Many companies rely on equipment to produce products or carry out services. Maintenance is critical for expensive physical assets such as machinery and manufacturing equipment, both in order to extend the usable lifespan of these costly assets as well as to maintain compliance with regulatory requirements. Routine, proactive maintenance helps to keep costly investments in good working order, which can boost efficiency and productivity while minimizing unforeseen delays due to malfunctioning machinery.
Preventive maintenance vs. predictive maintenance
According to Maintenance Assistant CMMS, preventive maintenance is also known as planned maintenance or planned preventive maintenance and is driven by triggers such as time-based triggers, event-based triggers, or other indicators that maintenance is necessary before a breakdown occurs. Following triggers, routine maintenance is performed, which may include various tasks depending on the age of the equipment and manufacturers' recommendations, all of which are designed to prevent breakdowns and interruptions in workflow.
Preventive maintenance has been a hot topic for many years now, but a different form of maintenance is growing in popularity in recent years: predictive maintenance. The ideas behind predictive maintenance have been around for at least two decades, although in recent years, there's a growing trend towards utilizing these technologies to head off problematic disruptions in manufacturing and other industries.
Reliable Plant explains that predictive maintenance is also known as condition monitoring, a method that can be defined as "the application of condition-based monitoring technologies, statistical process control or equipment performance for the purpose of early detection and elimination of equipment defects that could lead to unplanned downtime or unnecessary expenditures." Monitoring is carried out with the use of measurement tools and asset tags that are quickly and easily scanned to document readings, routine maintenance, and other information that, when combined and analyzed, creates a comprehensive picture of the health of an asset. With a multitude of types of asset tags designed for industrial use and other harsh environments, today's industries are even able to track assets regularly exposed to harsh chemicals or other hazardous environmental conditions.
In other words, predictive maintenance focuses on the continuous monitoring of a machine's health under normal working conditions -- without process interruptions -- to detect subtle changes that generally aren't detectable during typical inspection processes. These subtle warning signs then become the triggers that indicate an impending problem, enabling operators and maintenance providers to plan for repairs and downtime, order necessary parts, and take other preparatory action to minimize the eventual disruption in processes.
Why is predictive maintenance so crucial?
Predictive maintenance is becoming imperative for modern operations, as continuous production is critical for avoiding delays since stakeholders and end consumers have become accustomed to instant gratification. Production delays in the manufacturing process can create a domino effect, disrupting the entire supply chain. Product backorders can lead consumers to turn to competing vendors, with ramifications that can ultimately be catastrophic to a company's bottom line. In today's fast-paced world, it's all about who can produce the most with the greatest efficiency and accuracy.
Predictive maintenance emphasizes the detection of potential problems before they become real malfunctions. Modern Machine Shop explains, "The objective of PDM and similar programs—such as Machine Tool Variability Management System (MTVMS), Reliability and Maintainability (R&M), Failure Mode and Effective Analysis (FMEA) and Total Productive Maintenance (TPM)—is to predict when a machine tool will fail or go out of tolerance in order to reduce unplanned downtime, particularly at critical times during production."
Predictive maintenance relies on a few techniques to detect problem indicators, including tactics such as vibration analysis, the use of infrared thermography equipment, and calibration. Because these tools are used to monitor machines continuously, data can be collected and analyzed to create a baseline against which all further measurements can be compared. Even changes as subtle as an increase in vibration can indicate damage to a specific component such as a rolling element bearing. In contrast, such defects were previously detected through the use of lift checks which measured clearance, but by the time a detectable difference was measurable, the equipment had to be in dire condition. By preemptively addressing emerging problems by measuring minute changes, companies are better able to minimize downtime and maintain desirable production levels.
Today, most manufacturing companies and other industrial operations rely on a combination of predictive and preventive maintenance, conducting period recommended tasks such as cleaning equipment, oiling parts, replacing commonly failing components, and other techniques. Methods like this have proven to extend the usable lifespan of these costly assets while also engaging in continuous monitoring to identify potential emerging issues that aren't easily detected during routine maintenance. The combination of these activities enables companies to get more mileage from every investment while also maintaining a healthy bottom line by avoiding unplanned downtime and other disruptions that tend to have a ripple effect throughout the company and the subsequent supply chain.
Getting started with predictive maintenance
While implementing a predictive maintenance program is a process, the ROI can be substantial if you take the time to do it right. First, determine the specific business problem you're trying to solve (reduction in downtime, lowering replacement costs, etc.). Then rank and analyze your existing assets, determine which (if any) assets are currently equipped with sensors and other tracking devices such as asset tags. Finally, determine the specific metrics that can be used to trigger the need for maintenance activities and how those metrics are best measured.
Of course, these are general and basic steps describing what is actually a far more complex and individualized process. But by beginning with a clearly defined business problem and then working backward to determine the assets to monitor, metrics to measure, and ultimately how you'll measure them, you'll develop an effective predictive maintenance program that will have a direct impact on your bottom line.
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