What is the Difference Between Preventive and Predictive Maintenance?

What is the Difference Between Preventive and Predictive Maintenance?

The maintenance strategies organizations use have a large impact on how well they can maintain and improve their operations. The right type of maintenance approach for every organization should be one that is able to efficiently stop machine and system failures before they occur. It should also enable organizations to do so in a way that causes the least amount of disruption.

But what type of maintenance approach prevents breakdowns and makes machine downtime a factor you can completely control?
In this post, we are going to take an in depth look at important differences between preventive and predictive maintenance. While both concepts are proactive in nature and have the similar objective of stopping breakdowns before they occur, the methods and principles they employ are distinctly different and will impact how efficient and accurate your organization’s maintenance efforts will be.

What is Preventive Maintenance?

The prevailing theory behind preventive maintenance is that regularly scheduled inspections, tests, replacements, servicing, etc., will prevent these issues contributing to machine failure before they arise. It operates on the assumption that machine components will ultimately fail over time due to use or age and that those components should be replaced before they fail.

With preventive maintenance, the application of maintenance is determined largely by time. For example, in many preventive maintenance programs, a manufacturer’s recommendation that a certain component be replaced after a certain amount of time, regardless of the condition of the component. The expected working life of components guides when components are replaced and replacing components at regular intervals reduces the likelihood of failure and breakdown. Maintenance tasks can also be scheduled on a certain time interval or on a specific date.

The regularity of component replacement can make preventive maintenance easy to plan. However, these planned preventive measures are implemented even when systems and machines are in optimal working condition. Preventive maintenance does not consider the actual condition or state of equipment or component.

For organizations that are relying on preventive maintenance:

● Components are replaced even though they are still functional

● An exorbitant amount of time and other resources are spent on non-existent problems

● The amount of machine downtime increases whether or not it is planned

● The excessive maintenance introduces risks to the machines that may be functioning correctly

● More extensive parts and inventory management is necessary in order to have replacement components on hand

Under a preventative maintenance program, certain components of a wind turbine, such as the gearbox, would be replaced (an expensive and logistically complicated task) even if it had ample useful life remaining. In an aircraft crankshaft, the signs of an impending belt failure may be overlooked because its replacement is determined by a schedule rather than the current state of the belt.

What is Predictive Maintenance?

Predictive maintenance is a data-driven approach to preventing machine breakdown and unexpected downtime. It is a form of maintenance that has become particularly applicable in the Industry 4.0 age in which large amounts of data are able to move easily among connected devices. By leveraging data, organizations can accurately identify issues in the very early stages before they become full-blown issues that cause disruption.

Condition-based monitoring or monitoring of systems or machines using sensors that collect and transmit data about the current condition and performance, is a critical element of predictive maintenance. The data collected during this process, typically with connected, IoT-enabled CBM and PdM technologies like Odysight.ai’s visual sensor solution, is used to help detect anomalies that indicate potential problems. The technology that is used during condition-based monitoring is important because it helps to provide a comprehensive and accurate snapshot of the condition and performance of a machine.

In addition to the real-time machine data that helps provide a clear picture on the current state of the machine, historical data related to prior breakdowns and component failures is also used for predictive maintenance. Both historical and real-time data are processed with machine learning and AI-trained models that highlight important data trends and that create accurate forecasts about impending failures.

Based on the insights gained from the analysis, the appropriate maintenance measures can be executed to remedy the issues that have been revealed. These measures can be automated, such as the automated ordering of a certain component or the scheduling of the appropriate personnel to replace a component.

Predictive maintenance uses machine data to enable more informed and nuanced maintenance decisions that positively impact how an organization’s maintenance operates as a whole and how their machines and systems are managed.

In predictive maintenance:
● Machine downtime is more targeted and used in an efficient manner

● The maintenance options that are applied directly addresses impending issues as determined by the data and predictive analytics

● The management of inventory is more efficient as functional components are not replaced unnecessarily and enough foresight is provided to have replacement components on hand when needed

Predictive Maintenance is More Effective Than Preventive Maintenance

The consideration of the actual condition and performance of machine components is a factor that is making all the difference in the effectiveness of an organization’s maintenance efforts. With condition-based monitoring and predictive maintenance, organizations can leverage machine data to make machines and systems more reliable.

Predictive maintenance succeeds where preventative maintenance typically falls short. Instead of applying maintenance according to a set schedule, whether or not the measure is needed, predictive maintenance adds flexibility and adaptability to maintenance so that organizations are able to properly address maintenance issues when the needs arise. With predictive maintenance, machine downtime becomes a factor that the organizations control. It helps to make machines and systems more reliable, and it does so while avoiding the high costs and resource waste typical of preventive maintenance.

The financial impact of applying predictive maintenance instead of preventive maintenance is especially worth noting. The average organization is spending 50 percent more than they should on preventive maintenance considering the waste that occurs with the excessive maintenance prevalent with the approach.

One study also shows that preventative maintenance reliance on the expected working life and usage of components is beneficial to just 18 percent of an organization’s assets. However, organizations that use predictive maintenance will reduce maintenance costs, experiencing 8 percent to 12 percent cost savings over preventive maintenance programs.

Predictive maintenance has become the most relevant maintenance approach for today and requires the right technology for implementation. Odysight.ai®’s visualization AI-platform can help maintenance teams pinpoint issues within an organization’s machines and systems in the early stages, giving them the foresight to plan and execute the appropriate maintenance measures.

To see firsthand how our condition-based monitoring and predictive maintenance visualization solution can help with your organization’s maintenance efforts, sign up to obtain our starter-kit probe.

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