5.6 Relability Performance Measurement

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.

We know that reliability has value to your business, but many of us with technical backgrounds struggle to present a good business case to decision-makers. We are very often held back by budget constraints and we are not in a position to make decisions involving financial risk-taking. Most of us don't have a business background, nor do we speak "finance". It is a whole different language than maintenance and reliability, yet we all want the same things for our business. This presentation will give you some ideas on what you will need to determine in order to show what reliability is worth, and how to present that to decision-makers.

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 

"Reliability Engineering is an established science with rigorous concepts involving mathematical and statistical methods and those can often appear daunting for some Maintenance or Risk Practitioners. It is the role of the Reliability Engineer to master, explain and apply those concepts as well as work with peers to make the correct decision(s) regarding the maintenance of operating assets or future design capabilities. Those decisions are crucial especially when it comes to the safety of frontline workers, capital investments or the preservation of the environment. This presentation essentially defines the role of the Reliability Engineer mainly in an Owner/Operator environment but also helps non-Reliability practitioners understand some of the basic tools used in this field.The term “Reliability” is often generalized and not fully understood so this presentation helps clarify its definition and intent. Misinterpretation or incorrect calculations involving equipment life characteristics such as mean time to failure, bath tub curves or failure probabilities just to name few are covered in the presentation. Also explained, will be some of the most commonly used concepts in Reliability Engineering calculations as well as potential pitfalls encountered such as oversimplification, applying incorrect analytical approaches or mixing terms such as Availability and Reliability. The presentation will also define the “true” and “value-added” role of Reliability Engineering in an industrial environment and how it productively interfaces with other teams involving Maintenance Engineering, Risk Management or Spare Parts Management.       Originally presented at  AB Chapter Online Symposium (Part 2 of 7)    Presented MainTrain 2020  09/15/2020

New technologies regularly enter the world of asset management (AM), often leveraging new inventions, which, in turn, are driven or supported by other advances such as computing power and big data handling. Such is the case for Motion Amplification, a new technology impacting crucial aspects of vibration analysis, machinery and structural troubleshooting, root cause analysis, and communications. This presentation will outline which areas of AM are impacted so we can have a road map facilitating the integration of the new tool into a global strategy and get an overall picture of the impacts it will have. We’ll also provide a brief technical introduction and some practical illustrations.Originally presented at MainTrain 2020

The asset hierarchy is often thought of as a way to organize assets so they’re easy to find in the CMMS. While a well-structured asset hierarchy does make work management easier, it’s much more than that. The asset hierarchy, when well conceived and utilized, will ensure the right reliability and costing data can be extracted from the CMMS. This enables more than just micro improvements in reliability involving a single asset; instead, it enables macro views of reliability and cost trends across the entire organization. Setting up an asset hierarchy to support these types of activities requires forethought and planning, but by following some guidelines, any organization can be set up for success. First, the asset hierarchy must have a standard that identifies how all assets will be categorized and described, and the specific data required for each asset class. This is vital, as not all assets warrant the collection of specific data, reducing the burden of the setting of the hierarchy. As assets are categorized, the failure code library can be developed and linked to the specific asset classes. This ensures only relevant failure codes are displayed for the assets, improving the adoption of failure data collection. With the asset hierarchy built and relevant failure data collected, trends can be established across asset classes, similar processes, etc. The trends enable reliability improvements to be implemented across larger swaths of assets, providing rapid improvements in reliability. This presentation will provide guidance in how to develop an effective asset hierarchy based on ISO 14224, how to implement the changes in the CMMS, and finally how to leverage the asset hierarchy to identify macro trends. Without a proper asset hierarchy, any organization will struggle to get meaningful and actionable data from their CMMS to drive reliability.