5.2 Reliability Modelling

Reliability and maintenance teams in all manufacturing plants have a common goal; that is, to plan and execute initiatives that will help sustain the inherent reliability of the physical assets and increase the availability of the plant. Productivity and profitability of the manufacturing plant and overall organization are highly dependent on the reliability and availability of the plant. A thorough understanding of the importance of reliability has made the top management of major corporations invest in reliability and physical asset management. When the top management invests in reliability, they typically set the corporate strategy and directions for reliability through a road map for all the manufacturing plants that operate under the corporation. When the commitment or support from the top management is available for reliability initiatives, the onus is now on the individual manufacturing plant to develop a reliability plan that aligns with the corporate strategy and/or reliability road map and the current needs of the plant. The reliability and maintenance teams typically build all the strategic, tactical, and operational reliability plans that have initiatives that would make the biggest impact on continuous improvement in reliability and bring the desired benefits for the site. This presentation will explain the systems thinking approach, along with the seven golden rules and seven key factors, that will help reliability and maintenance teams to build an effective reliability plan. In addition, this presentation will also address the top three challenges in building a reliability plan and how to overcome those challenges through two case studies. The first case study is about developing a three-year reliability plan, and the second case study is about developing an annual reliability plan. Both the case studies will explain the application of the systems thinking approach, seven golden rules, seven key factors, and top three challenges that were dealt with and solved.

3D printing has important advantages over traditional manufacturing processes. However, as it is a relatively new class of manufacturing technologies, problems of reliability and parameter optimization remain largely unresolved. Our work focuses on addressing some of these issues through in-situ monitoring and closed-loop control, using machine learning as a tool for the endeavour. The idea is to analyze the condition of the process by predicting key characteristics of the final product, and then to use this analysis for adjusting process parameters on the fly. We imagine that this framework of predictive analysis leading to closed-loop control can be extended to a variety of applications outside of 3D printing. In a more general maintenance scenario, sensor readings can be used to assess the condition of equipment and to predict the condition at a future time. This information can then be used to determine appropriate maintenance activities, such as triggering preventive maintenance, scaling back on the intensity of use, and ordering replacement parts, as well as the timing of these events. For our case study in 3D printing, we have implemented in-situ monitoring hardware for a fused deposition modelling (FDM) printer and have constructed a dataset for modelling the process. The dataset consists of in-situ observations (photographs) and select mechanical property measurements for 359 fabricated parts. With this data, we demonstrate the ability of machine learning methods to capture the complex dynamics of a 3D printing process. Specifically, we train a neural network-based model which is able to predict mechanical properties of the final product based on in-situ photographs as well as parameter information. Predictions made by these models can then be used to assess the quality of products as they are being fabricated, thereby making it possible to correct errors or to improve the expected outcome through online parameter adjustments.

This paper introduces a Reliability Engineering process also known as Barringer Process Reliability (or BPR). It is a simple yet powerful method for senior managers to assess and quantify the performance of their production plant with simple graphics and a few key performance numbers.  It is “my factory on an A4 page” appropriate for busy managers in an organization.  The underlying mathematical concept for BPR is the Weibull statistical distribution assuming that daily outputs in production plants all follow a Weibull statistical distribution. BPR is not intended to go into the weeds of the losses or low production root causes but rather remains at a high level. However, it is still able to benchmark, quantify production losses as well as opportunities and measure quite precisely, the variability in production outputs. The presenter who is well versed in this technique, will briefly introduce the concept followed by a variety of applications in industrial environments.

An Asset Health Index or AHI refers to analysis performed using various asset data to determine the state or condition of the asset. AHI can be used to better assess asset condition, used and useful life, progression toward potential failure, and failure probability. Further, using AHI can also enable the development of optimized maintenance and replacement strategies for assets using a set of objective criteria to assess the true health of the asset. However, entities vary widely in whether they develop Asset Health Indexes (AHIs) for their key assets. For those that do, there are marked differences in the level of rigour and sophistication employed in developing and applying AHIs for effective asset management decision-making. AHI calculations involve identifying and collecting data which may include a review of core asset attributes such as manufacturer, inspection data including field observations, destructive and/or non-destructive test data, maintenance data including historical records, operational records, and asset failure/refurbishment data. In other words, some are core inventory data, some work records, and some inspections or tests. This presentation will go through how to make the best use of asset SMEs and how you can start to develop useful AHIs from what you already know/have. Technically, the process begins with identifying the most critical assets and determining which can best benefit from AHI formulation development. The next steps are used to develop proposed condition factors (CF) and weighting factors (WF) that provide insight into the condition of the assets. Finally, CFs and WFs are used to develop a mathematical algorithm or formulas for the Health Index. We will also discuss how AHI can be used to develop asset management and maintenance strategies – the whole point of the data and analysis in the first place.

