3.06 Reliability Engineering

Many maintenance & 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 integrates with our current troubleshooting efforts and drives bottom line business improvement. We can make our workplaces safer by reducing the number of unexpected failures while improving our business performance through increasing our facility’s throughput and reducing the money spent on repairs – straight to the bottom line.This presentation will cover the steps to implementing a data-driven Root Cause Analysis (RCA) program and building the business case for change, suggest an approach to implementing root cause analysis thinking, establishing a work culture that is focused on failure prevention, quantifying the cost savings, integrating RCA with existing maintenance program approaches, and realizing the business value.

The world your business operates in is constantly changing. Supply chain disruptions, market pressures, budget constraints, regulatory changes, extreme weather, new technology, challenges retaining experienced engineers and maintenance technicians, and more can impact the operating context of your assets and equipment.Minimizing risk, and maintaining high performing assets in the face of constant change often requires organizations to accelerate the rate at which risk and criticality are re-assessed, maintenance strategies updated, people and systems enabled, and new maintenance plans put into practice; connecting the dots in a closed loop process.In this webinar, we will share practical approaches to help you connect the dots between strategy and execution, and ensure the investments you make in your reliability based maintenance program are put into action and deliver the intended results as the broader organization and asset management context continues to change.

An integrated and aligned link between asset management and maintenance management practices is a key value driver for organizations. This session will focus on defining, understanding, and quantifying the value from cascading business improvement initiatives, how asset management, maintenance and reliability decisions at all levels contribute to organizational success and provide takeaways for calculating the impact on organizational performance measures.How does a corporate Asset Management program translate into processes, practices and procedures on the shop floor?Key Take AwaysAsset lifecycle and relationship between AM and M&R (end to end holistic asset management with the emphasis on contributions from lifecycle delivery)How M&R decisions and front-line improvement initiatives contribute to achieving overall organizational goals and objectives.  How is goal alignment to AM program established, managed, and reinforced at the M&R execution level?Measuring and moving the dial on M&R performance indicators and how they contribute to overall organizational key performance indicatorslinks between key asset availability, reliability, maintainability, uptime to SHEER – safety, health, environment, economics, regulatory; PESTLE, ESG)example of increased availability leading to more uptime and thus increased economics; include examples of how improved planning & scheduling can lead to increased availability, how PMO / RCM contribute to increased PdM and thus increased uptime (more online condition-based monitoring while assets are running)example of improved reliability and maintenance practices contributing to easier demonstration and reporting of regulatory complianceTCO, LLC

Cameco Corp. has recently deployed approximately 1,500 wireless asset condition monitoring sensors across four of its operations. This presentation will explore all aspects of this project, from initial identification of business pain, all the way through to deployment and management of the system. Condition-based monitoring of rotating assets typically involves a route that is executed at a fixed interval to collect asset condition data. This data can include vibration, temperature, acoustic emissions, and others. This data is then downloaded into software and analyzed for faults and trends. This method has many shortcomings that can be solved with remote sensing technology. This presentation will take you through Cameco’s journey of identifying the limitations of traditional data collection and why an alternative was investigated. Some of the key topics will include problems and inefficiencies with the current system, methodology used to determine which sensor company to partner with, potential cost savings and benefits, deployment strategy and execution, and some screen captures of actual asset detections. Finally, we will conclude with lessons learned and benefits realized from deploying a sensor solution.

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.

We all love to solution and check off that win for the team, but moving too quickly may result in introducing new problems or even amplifying previously smaller issues. The latter is common particularly in technology implementation where the hope is for efficiency improvement, but the results sometimes don’t meet the desired outcome. Although root cause analysis was informally practiced at the SMCDSB, the implementation of a formal program using a process approach to problem solve, define requirements, and solution has strengthened troubleshooting and preventing future problems. By taking a very focused stance on identifying the problem or need clearly and leveraging the Kepner-Tregoe Analytical Troubleshooting method, there is improved clarity, definition, and logic in how the analysis completed. I wish to share the journey of RCA program implementation for the SMCDSB with examples and successes we've achieved.

Organizations are observing the change in the maintenance landscape with the use of data-driven analytics for decision-making. Underpinning these analytics is Machine Learning. The algorithms form the model that ingests data to represent the system and predict its future state. While this method has found rapid applicability in other sectors, the field of Reliability & Maintenance Engineering is still exploring ways to adapt this idea to its conventional Asset Management programs. The objective of this paper is to explain the predictive power of machine learning by wrapping it around a prevalent reliability tool: the P-F Curve. Initially proposed in the Reliability-Centered Maintenance (RCM) framework, the P-F Curve is ubiquitous and the practitioners understand its simple and elegant description of the failure behavior. In spite of its understanding, the use of the P-F Curve has been minimal in everyday analysis to estimate when and how soon the failure will occur. Predictive Maintenance (PdM) Tools such as Vibration Analysis, Ultrasound, and Thermography have made the P-F Curve more accessible. Adding Machine Learning to these PdM tools, with a real-time data stream, will amplify the value of this analysis with better detection of Potential Failure (Pf) and forewarning of Functional Failure (Ff). Having real-time data and a real-time P-F Curve plot will enable the users to capture the changing conditions. We define this new curve as the Dynamic P-F Curve™. Dynamic P-F Curve™ Machine Learning models will estimate the time available (P- F Interval) for the maintenance team to respond to an asset before catastrophic failure. This interval will change if the asset experiences an external force causing it to wear out sooner. Thus, a dynamic curve makes the maintenance plan itself changeable and agile, improving the plan's efficiency. The final part of the presentation will showcase an example of how a Dynamic P-F Curve™ is calculated and represented by using an open-source data set. The resulting change in the maintenance plan actions is also prescribed to fully explain this idea, concluding with the list of use cases.    

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.