5.2 Reliability Modelling

In our increasingly digitized and networked environment, the expectations for excellence in asset management are ever growing. While an abundance of maintenance and sensor data have become available, companies must develop the proper application of the data in their maintenance strategies. In this presentation, we’ll discuss the potential of your operational and maintenance data in the context of asset management, and explore different machine learning (ML) algorithms and how they may be leveraged to unleash hidden patterns in your asset management strategies. We’ll introduce some foundational topics required for ML, such as the taxonomy and data preparation steps critical to all ML approaches, the probability and statistics supporting ML, and how the evaluation of the quality of our models. C-MORE has actively applied machine ML methods to interesting real-world problems, such as the categorization of power generation units according to reliability characteristics, and anomaly detection in linear assets to optimize required maintenance actions. We’ll share a few of our case studies so participants can experience how ML methods can be used in maintenance, reliability and operations.

Companies are continually faced with competing priorities and limited resources, and, as a result, it's critical to drive value from the business decisions they make. With more focus on asset management principles, these decisions must be justified through demonstrated and quantified value to the business. With complex systems of equipment, such as a pipeline, determining the value of a project or proposed improvement can often be difficult to estimate—especially when considering an asset's lifespan. In such cases, a more systematic, data-driven approach may be required to predict value. RAM models are one such tool that can be used to achieve this. In this case study, we'll look at how RAM models have been developed and used at Enbridge Pipelines to identify and quantify the risks to throughput volume. The models include key equipment and operational events, along with specific throughput impacts and simulated long-range demand forecasts in order to prioritize the risks and focus resources toward capturing the best opportunities. When specific improvement opportunities (e.g., additional equipment sparing) are identified, alternate cases of the model can be created and the results compared to the base case to support decisions and justify projects. We'll explore some of the challenges we encountered during development and highlight examples of how this tool is being used to quantify value, as well as the approach taken to disseminate the information within the organization.

Quelle est l’opportunité pour les gens de maintenance dans l’Industrie 4.0 ?- Constat de la maturité de la maintenance au Québec- Rappel de vieux concepts d’ingénierie de maintenance- Survol des concepts de l’Internet des objets et de l’Industrie 4.0- Analyse de l’opportunité 4.0

Reliability Centered Maintenance – Reengineered, provides an optimized approach to a well established and highly successful method used for determining failure management policies for physical assets. It makes the original method that was developed to enhance flight safety, far more useful in a broad range of industries where asset criticality ranges from high to low. RCM-R® is focused on the science of failures and what must be done to enable long term sustainably reliable operations. If used correctly, RCM-R® is the first step in delivering fewer breakdowns, more productive capacity, lower costs, safer operations and improved environmental performance. Maintenance has a huge impact on most businesses whether its presence is felt or not. RCM-R® ensures that the right work is done to guarantee there are as few nasty surprises as possible that can harm the business in any way. RCM-R® addresses the shortfalls of RCM that have inhibited its broad acceptance in industry. Little new work has been done in the field of RCM since the 1990’s, yet demand for such a method, better adapted to industrial applications is higher than ever and growing. Demographics and ever more complex systems are driving a need to be more efficient in our use of skilled maintenance resources while ensuring first time success – greater effectiveness is needed. RCM-R® was developed to leverage on RCM’s original success at delivering that effectiveness while addressing the concerns of the industrial market. RCM-R® addresses the RCM method and shortfalls in its application. It modifies the method to consider asset and even failure mode criticality so that rigor is applied only where it is truly needed. It removes (within reason) the sources of concern about RCM being overly rigorous and too labor intensive without compromising on its ability to deliver a tailored failure management program for physical assets sensitive to their operational context and application. RCM-R® also provides its practitioners with standard based guidance for determining meaningful failure modes and causes facilitating their analysis for optimum outcome. It places RCM into the Asset Management spectrum strengthening the original method by introducing International Standard based risk management methods for assessing failure risks formally. RCM-R® employs quantitative reliability methods tailoring evidence based decision making whenever historical failure data is available.

