Page 56 - North American Clean Energy March/April 2020 Issue
P. 56

                    wind power
Forward with
Maintenance 4.0
by Josh Flemming
Digital transformation and the power of big data is fueling 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.
  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 operational 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 because 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 operators struggle to implement Maintenance 4.0 strategies due to perceived obstacles. Companies can optimize machine performance and increase productivity by overcoming the challenges outlined below.
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.
Any company with an internet connection can use connected devices 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.
Operational Shortcomings
One concern is whether OEM-specific or proprietary monitoring equipment can support equipment 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 skillsets.
Run-to-Failure Mindset
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 costs and breakdown costs, in addition to inventory and labor 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 personnel 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.
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. Using the wrong type, the wrong amount, or using it at the wrong time, are primary reasons for lubrication-related failure.
Automatic lubrication increases bearing, gear, and chain life, by applying measured amounts of lubricant consistently while the machine is operating. This virtually eliminates the need for manual lubrication. It also reduces lost production time and prevents accidents that can occur during manual lubrication, since the machine
no longer needs to be shut down. Waste, product contamination, and cleaning issues, are also substantially reduced.
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