The “Power Panel” is comprised of reliability and asset management professionals in the electricity industry and will speak to their experiences within the industry that can be applied to outside of an electricity utility. They will cover a roadmap to ISO55001:2014 certification, asset maintenance strategies and mitigation of risks for capital assets.  Moderator:Daniel Gent, Director of Analytics, Canadian Electricity AssociationDan leads CEA’s Reliability Programs, the only national industry program that leverages reliability data analytics on behalf of member and participating utilities. Dan is a Certified Business Analyst Professional with Certification in Business Data Analytics with over 10 years of experience in the electricity sector working in reliability and asset management, and over 15 years in the telecom industry working in business intelligence support. Rounding out his work at CEA, he also oversees several other committees from Technology to Data Strategy and Finance, Tax and Accounting.Panelists: Erin MacNeil, P.Eng, Asset Management Operations Manager, Nova Scotia PowerErin MacNeil is a Mechanical Engineer with experience in both the Utility and Alberta Oilsands industries.  As the Asset Management Operations Manager for Nova Scotia Power, Erin leads a team which designs and administers processes, programs and technologies which enable, sustain and optimize asset-centric maintenance strategies, and advance initiatives with respect to operations and maintenance excellence.  Erin has worked with NS Power's Asset Management Office for 8 years, and is a Professional Engineer (Mechanical) as well as having attained IAM Certification.  Nova Scotia Power’s AM team has been the recipient of a number of awards including a 2019 Game Changer Award (Connected Plant Conference), and a 2018 GE Digital Innovator Award.  Kyle Smith, Supervisor, Maintenance and Reliability, Hydro OttawaKyle Smith, P. Eng. oversees a team of engineers dedicated to keeping Hydro Ottawa’s distribution system assets functioning, reliable, and cost effective for customers. He leads the development and operation of routine maintenance and inspection programs, as well as processes and initiatives to improve system reliability. Prior to joining Hydro Ottawa in 2019, he gained experience in various technical roles with Nova Scotia Power, Inc. and held leadership positions with both the Canadian Electricity Association and Engineers Canada. Kyle holds degrees in Mechanical Engineering and Engineering Mathematics from Dalhousie University.Ehsan Abbasi, Ph.D., P.Eng, SMIEEE, Senior Reliability Engineer, Lifecycle Maintenance Engineering, AltaLinkEhsan has been with AltaLink as Senior Reliability Engineer – Lifecycle Maintenance Engineering since 2015. He has been active in electrical power industry and academia for more than 12 years with experience on reliability assessment of power transmission and distribution networks, power assets lifecycle, reliability centered maintenance, risk based asset management, condition monitoring along with power system SCADA and IED management solutions. Ehsan received B.Sc. from Amirkabir University of Technology and M.Sc. in Energy Systems Engineering from Sharif University of Technology, Tehran, Iran in 2005 and 2007 respectively. He received M.Sc. and Ph.D. in Electrical and Computer from University of Calgary, AB, Canada in 2010 and 2018 respectively. He joined IEEE in 2009 and is currently a Senior Member. He has been a committee member of CEA Transmission Consultative Committee on Outage Statistics (T-CCOS) since 2015.

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.

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 

Not knowing what you do not know can be very dangerous for an organization. With unfortunate events that led to injuries at competitor’s facilities, Skeena Bioenergy activated a safety review of all equipment using bowtie analysis and a risk matrix. Bowtie analysis identifies causes and preventative action to stop a defined event from occurring. Then, looks at loss prevention actions to prevent disastrous consequences that stem for the described event. The risk matrix is a chart that has frequency of occurrence on the vertical plain and the consequence of severity on the horizontal plane. When combined, gives a risk level number, colour coded, that identifies levels of acceptable and unacceptable risk. This application was successful in identifying that the design of the Cooler, one part of the process, does prevent fires and explosions. Further fire control measures identified will be added; 1. to improve containment of a fire so it remains in the Cooler and 2. to prevent a fire event from cascading into an explosion. These continuous improvements in the Cooler reduce the risk level to 3, Skeena Bioenergy’s acceptable level of risk. This abstract demonstrates the application and findings of applying bowtie analysis and a risk matrix to a piece of equipment, the basis of good risk management.