Redesigning maintenance processes to optimize PdM automation
As the Industrial Internet of Things (IIoT) expands, organizations may be tempted to throw devices at the wall to see what sticks and what doesn’t. While some may choose this route to implement connected reliability devices and tools, it doesn’t work well in the long run. Instead of rushing into IIoT-enhanced maintenance programs, companies should take the time to adapt their processes and people before adopting new technologies and workflows.
Whether a company is starting or stalled in maintenance automation implementation, an audit will help the company determine where they’re heading. Finding out what is and is not working within the maintenance department enables companies to improve productivity, automate previously manual efforts, and successfully implement predictive maintenance (PdM) devices.
The first steps to maintenance automation
Before beginning maintenance automation and digitalization, maintenance leaders should conduct several critical internal studies. The first is an asset criticality list, which creates a hierarchy of equipment based on how important it is to the organization’s bottom line. The asset criticality list will help teams prioritize repairs and decrease downtime. It provides direction on the best maintenance strategy for production. Once the assessment is completed, an organization can determine how to allocate resources based on time spent or the types of tools needed.
The key to asset longevity
A potential-failure (P-F) curve demonstrates the relationship among asset lifecycles, costs, and the various technologies/maintenance practices used to prevent failure. How production flows are designed, equipment is installed, and how it operates against manufacturer specification determines performance over time. Assets will never work properly if design and installation are not precise. The design and installation portions of the curve, which are ahead of the P-F interval, are key to the longevity of the asset. The true intent of the P-F curve is to illustrate where reliability tools, strategies, and maintenance processes need to be applied to have the most impact on the reliability of assets.
Define failure, leverage the P-F curve
The second step is defining what “failure” means to the organization. The P-F curve is used to determine the best testing modality to extend performance over time and elongate life cycle. Specifying what does and does not constitute a failure helps teams standardize their response and prioritize actions. An asset running at lower capacity may not have failed yet, but it will negatively impact an operation. This is called a functional failure and it happens toward the bottom of the P-F curve.
Assets with identifiable physical conditions may be nearing the potential failure point — where a failure is probable, but hasn’t happened yet. By this time, corrective action is necessary to extend the asset’s life.
Functional failure comes when the asset is unable to meet the specified performance standards, which vary by company. Creating an asset criticality list and defining each asset’s functional failure across the organization will help prioritize asset repairs and ensure uptime.
Processes and people
After processes have been audited and the asset criticality list is compiled, supporting team education is vital to successful implementation.
Companies in a rush to adopt new technologies often inadequately prepare personnel for the impending change. Before implementing IIoT devices, ensure personnel:
- Receive proper training
- Understand the processes
- Adopt a reliability culture
- Know how to select and review data.
When the process begins, involve all levels of team members in the journey. Those who do not participate may be left wondering “What’s in it for me?” Participation is a key component to success. Allowing employees to provide feedback on implementation will help them be engaged and have a vested interest in the program’s success.
Pave the way to IIoT automation
Implement a limited pilot program to determine whether an organization is ready to adopt an IIoT approach. By ensuring people and processes are prepared for the digital transformation, organizations have verified there is capacity for changes, and goals are attainable and maintainable in the long run. Demonstrate how PdM makes a difference to the bottom line to encourage leadership buy-in.
Starting small will help ensure the success of the large-scale expansion. This approach keeps teams and the organization from being overwhelmed by large amounts of data. Pick a set of assets to run through the program from start to finish. Choose the testing modalities — such as ultrasound, thermography, or vibration — that apply to the pilot assets and select the associated IIoT devices. Use them to gain actionable data from assets, have teams execute against the data, and monitor the time and money saved while creating the case for investment.
Automating maintenance processes and practices allows teams to do more with less and leads them to connected reliability through PdM. New technologies enhance and automate maintenance strategies to help organizations maximize reliability. Redesigning processes prepares teams for IIoT and transforms maintenance efforts into a business value driver.
Frederic Baudart, CMRP, lead product application specialist, Fluke Corp. Edited by Chris Vavra, production editor, Control Engineering, CFE Media, firstname.lastname@example.org.
Keywords: Predictive maintenance, PdM, Industrial Internet of Things
Companies seeking to adopt predictive maintenance (PdM) programs should take a long-term and pragmatic approach.
Defining success and failure through a potential-failure (P-F) curve can bring companies some insight.
Automating maintenance processes and practices leads to connected reliability through PdM.
What immediate and long-term benefits could PdM provide?