Key Considerations to Get the Biggest Bang for Your Predictive Maintenance Buck

Proactive planning and preventative measures are key ingredients in the organizational strategy of many companies; helping businesses reap benefits from cost reductions to improve efficiency.

In many facilities, equipment is one of the primary areas in which a proactive and preventative approach can make a significant impact. In this post, we will examine predictive maintenance – a technique that reliability engineers and other relevant personnel can implement to reduce downtime and save the organization money. Specifically, we will outline the top three considerations in rolling out and effectively operating a predictive maintenance program.

Why predictive maintenance?

First things first – it’s important to establish a good working definition for predictive maintenance. Simply put, predictive maintenance refers to a technique by which determinations can be made as to the condition of equipment currently in service. This information allows companies to estimate when maintenance should be performed on the equipment.

How does it work?

Predictive maintenance relies on a series of sensors to collect and transmit key data points. Through algorithms and statistical models, this data can proactively identify potential failures before they occur and inform preventative measures.

Key considerations

1. Clearly communicate the ROI

As with most programs and initiatives, it is important to effectively communicate the potential return on investment (ROI) to the team. Predictive maintenance can offer benefits both in terms of uptime and equipment capital investment.

The ROI can also be rolled-out and demonstrated in phases and pilots. For example, small test cases on the organization’s most important equipment can help demonstrate the advantages of the technique on a smaller budget. Armed with the data and results of an effective pilot, the program can then be expanded company-wide.

2.Use data-driven analytics to support changes

As is the case with most programs designed to reduce cost or increase efficiencies, effective predicative maintenance relies heavily on data.

When building and executing a predictive maintenance program, data analysts and scientists can prove to be invaluable members of the team. These experts develop predictive models based on past trends and data, as well as clean data sets and identify important correlations.

While data is a key driver and critical component of predictive maintenance, it is important to note that it cannot replace human logic. Rather, the process should be treated as a complement to reliability engineers’ work – enhancing their ability to reveal opportunities for maintenance and process improvement.  

As sensors are an important component of any predictive maintenance model, it is important that data analysts and scientists fully understand the sensors in use – their accuracy, behavior and patterns. Signals and moving averages are among a number of measures that are key indicators for data scientists and analysts to understand.  

3.Prioritize predictive maintenance needs asset-by-asset

Planning is a critical component of predictive maintenance. The technique should be implemented in a controlled manner, asset-by-asset. With this in mind, determining which assets to begin with and prioritize is critical.

Some key considerations in prioritizing needs asset-by-asset include:

• Examine data and determine what information is relevant and necessary for the roll-out
• Identify the assets that operate in a predictable manner
• Determine where opportunities exist to minimize downtime and reduce investment in equipment

The takeaway

While predictive maintenance may require an initial investment in resources, personnel and equipment, it has the potential to bring your company a significant ROI by reducing costs and downtime.

Learn more

For more industry news and insights, be sure to visit our website at www.pfmainc.com.

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