costs for both product and process, to improve and protect the brand image by reducing warranty costs and customer complaints, to optimize maintenance strategies, to improve availability or to assess and improve design safety, you want to contact us.
remember that A GOAL WITHOUT A PLAN ... IS JUST A WISH !
MTBF Montréal Center at Concordia University is also the perfect common ground to bring together Experts from various Industries, Academics and Students.
Counting on our excellent relationship with Experts from various Industries combined with the enormous potential of our best Students, we set our targets high.
MTBF Montréal is here to:
The MTBF Montréal Center is the initiative of Dr. Sorin Voiculescu, who joined Concordia in 2018 after a rich industrial reliability and safety related experience in a variety of field cases across several large companies in Europe and Canada.
A Reliability Program Assessment is a detailed evaluation of your entire organization's approach and processes across all departments that are involved in creating your products *. The assessment captures the current state of your organization and leads to an actionable Reliability Program Plan (RPP).
When to perform a Reliability Program Assessment*
You need to perfrom an assessment when:
- An established company is trying aggressively to improve their reliability due to a large number of failures in-house and/or in the field.
- An established company is spending too much money on warranty re- turns and needs to find a way to reduce it.
- An established company doesn't know its own stage of reliability on its products or doesn't know why its products are being returned.
- An established company is trying to get into a new market
- A new company is developing its first product.
- A company, established or new, has never written a reliability plan.
Why to perform a Reliability Program Assessment*
You need to perfrom an assessment because:
- A Reliability Program Assessment identifies systemic changes that impact reliability.
- It ties into the culture of your organization and to the product.
- It also provides a roadmap for activities that achieve results.
- It is the matching of capabilities and expectations.
- Allows optimizing the investment in reliability
MTBF Montreal offers an online anonymous quiz, tailored to the Assessment of the Reliability Program, which allows weighting the answers based on the role of the person answering.
The tool comes with a set of in-house developed questions.
MTBF Montreal partners will read the results with you and identify the optimum development steps.
Either in English or French, all our tailored seminars come with a variety of tools and practical examples. For more details, please contact us.
Our expertise is spread over the entire life of a program. Industry oriented, we value any action that long-term secures your business.
For more details, please
We can help with:
Tailored to your needs, we can audit on or ask justification for the reliability targets validation (design capability) or for the reliability targets demonstration (testing). A valuable tool that we propose is the operational / business risk assessment that highlights the potential weak points of the fleet, classified by the specific impact on the business case.
If the safety assessment is a concern to you, we can always evaluate or audit the performed safety studies.
We are also here to support a design comparison among multiple offers. We can help you choose not based on the cheapest price, but on the right long-term operating price, considering the selling price, the reliability, the maintenance cost, as well as the operational / business risk assessment.
The downside is the increase complexity of the control system and the nuisances.
Make sure your units are equipped with the optimized set of sensors. Let us provide our expertise in increasing the availability of the design.
The information developed from the DFMEA will provide excellent input for the earlier phases of the Concurrent Engineering or Integrated Product Development processes, and vice versa. Having some timing overlap (concurrency) between the DFMEA and the PFMEA will further reduce the Time to Market.
If an existing design, on which there is already a DFMEA, is applied in a different environment or usage, then the FMEA should be focused on the impact of the new environment or application.
Let our open-minded students free their creativity and search for potential failure modes and their consequences.
- Provide a safety lifecycle (management, development, production, operation, service, decommissioning) and support tailoring the necessary activities during these lifecycle phases
- Cover functional safety aspects of the entire development process
- Provide an industry-specific risk-based approach for determining risk classes
- Provide requirements for validation and confirmation measures to ensure a sufficient and acceptable level of safety is being achieved
– Tin Whiskers
– Reliability issues with Pb-free Alloys in COTS Electronics
– Unpredictable Service Life & Reliability
- Assess the effect of product reliability on the maintenance activity and on the quantity of spare units required for acceptable field performance of any system. For example, predictions of the frequency of unit level maintenance actions can be obtained. Reliability prediction can be used to size spare populations.
- Provide necessary input to system-level reliability models. System-level reliability models can subsequently be used to predict, for example, frequency of system outages in steady-state, frequency of system outages during early life, expected downtime per year, and system availability.
- Provide necessary input to unit and system-level life cycle cost analyses. Life cycle cost studies determine the cost of a product over its entire life. Therefore, how often a unit will have to be replaced needs to be known. Inputs to this process include unit and system failure rates. This includes how often units and systems fail during the first year of operation as well as in later years.
- Assist in deciding which product to purchase from a list of competing products. As a result, it is essential that reliability predictions be based on a common procedure.
