Overcome the Challenges of Implementing Maintenance 4.0
Digital transformation and the power of big data are fuelling a manufacturing revolution. To remain competitive, operations and IT must work together to transform data into meaningful, actionable insights that create value and solve problems for customers.
BY: Josh Flemming
The arrival of Industry 4.0, which encourages the digitalization of manufacturing, has created a subset in the support of machine optimization. Maintenance 4.0 involves complete visualization and integration of the industrial segment by enabling emerging technologies that allow companies to maximize plant efficiency, reducing or even eliminating unplanned downtime.
Understanding Maintenance 4.0
A key component of Maintenance 4.0 is predictive maintenance (PdM). This approach to monitoring machine health uses connected devices to collect data on a variety of assets. Analysts then bring that data together to deliver valuable, actionable insights. This approach delivers cost savings over routine or time-based preventive maintenance since tasks are performed only when necessary.
In today’s competitive market, Maintenance 4.0 is giving companies a leading edge through improved efficiencies. However, many plants struggle to implement Maintenance 4.0 strategies due to perceived obstacles. Overcoming the challenges outlined below allows companies to optimize machine performance and increase productivity.
Limited Operational Budgets
In the past, the cost of connected devices to monitor rotating equipment was viewed as a capital expenditure. However, advancements in technology are making PdM programs more economical to implement. Procurement of these connected devices can be shifted into an operations or maintenance budget, providing an easier point of entry.
Use of connected devices gives any company with an Internet connection the ability to access and analyze its machine condition and operating data anytime and anywhere through the cloud. Maintenance 4.0 offers a scalable approach, allowing critical assets to be monitored in the first phase while other machines are added as budgets allow.
Manufacturing facilities often support a variety of technologies, including ethernet, cabled networks, wireless, or even industry-related communication protocols. This offers flexibility for operators to collect machine data using mobile devices, providing an easy, affordable solution for companies to implement.
Another concern is whether OEM specific or proprietary monitoring equipment can support machines from a variety of different manufacturers. Using non-proprietary connected devices gives operators the ability to focus on overall machine health without limiting the scope of what can be monitored. Additionally, the next generation of maintenance workers may not have the same knowledge and experience as the existing workforce. A PdM program utilizing connected devices supported by remote diagnostics reduces the time and cost of training and retaining increasingly scarce and expensive maintenance and diagnostic skill sets.
Run-to-failure maintenance requires minimal planning since maintenance does not need to be scheduled in advance. However, this type of approach is both unpredictable and inconsistent. This strategy can also increase production and breakdown costs, in addition to inventory and labour outlays associated with performing the maintenance.
This business model can put companies in a dangerous position in today’s competitive marketplace. Companies that fail to anticipate machine breakdowns will ultimately run into supply chain issues. If a piece of equipment fails and replacement parts are not readily available, it can create production delays and contribute to significant profit loss.
Embracing a more proactive approach with PdM allows companies to create a planned maintenance schedule while eliminating unanticipated machine downtime or failure.
Optimizing Big Data
Digitalization and technology developments are quickly becoming key drivers in the manufacturing industry. The Internet of Things (IoT) is connecting machines in conjunction with big data, which offers new insights into machine performance and opportunities to drive efficiencies.
Maintenance 4.0 moves beyond the collection of data by applying predictive and prescriptive analytics. Knowing how to interpret data and when to take action helps operators increase machine reliability to improve both uptime and productivity.
Having the ability to cross-functionally compare vibration, temperature, and oil analysis allows operators to get an overall snapshot of machine health. These analytics can deliver actionable information for quick and strategic decision-making. Connecting, collecting, and correlating data offers a new way for people to interface with machines to increase efficiency and productivity.
Automating Maintenance Tasks
Approximately half of rotating equipment failures are due to improper lubrication management. Manual-scheduled lubrication management is often the cause of over- or under-lubrication. Not using the right type or amount, or not using the lubricant at the right time are three of the primary reasons for lubrication-related failure.
Automatic lubrication increases bearing, gear, and chain life by applying small, measured amounts of lubricant consistently while the machine is operating. This virtually eliminates the need for manual lubrication. It also reduces lost production time, since the machine no longer needs to be shut down and prevents accidents that can occur during manual lubrication. Waste, product contamination, and cleaning issues are also substantially reduced.
Increased competition and the rise of Industry 4.0 are influencing companies to consider outsourcing their PdM programs. This allows manufacturers to focus on their core capabilities while leveraging the knowledge and expertise of trained experts in remote diagnostic centers. A well-designed rotating equipment maintenance program should not only prevent and identify pending machine failures, but also have the ability to eliminate their occurrence or reoccurrence. It should also provide insights to operations and maintenance, offering a better interpretation of analytics and improved lubrication for a more performance-based approach.
Future of Maintenance 4.0
Maintenance 4.0 will continue to evolve and generate more precise, quicker diagnostics, allowing companies to take action at an even faster pace. Virtual reality will also emerge as an enhanced form of communication between operators and engineers. Devices such as smart glasses allow engineers to create a digital representation of faults and analyze the situation in much greater detail.
Advancements in the decision-making capabilities of machines, especially robots, will continue, resulting in benefits such as carrying out work in dangerous or hazardous environments as well as the use of self-driving vehicles for better supply chain management. As IoT continues to evolve, connected devices that use cloud computing will become the workhorses for a successful PdM program. These devices allow plant personnel to access previously siloed data for a clearer, more holistic view of asset health.
Implementing Maintenance 4.0
A successful PdM program begins by assessing the plant’s daily workflow with industry benchmarks and key performance indicators. A roadmap for improvement is then established based on business goals and budgetary requirements.
Digitalization and the ever-increasing use of big data bring greater interconnectivity between machines and devices. Industry 4.0 is arming manufacturers with the ability to use data strategically to streamline processes, boost efficiency, and significantly improve productivity. The gains from these measures include improved control, transparency, speed, productivity, modularity, availability, safety, and sustainability.
The goal of Maintenance 4.0 is to utilize connected devices to gain insight into asset behaviour so the plant can move from reactive maintenance to PdM and prevention with the ultimate goal of dramatically improving reliability and performance. MRO
Josh Flemming is the Strategic Market Director for SKF USA.