5.5 Reliability Sustainment

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.

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.

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 

Focused on the HR aspect of maintenance management, this presentation will provide insight into how “asset maintenance management is about managing the people who manage assets.”We’ll look at how individuals’ confidence, competence, and validation of skills play a significant role in the overall reliability and costs of the assets we manage.

Is safety a concern for your organization? What about reliability? How is it that aviation is able to ensure such safe operations? Yes, that industry has trained pilots with lots of experience, but that alone is not enough. Doctors are well trained, as are our skilled tradespeople, yet mistakes are common. So how do we overcome those mistakes? The Checklist Manifesto shows how checklists have, and can, make such a significant difference in the aviation, medical, and construction fields. Why not learn from these industries and apply that same methodology to our maintenance programs? Developing a checklist is not as simple as throwing a bunch of steps on a piece of paper and handing it to our skilled tradespeople. Checklists have to be simple and address the right issues. We also have to overcome some stigma to get our tradespeople to use them. 

This article is to address the difference in conditional probability of failure patterns, and the impact on how best to maintain assets based upon those differences.

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.