4.0 Tools and Tactics General

One major challenge at the operate and maintain phase of an asset is achieving and sustaining the forecasted availability and reliability as intended at the project delivery phase. Many problems arise—equipment failures, underperformance, high costs—that are caused by numerous issues. The resolution demands thorough understanding of the causes of the issues, which we usually attempt to achieve through RCA methodologies. I've experienced many repeated failures even when RCAs have been conducted, due, mainly, to most of the RCAs focusing attention on solutions to the problem outcomes with limited focus on the human and system causes that drive the outcomes. The Causal Learning Approach brings in the understanding of these other causes that ensure effective and sustainable solutions development. There are three levels of causes: the physical outcomes; the human causes; and the system causes. The Causal Learning Approach also focuses on causal reasoning instead of defensive and solution reasoning. This presentation will provide the understanding of these causes and the three key elements of this approach: discovery, learning, and solution generation.

Many maintenance and 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 integrate with our current troubleshooting efforts and drives bottom-line business improvement. We can make our workplaces safer by reducing the number of unexpected failures, which will then result in improving our business performance, increasing our facility’s throughput and reducing the money spent on repairs – straight to the bottom line.

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.

“Sustainability through reliability” — presented at the 2015 MainTrain Conference — focused on the rapid growth of passenger flow at Toronto Pearson Airport and how, due to this growth, we were experiencing a high number of plumbing drainage failures. We carried out an RCA on our system and came up with changes in how we would prevent drainage failures. The changes we made dealt with our plumbing design standards; food and beverage tenant fats; oil and organics recovery system; lease agreements; and maintenance practices. However, that was only the starting point. In this presentation, we’ll discuss RCA conducted, the failures experienced, and the enhancements and improvements we made to make our system more reliable.

Reliability engineers in industry are often thrown into the position with very little knowledge about what they’re supposed to do. Or, sometimes, the organization isn’t set up to take advantage of what a reliability engineer can do. Sometimes these engineers have the theoretical knowledge from college but never learned what will be used in the real world. This presentation will address all the basics a new reliability engineer must know. We’ll focus on managing existing equipment and provide an overview of the reliability engineer’s role in new equipment procurement and design. We’ve found that the role of a reliability engineer is not often clear; in fact, many reliability engineers end up doing a lot of work not always related to what they should do.

With all the talk about big data and the IIoT, many are asking how can we use this in maintenance? The IIoT enables us to put sensors in any location where we might want to collect and analyze equipment condition and performance data. There are companies that offer predictive maintenance services, and some companies do this for themselves, in-house. Typically, it’s the larger companies that can afford this, but democratization has meant this has become available to a much broader market. But there are hurdles to taking advantage of this sort of continuous monitoring program, even for your most critical equipment. One, it’s expensive, whether you do it in-house or outsource. And two, there are data bottlenecks. Condition monitoring data comes is huge volumes and it’s all time-sensitive. Even if you can afford it, you need a data handling network with a lot of capacity. In this workshop, we’ll present a viable technical solution to the data bottleneck problem — based on a solution already proven in financial securities markets — that opens up these possibilities in the realm of plant continuous condition monitoring.

Asset condition management (ACM) teaches on-condition monitoring for any business with high-capital assets looking to harness machine learning to avoid unexpected failures and control rising equipment maintenance costs. Many businesses are already using continuous condition monitoring technologies like IoT-connected devices. However, beyond simple threshold alerts from condition sensors, extracting real value from the data generated by these sensors for true predictive monitoring requires expert analysis and interpretation. To generate actionable results from condition sensor data, these experts also apply knowledge about the asset’s operation. This limits the value that IoT-enabled ACM can provide to the business. By taking the next step and using advanced algorithms and machine learning to automatically extract real-time insights that drive action, we can now achieve the full potential of ACM. Modern, cognitive online ACM takes data from multiple and varied sources, combines it, and uses AI and machine learning techniques to anticipate equipment failure before it happens. Many reliability professionals recognize the potential of IoT, machine learning, and AI, and are trying to learn these technologies. However, the available training is complex and assumes learners have a background in data science and computer programming. This workshop will provide a beginners’ level understanding of terminology, basic concepts, and techniques to determine how and where you can apply AI in your facilities for meaningful ACM.

