Condition-based monitoring: Planning for better asset management

3 best practices to improve maintenance and asset management with condition-based monitoring

Condition-based monitoring helps you increase asset uptime and improve throughput. But to maximize the value you gain from condition-based monitoring, you need start with planning and strategy. Here are three critical steps that help you develop a plan for improving asset management with condition-based monitoring. 

#1: Document the current state of maintenance and asset management

When it comes to production, value generation depends on the timely delivery of goods that meet or exceed customers’ quality criteria and help them address their own business requirements, often creating value for their customers in turn. When you review asset management in that context, some machine breakdowns or performance lags may be highly consequential and the impact of others’ might be minimal.

Take an inventory of your current asset management process. What are its successes and shortcomings.? How has your process affected your maintenance teams and machine operators? What do your historical asset records tell you? The answers to these questions help you understand where you’re starting from.

Because you rely on machines to create the output that customers want, you’re likely already taking measures to ensure that production proceeds without disruption. Asset histories can tell you what the costs and consequences were if machines failed, and help you pinpoint possible improvements that could avoid waste.

You may find that certain machines have a tendency to fail, demanding a disproportionate amount of time and attention from your maintenance team. When you assess the financial and operational costs of these breakdowns, you are closer to deciding whether it’s best to continue maintaining that machinery or whether it might make more sense to upgrade, redesign or replace it.

#2: Set priorities and targets for condition-based monitoring

When you set practical, meaningful targets for condition-based monitoring, the effort becomes far more manageable and efficient. Once you know what you want to accomplish with condition-based monitoring, you need to determine which machine or production line to start with.

As you begin, you might want to conduct a proof-of-concept with one of your industrial assets to make sure your data gathering and analytics tools and processes are effective. You should also create a hierarchy of machinery and equipment, depending on how critical these assets are. Condition-based monitoring will be revealing for certain assets, while for some others it might just confirm the obvious.

For some of your machines it will make sense to implement detailed monitoring, determining how well certain components function, lubrication levels, vibrations, throughput, compliance of the output to target specs and more. For others, you might simply track one or two key elements, such as the internal temperature of the power supply or the presence of particulates in the air of a machine’s environment.

You should ensure that the efforts and costs of gathering, storing, and analyzing machine data can translate into actual improvements in maintenance and asset management. Some companies focus only on the critical machinery whose failure would stop production, because that’s where the likely returns of condition-based monitoring are the highest.

#3: Align condition-based monitoring with customer needs and business roles

When you start real-time condition monitoring on your machines, the amount of data generated may quickly become overwhelming. A variety of tools can help you discover relevant findings, identify asset trends, and enable the best responses. For people with different interests and skill levels, today’s asset management and reporting software solutions are much easier to use and configure than earlier versions.

Proven industry metrics such as Overall Equipment Effectiveness (OEE) give you a way to assess machine data in such terms as throughput performance, uptime, and output quality. OEE and similar metrics are not absolutes, but they need to align with your business goals and customer commitments. If your customers want flawless quality above all, throughput may matter less. Or, if a machine or a machine component can quickly and inexpensively be replaced when it fails, uptime statistics might not really help you improve its functioning or make maintenance more efficient.

The findings from condition-based monitoring can enable predictive maintenance, and analytic tools can help you create an efficient schedule for this, including which technician of what skills should perform the work. If you don’t want the maintenance team to be poring over machine reports, you can also set up thresholds in asset monitoring. When a machine’s temperature, vibration, or other condition indicates a possible failure or output waste, automatic alerts will then be displayed on the mobile devices of the technicians and maintenance managers who can return a machine to optimal working order.

You can also provide subsets of condition-monitoring data to other business roles:

• Production and operations managers could verify that manufacturing aligns with the company’s commitments for customer delivery.
• Engineers could better understand how machines work under everyday workloads and make them run better.
• If your condition-based monitoring connects with other business systems, such as ERP, it could be even more useful. For instance, it might become easier for finance managers to assess the true costs of asset management and its impact on margins.

5 best practices to improve OEE with condition-based monitoring