05 Maintenance & Reliability Engineering

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.

One major challenge at the operate and maintain phase of an asset is achieving and sustaining the forecasted availability and reliability as intended at the project delivery phase. Many problems arise—equipment failures, underperformance, high costs—that are caused by numerous issues. The resolution demands thorough understanding of the causes of the issues, which we usually attempt to achieve through RCA methodologies. I've experienced many repeated failures even when RCAs have been conducted, due, mainly, to most of the RCAs focusing attention on solutions to the problem outcomes with limited focus on the human and system causes that drive the outcomes. The Causal Learning Approach brings in the understanding of these other causes that ensure effective and sustainable solutions development. There are three levels of causes: the physical outcomes; the human causes; and the system causes. The Causal Learning Approach also focuses on causal reasoning instead of defensive and solution reasoning. This presentation will provide the understanding of these causes and the three key elements of this approach: discovery, learning, and solution generation.

When entering a maintenance record into a CMMS, there's often a place where the operator can enter free-form comments. These comments may contain valuable information about the health of the equipment, any maintenance activities that were undertaken, and plans or recommendations for the future. The flexibility of the comments is attractive to operators, as a precise description of the observations can be recorded. However, using the comments information in data analysis usually requires some codifying of the comments, which is time-consuming and results in a loss of nuanced information. A machine learning approach to using comments data has been applied to predict the health of hydroelectric generating units. By embedding comments into a matrix to generate a "bag of words," and applying neural networks on the vocabulary, comments can be used to assess the current state of the asset and predict its next state. In this presentation, we'll discuss three machine learning algorithms in a way that's accessible and relevant to M&R practitioners: a classification method, a clustering method, and a neural network method. Each method will be partnered with direct applications to real-life maintenance problems, including lessons learned and potential uses in other contexts.

The U.K. railway network dates back to 1825 and is the oldest railway in the world. Several electrical assets on the network such as track power cables, switchgears, overhead line isolators, circuit breakers, and insulators are beyond their design life and the business must decide whether to renew or replace them—even though they're still operating at the optimum performance level. These assets are still being maintained at the original regimes; the challenge to the business is to understand the degradation models and change them to achieve different maintenance regimes for the aging assets. The work we're currently undertaking is intended to influence and change our asset policies—in particular, the assignment of asset regimes for assets that remain in service at the end of their design life and beyond. The philosophy behind the maintenance regimes is that they're based on degradation models, which are algorithms that consider various factors such as the environment, the loading, the utilization, the reliability, and the cost for interventions. The approach we pursued was to review the parameters of the degradation models for their “fit,” based on the knowledge asset managers have gained on the ground and through large volumes of asset data. The asset data was analyzed with data visualization software to gain further insight to influence the review of the degradation models. The findings of the work are summarized here: asset population is aging and future renewals bow wave are predicted; asset policy pushes all assets to maximum asset technical life and fix-on or run-to failure; safety-related works prioritized over asset performance/resilience; there's a need to modify some factors associated with the degradation models to cater for extension of technical asset life and maintain a more realistic/sustainable asset renewal profile; composite asset condition scores are required to manage bow wave of asset renewals and implement sustainable obsolescence management techniques (this is predominantly driven by organizational investment decisions where enhancements are the main driver of asset acquisition, making future renewals difficult due to the requirement to renew similar age assets at the same time); and determination of useful asset life required for assets that are being left in service longer than their originally predicted life.

Metrics, best practices, more than 40 key elements to implement, challenges, and opportunities all combine to make a successful implementation difficult. Where do you start, and how do you know how to work on what matters? Once you understand how it’s all related, you can focus on the vital few to leverage the maximum ROI. This presentation will clarify the importance of culture and employee engagement, along with other key plant floor performance indicators that will be clarified with data. We'll look at the current state of R&M; what’s working and what's not; survival skills for the next decade; impacts of connected technologies (edge computing, big data, machine learning, AI, 3D printing, augmented reality); the importance of getting your data ready for what's coming next; and relationships between R&M and safety, people engagement, quality, throughput/uptime, and cost.

Many maintenance and reliability staff are so busy fixing problems that they never get the chance to prevent them. In a reactive work environment, there is simply no time to spare. Root cause analysis (RCA) gives us an easy-to-implement approach to preventing failures that integrate with our current troubleshooting efforts and drives bottom-line business improvement. We can make our workplaces safer by reducing the number of unexpected failures, which will then result in improving our business performance, increasing our facility’s throughput and reducing the money spent on repairs – straight to the bottom line.

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

Every failure is part of a puzzle. The equipment we are maintaining is trying to communicate with use with each and every failure. From alignment errors to lubrication mistakes, to material degradation or wear, there are clues and indications in every failure. And, if we’re paying attention, we can sort out the root cause of the failure along with replacing or repairing the damaged parts. Sometimes though the damage is caused by another issue with the system.

A Risk-Based Approach to Spares Management 

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