- Can be used to set factory test standards for products requiring a reliability test. Reliability predictions help determine how often the system should fail.
- Are needed as input to the analysis of complex systems such as switching systems and digital cross-connect systems. It is necessary to know how often different parts of the system are going to fail even for redundant components.
- Can be used in design trade-off studies. For example, a supplier could look at a design with many simple devices and compare it to a design with fewer devices that are newer but more complex. The unit with fewer devices is usually more reliable.
- Can be used to set achievable in-service performance standards against which to judge actual performance and stimulate action
Standard methods are available to allow predicting the FAILURE RATE . The predicted failure rate gives a feeling on the failures' frequency.
For electronics, multiple methods are available. The most popular method remains the MIL-HDBK-217 methodology. Though obsolete and pessimists, it is the preferred one for safety critical applications. In a few words, it is considered that the worst case would be if the system that performs as per the predicted reliability. If is capable to withstand the safety requirements under such pessimist approach, any increased reliability performance would simply make it even safer to operate.
MIL-HDBK-217 includes two methods of predicting the reliability:
- Part Count Prediction, used to predict the reliability of a product during its development stage,
- Part Stress Prediction Analysis, used when the product approaches the production phase.
If your system is not safety critical or if you simply need to understand what the system is most likely to perform, we may suggest using the more recent developed FIDES approach. Though more complex as approach, the tool is capable to predict a more realistic prediction. Our lab has developed an interface that facilitates the use of this tool for large and complex electronic boards, providing both efficiency and data quality.
Mechanical designs are often addressed by employing an appropriate data sources for part reliability.
For any reliability test, two concerns need to be addressed:
- statistical test aspects: number of cycles, sample size, acceleration conditions, extrapolations, selection of statistical model to employ, hypothesis testing, etc.
- test representativeness: setting up a representative is always the challenge when moving from theory (statistics) to practice (test set-up). Tailored settings need to be considered when deciding to move on with a specific test:
When a predictive maintenance strategy is working effectively, maintenance is only performed on machines when it is required, thus reducing the parts and labor costs associated with replacements. With more and more systems shipping with Internet connectivity, the concept of predictive maintenance is likely to expand exponentially in the Internet of things*.
The aim of CBM is to maintain the correct equipment at the right time. CBM is based on using real-time data to prioritize and optimize maintenance resources. Observing the state of the system is known as condition monitoring. Such a system will determine the equipment's health and act only when maintenance is actually necessary**.
Development in recent years has allowed extensive instrumentation of equipment, and together with better tools for analyzing condition data, the maintenance personnel of today are more than ever able to decide when the right time to perform maintenance on some piece of equipment is. Ideally, CBM will allow the maintenance personnel to do only the right things, minimizing spare parts cost, system downtime and time spent on maintenance.
One necessary action in achieving the above is to implement tailored monitoring systems designed to provide the actionable data necessary to fully implement application-specific condition-based maintenance strategies.
Let us join our forces in a large project that will lead to a cost-effective maintenance.
* extract from Improve maintenance with Internet of Things web page
** extract from Reliable plant web page
*Reliability Growth Analysis
There are four principles that are critical for an RCM program.
1.The primary objective is to preserve system function
2. Identify failure modes that can affect the system function
3. Prioritize the failure modes
4. Select applicable and effective tasks to control the failure modes
Equipment reliability and availability, achieved by minimizing the probability of system failure is the focus of Reliability Centered Maintenance (RCM). With this maintenance strategy, the function of the equipment is considered, and possible failure modes and their consequences are identified. Maintenance techniques that are cost-effective in minimizing the possibility of failure are then determined. The most effective techniques are then adopted to improve the reliability of the facility as a whole.
* Reliability centered maintenance - Wikipedia
**Reliability Centered Maintenance
Though in theory same steps are required (Failure Reporting, Analysis and Corrective Action System), a tailored implementation is always required. Such, both an optimized input time and accurate conclusion can be achieved at the same time.
1. Review the process — Use a process flowchart to identify each process component.
2. Brainstorm potential failure modes — Review existing documentation and data for clues.
3. List potential effects of failure — There may be more than one for each failure.
4. Assign Severity rankings — Based on the severity of the consequences of failure.
5. Assign Occurrence rankings — Based on how frequently the cause of the failure is likely to occur.
6. Assign Detection rankings — Based on the chances the failure will be detected prior to the customer finding it.
7. Calculate the RPN — Severity X Occurrence X Detection.
8. Develop the action plan — Define who will do what by when.
9. Take action — Implement the improvements identified by your PFMEA team.
10. Calculate the resulting RPN — Re-evaluate each of the potential failures once improvements have been made and determine the impact of the improvements.