R&M professionals are typically the main drivers and beneficiaries of an RCM or similar reliability study at a facility. However, when you invite operations and other key business personnel to participate, we’ve found it often opens their eyes to M&R improvement opportunities and helps paint the picture for future joint improvement efforts. Organizations are then able to operate with the most efficiency, driving toward a world-class reliability program with plant-wide buy-in for the reliability improvement journey. This presentation will discuss a case study of a joint client and partner consultant approach of choosing a machine or line performing below desired performance levels. Using an RCM approach to improve maintenance strategies, the organization experienced reduced downtime and less labour reallocation and idle time, and gained many instant wins like increased visibility in the maintenance budget and increased collaboration between facilities.Presented at MainTrain 2018

With all the talk about big data and the IIoT, many are asking how can we use this in maintenance? The IIoT enables us to put sensors in any location where we might want to collect and analyze equipment condition and performance data. There are companies that offer predictive maintenance services, and some companies do this for themselves, in-house. Typically, it’s the larger companies that can afford this, but democratization has meant this has become available to a much broader market. But there are hurdles to taking advantage of this sort of continuous monitoring program, even for your most critical equipment. One, it’s expensive, whether you do it in-house or outsource. And two, there are data bottlenecks. Condition monitoring data comes is huge volumes and it’s all time-sensitive. Even if you can afford it, you need a data handling network with a lot of capacity. In this workshop, we’ll present a viable technical solution to the data bottleneck problem — based on a solution already proven in financial securities markets — that opens up these possibilities in the realm of plant continuous condition monitoring.

Reliability is a powerful concept that applied properly makes the difference between success and failure of installations, processes and systems; nevertheless, reliability does not need to be difficult, complex and overwhelming. Quite the opposite: reliability applications  are more likely to succeed if they are easily understood by the different levels of the organization from trades to CEO. This is achieved by explaining reliability concepts easily understood by everyone within the organization.The general objective of the workshop is to provide theory and practical guidelines to the audience for identifying and addressing reliability issues that are impacting the productivity and profitability the assets they are responsible for.The workshop will provide basic knowledge and understanding regarding the tools and methodologies available within the reliability discipline, for the design, analysis and implementation of equipment strategies to systematically improve the performance of assets.At the end of the training the assistants will be able to:- Understand the current Reliability Engineering terminology- Understand and apply reliability concepts at all levels of the organization- Understand and overcome the typical reliability paradigms- Collect the adequate data and calculate basic maintenance and reliability Metrics- Understand the different reliability methodologies and the right application framework for each one- Combine different reliability methodologies to create an effective reliability program- Understand the role that Reliability Engineering plays within the Asset Management FrameworkThe workshop is specifically designed for current or future reliability engineers, maintenance supervisors and planners with maintenance, engineering and/or operations background that desire to be involved in improving reliability, availability and maintainability of assets through the application of modern reliability methodologies and tools.

Vibration condition monitoring programs play an important role in an organization's rotating equipment reliability and maintenance strategy.  While organizations may be in different stages of establishing their program, there are some common things to implement in order to get the greatest benefit from any program.  This workshop focusses on starting your program off on the right foot.  The workshop will use case histories to outline reasonable expectations, current vibration technology, and specific vibration standards for various industries.In addition to starting off on the right foot the success of a vibration condition monitoring program will depend on maintaining momentum.  This workshop will finish by addressing areas within the participants program where they can focus on improving the long term reliability of their assets instead of simply finding failures.

In the face of global competition organizations must innovate to achieve their strategic goals as cost effectively as possible. In this session hear how the BHP Billiton Maintenance and Improvement team worked to validate the capability of the world’s biggest greenfield potash project. Using key pillars such as: asset management plan aligned with business goals, reliability design, maintainability by design, work management implementation plan, and asset care strategy development, this team unlocked new horizons of effective development for reliability and maintainability professionals. Thinking outside the box, using common and accessible tools, while determining the best stage each method should be implemented has been key to the team’s success.

Hydro facilities rely on spillway gates to protect the dam integrity by releasing water when too much water is entering the reservoir.  Spillway gates are critical flow discharge components in the dam design and need to be available to operate on demand.  This session explores the learnings from BC Hydro projects, over an 8 year period using reliability methods for project planning and spillway system equipment analysis for improvement projects and for new spillway systems.  See how Spillway projects can benefit by including reliability methods in their pre-detailed design phase. Early deployment of reliability analysis allows for optioneering work and the development of suitable design alternatives.  Reliability Block Diagrams (RBD) can be applied to improve design and to evaluate alternative design configurations.