05 Maintenance & Reliability Engineering

The Global Forum on Maintenance & Asset Management and highlights of the second edition of it’s recently published Maintenance Framework, including the project to develop the document and differences from the first edition.

L'objectif de ce webinaire, sera un partage d'un récent mandat chez un client minier ou ils ont fait l'évaluation de la maturité de la gestion de maintenance. Le webinaire couvrira également les découvertes et nos actions pour l'améliorer.Agenda de la présentation:- Contexte opérationnel- Évaluation (analyse de données et temps d'outil)- Découvertes- Action en partenariat- Résultats préliminaires- Les actions prochaines pour fiabiliser les opérations

As maintenance and reliability professionals we often get caught in the detail of appropriate reliability techniques and can sometimes lose sight of the bigger picture and organizational goals.  This presentation will provide an indication of the place of reliability engineering in asset management and business planning.  We will also touch on some of the tools in the toolbox of reliability engineers and maintenance professionals such as asset modelling (deterioration and failure modelling), risk assessment, prioritization and inspection.  The presentation will indicate how these tools relate to business planning and when is appropriate to use during an asset lifecycle.  The presentation will show examples of application through case studies in treatment plants, infrastructure, and building facilities and highlight what we have found to be some of the key issues and challenges and how they can be overcome.  To conclude we will highlight some of the key lessons learned from application of these tools and approaches.

Maintenance strategies and practices have evolved. From reactive, preventative, predictive to proactive maintenance – all or parts of them continue to be used in some industries and plants but not all of them work. In fact, some of them, like predictive maintenance failed. CMMS systems have greatly contributed to the evolution of maintenance. It gave us better order & structure. It contains many records including items like a machine's assets condition monitoring (CM) history. However, in most cases, what’s not in it is the initial installation or commissioning report. As an industry, we often overlook the importance of the installation and commissioning report data. In many cases, it’s assumed that the installation was done correctly and that’s why predictive maintenance failed. All the CM data was incorrect because we were working off the wrong benchmark. In our experience, we know that there are still many companies who have not subscribed fully to the precision maintenance philosophy, which is: installing, maintaining and working to a recognized standard. One of the reasons for this was that there wasn’t one until the ANSI Shaft Alignment and Machinery Installation Standard came out in 2017. New technologies like motion amplification cameras are now game changers in revealing how important the machinery installation and precision maintenance as a whole is. Seeing and watching a video broken down at that frequency, showing a machine moving and vibrating excessively allows us to now pinpoint exactly what the issue is. Instead of only a select few who know specific CM technologies, this is a visual that everybody can see and understand that the machine has not been installed correctly. The introduction of the Industrial Internet of Things (IIoT) is a further step in the evolution of maintenance. In order for us to keep our machine assets running for their full life cycles, the initial precision installation, commissioning and continued precision maintenance practices must be done including the report the machines history before you can begin to collect data for CM analysis. If we don't do this, our maintenance strategy will fail like it did for predictive maintenance. 

When a defect occurs in a physical asset, it’s often not immediately detectable by operators. In fact, in some cases the defects are not visible to the naked eye. However, from the moment a defect occurs until it is found, there is a risk that the defect will grow in severity, and possibly transition into a failure, resulting in reduced or halted production. At a municipal transit company, the Nondestructive Testing (NDT) team uses specialized equipment to inspect the train tracks and identify the location and severity of any defects. Due to the limited hours during which the team can perform their work, the whole subway system can only be tested once per year. Using their data on train tracks and found defects, we investigate efficient ways to use the NDT team’s fixed resources in order to improve the reliability of the train track system.

The cost of an error depends on where you catch it. In DfR programs, the design phase is a good place to start.          Interview with ; Marie Getsug, PMP, CAMA, CMRP, CRL, CPIP | Jacobs | Program Manager            This article was originally published on MachineDesign.com.