Except for step 9, let us provide you our high-quality analysis of your process.
No manufacturing facility is 100-percent reliable. There is always some level of waste due to inefficiencies and unreliability. So, opportunities to improve exist, and these improvements can impact three key areas in which every organization wants to improve: customer loyalty, employee satisfaction and, of course, profitability. However, improvement is a journey that requires time, culture change and dedication to performance. There are no shortcuts, and the journey can never be completed without a commitment to continuous improvement.
Today, an increasing focus on reliability is putting many forward-thinking organizations on the path to high-level performance and even higher profitability. The path begins with executive support and is marked by a company-wide shift in focus toward reliability. This shift in focus is what sets top organizations apart from others when an improvement initiative is undertaken.
We support by organizing seminars and workshops in order to ease the cultural shift within your company. Our tailored presentations cover the life cycle of a program. Contact us for further details.
- Very complex systems can be analyzed. “Unknown unknowns” that were previously only found in operations can be identified early in the development process and either eliminated or mitigated. Both intended and unintended functionality are handled.
- Unlike the traditional hazard analysis methods, STPA can be started in early concept analysis to assist in identifying safety requirements and constraints. These can then be used to design safety (and security) into the system architecture and design, eliminating the costly rework involved when design flaws are identified late in development or during operations. As the design is refined and more detailed design decisions are made, the STPA analysis is also refined to help make more and more detailed design decisions. Complete traceability from requirements to all system artifacts can be easily maintained, enhancing system maintainability and evolution.
- STPA includes software and human operators in the analysis, ensuring that the hazard analysis includes all potential causal factors in losses.
- STPA provides documentation of system functionality that is often missing or difficult to find in large, complex systems.
- STPA can be easily integrated into your system engineering process and into model-based system engineering.
Many evaluations and comparisons of STPA to more traditional hazard analysis methods, such as fault tree analysis (FTA), failure modes and effects criticality analysis (FMECA), event tree analysis (ETA), and hazard and operability analysis (HAZOP) have been done.
In all of these evaluations, STPA found all the causal scenarios found by the more traditional analyses, but it also identified many more, often software-related and non-failure, scenarios that the traditional methods did not find. In some cases, where there had been an accident that the analysts had not been told about, only STPA found the cause of the accident. In addition, STPA turned out to be much less costly in terms of time and resources than the traditional methods.
Follow 4 simple steps:
1. Identify your need. Use any generic topic listed at RAMS activities or define your specific need.
2. Partner with Concordia's Faculty Members and access one of the many possibilities to increase your R&D activities.
3. Pick your most effective solution:
4. Get the project done.
For more details, please contact us.
Feedback on previous event:
Our final goal is to support the local economy to improve competitiveness and to acquire world-wide recognition.
To achieve this goal, we strongly believe that non-competitive experience sharing can be beneficial and that various Industry domains can learn from each other. For example, due to its extremely safety regulated environment, the Aerospace Industry should be the best choice to share expertise with any non-Aerospace Industry willing to excel in product safety.
The RAMS Center at Concordia University is the perfect common ground to gather around recognized Experts from various local Industries. Their regular meetings will be an excellent opportunity for experience exchanges and the expertise sharing contributes to the implementation of the most added-value tools and to the development of new methods.
The intent is to also establish international relationships with recognized RAMS experts across the world, exchanges experiences and observes international trends.
This initiative is developed in close partnership with SREMONTREAL.ORG: Montreal Chapter of the Society of Reliability Engineers.
As Professors, we know our students: we meet them in class for 13 weeks, we interact with them by projects, assignments, exams, etc., so we can identify the best fitted student to your needs.
If you need a student to hire or for a project (e.g. internship, CAPSTONE, etc.), please check
our dedicated page or simply
Universities need to prepare students for a world that, in many ways, will be dramatically different from today’s. Providing a next-generation education means grounding students in the academic fundamentals while providing them with the kinds of skills and knowledge that will prepare them for the emerging demands of work, citizenship, and life. Students learn more, finish their degrees at higher rates, and are more likely to achieve positive life outcomes when they are highly engaged at university*.
Experiential learning is one of the most effective forms of engagement. It provides students with complex, collaborative tasks and projects that test their conceptual knowledge against experience and situate their learning in real-world contexts. More hands-on opportunities for students complement and extend what happens in classrooms, providing transformative learning experiences that encourage students’ civic involvement and enhance their career readiness*.
Dr. Sorin Voiculescu, Lecturer
1515 Saint-Catherine St W
Montreal, QC, H3G 2W1
Phone: (514) 848-2424, ext. 3128