Electrical maintenance surveillance device (EMSD) technologies refer to condition-based monitoring technologies and equipment used every day to inspect electrical distribution assets. These surveillance and inspection systems determine the condition of the individual asset or system being inspected and include, but are not limited to, infrared thermography, airborne ultrasound, motor current analysis, partial discharge testing, corona cameras, and visual inspections. The implementation challenge is that the inspection and surveillance equipment used yield their most valuable results when inspecting electrical distribution equipment that’s operating under full load conditions. Doing so while working within the confines of CSA Z462 guidelines can be challenging when the equipment is both of danger to maintenance personnel and of value to the process they’re powering. The surveillance equipment implemented normally requires direct access or direct line of sight to the energized components inside the electrical system. This requires panels to be open, which is extremely dangerous. In this workshop, we’ll show you how EMSD technologies maintain the energized compartment’s closed and guarded condition, ensuring that personnel are not endangered. You’ll learn how the design allows the required test equipment to be used safely at any time, especially when equipment is under full load conditions. We’ll also present a case study from an Ontario beverage bottling facility, demonstrating how these devices can be easily retrofitted on existing electrical distribution equipment to become the nexus for an electrical infrastructure reliability program.

This webcast will highlight Potash’s extensive implementation of mobile devices to support its business processes. Aligned Mobile Applications are now in use or being implemented at Potash’s Allan, Augusta, Aurora, Geismar, Lanigan, Lima, Rocanville & Trinidad sites. Potash has partnered with Viziya to develop a single integrated mobile app to meet its maintenance and supply chain business requirements, and Postash continues to deploy ‘out of the box’ apps from its Enterprise Resource Planning (ERP) system. Vendor mobile devices are now a commodity which provide a cost effective way to drive efficiencies. Importantly, apps are available across various platforms; hardware choices do not drive decision making when it comes to selecting the best tools for our business. If you are thinking about implementing a shift to mobile devices on the front lines, this will be a great opportunity to learn from the Potash experience.   Reviewer's comments;  Excellent presentation outlining how Potash Corporation of Saskatchewan has deployed a combination of technologies, enabled on mobile devices (tablets / laptops) integrated fully with their EAM and KPI monitoring systems. Author provides an overview of the situation "before" deployment, through the deployment (which took place over several years) to the "after" or current state. If you want to know what can be done and has been done, this is pretty leading edge stuff and well worth the time to listen.

Jeffboat is a company with a long history.  Originally named the Howard Steamboat Company, Jeffboat is America’s largest inland ship builder and has been manufacturing ships for over 100 years.  Jeffboat has built such famous ships as the Mississippi Queen, the General Jackson showboat and the Casino Aztar riverboat casino. Like most manufacturing firms, Jeffboat has an enormous amount of equipment stretched out over a shipyard that is over a mile in length that is needed to make its boats.  Also like many old-line manufacturing firms, Jeffboat has both equipment and employees who have been there for several decades. Overall, because of the size of the shipyard and age of the equipment, Jeffboat’s maintenance was used to working in reactive mode.  There was no CMMS software in place and equipment was put into numerous Excel spreadsheets.  In addition, it was often hit or miss whether the right parts were in the stores room and finding the right equipment often took maintenance technicians a significant amount of time.  There was no Scheduler/Planner and maintenance procedures were done informally and based on need at that particular moment.When implementing a maintenance management strategy, a critical component is the resistance to change. Whether it is the introduction of new software or a complete overhaul of the maintenance function, the process of change represents disruptive technology (Christenson, …). According to Christenson, most changes are really improvements on something old and the old paradigms can be used. However, there are changes that organizations need to make that disrupt the dominant paradigm, rather than sustaining it. These are disruptive technologies and make the old things less important or obsolete. The problem with these disruptive changes is that people are still applying the old paradigms to the new realities. They are trying, in a sense, to understand the car as nothing more than a carriage without horses.