Often organisations are conflicted on where to direct capital and resources, given constant request and competing priorities requiring resources and money, many initiatives and maintenance activities are deferred or cancelled all together in order to meet the constraints of budget and available capability expectations. How do you know if the allocation of your money/capital and resources are being directed to the areas of greatest need and also delivering greatest value? How do you evaluate value? A deep dive into capital and resource allocation may uncover wasted effort where valuable resources have been allocated to initiatives and activities that are not as value add as some activity that has been deferred or cancelled. These decisions are often made with little or no consideration of the vast amount of information being held within the company’s CMMS. Every initiative and job can be justified but is it the best use of time and money? What data do you use to decide where best to allocate your valuable money and resources? I will share the principles behind real life examples of where failure data has been sorted to demonstrate both micro and macro impacts on a business bottom line, allowing for managers to make better decisions on resource allocation, and decisions that will deliver high value outcomes for the business. These decisions were made using information freely available, but largely ignored, within the CMMS. The information was sorted into failure modes/types and overall cost of unreliability and presented in the form of a pareto chart. When organised in this simple manner the data clearly identifies areas requiring attention that have significant impact on the business performance, and also leading to reduced wasted effort on jobs that are less important. This approach also removes some of the countless debate over the order of priority when people within the organisation feel they are competing for the limited resources up for grabs, instead allowing people to focus on what is best for the overall organisation and not just their patch.

2020 was a very different and transformative year for all industries due to the pandemic and mining was not an exception; one of the important challenges for the Kinross’ corporate team was to provide support from the headquarters in Toronto without the capability of travelling to sites; among the different corporate functions, the Maintenance team is responsible for leading the identification; application and effective implementation of practices aiming to the continuous improvement and evolution of Maintenance in order to better position the function globally towards asset management optimization Kinross Gold is a senior gold mining company Headquartered in Toronto with a diverse portfolio of mines and projects in the United States, Brazil, Chile, Ghana, Mauritania, and Russia. The proposed paper and related presentation will explain how the Corporate Maintenance group had to adapt and evolve in order to provide meaningful support to sites in spite of the impossibility of traveling and work side by side with our site based counterparts; in particular the presentation will focus on describing: - Initial failures and struggles in the adaptation process - New streams of support that were developed to overcome the mobilization challenge - How the maintenance corporate changed for good due to the new way of working - The overall performance of the maintenance function globally - The challenges that remains and new barriers expected for the future The proposed paper is simply an effort to share our experiences with the hope that the Asset Management community globally considers these lessons and benefit from them on their own quest for excellence

ARMS Reliability was engaged by a client to vet its design for an extension of its oil facility with special focus on the diluent recovery unit performance. The facility had capacity to load and ship approximately 100,000 barrels of oil per day and were looking to increase the capacity to 120,000 bbls/d with the addition of a DRU. In order to meet capacity goals, ARMS Reliability assisted in building a Reliability Block Diagram (RBD) remotely using failure data from previous RBDs equipment types with similar operating contexts and assigning failure models using RBDs from other sites and the ARMS Component Strategy Library. Since design choices were not finalized at the time of build, multiple scenarios were simulated using existing VRU packages from other sites and data on reliability performance of previously modeled equipment at other terminals.This case-study presentation will discuss how a Reliability Block Diagram helped our clients:• understand expected performance of current and potential design choices• enable cost-benefit-analysis to determine what changes need to be incorporated for top contributors• target optimized strategies against top contributors to availability and capacity losses• quantify the best-case impact of process cleaning activities to inform their cleaning intervals and condition monitoring methods

Reliability Engineering uses a modelling approach specifically known as Reliability Block Diagrams (RBD) to asses all the characteristics of an asset during its life. The output of this exercise is a dynamic model illustrating different decision-making elements of the asset being studied. This includes economical aspects such as operating costs, spare part management, expected failures and other maintenance outcomes over time. This information allows and operator to correctly budget costs, logistics or labour requirements over the life of the asset. The model also allows the designer to estimate the production output of the asset over time and have a more realistic view of the design output. When it comes to preventive maintenance or redundancy (i.e. adding extra equipment), the model can be altered to visualize the expected output and incremental economical benefits or lack of there off, leading to better decision in terms of capital spending. The author will illustrate the study of a centrifugal pump and help the audience visualize all the above-mentioned aspects.Originally presented at MainTrain 2021