Learn the difference between predictive and preventive maintenance, examples of predictive maintenance, its benefits, how it works, and how to best use it when given limited resources
Updated 31 Jan 2023, Published 16 Jul 2021
Predictive maintenance is a strategic approach to optimizing equipment usability. Using data collected from IoT devices such as sensors, machine learning, and real-time equipment monitoring, predictive maintenance determines exactly when it's the best time to perform equipment maintenance. With this capability, maintenance managers save both time and resources.
Preventive maintenance involves doing whatever is required, regardless of the cost or strain on resources, to avoid equipment failure. Predictive maintenance, on the other hand, is focused on taking minimal action while still ensuring that equipment can be used for longer periods of time.
Another difference between predictive and preventive maintenance is that the latter requires frequent inspections, as these form the basis of when maintenance should be performed. With predictive maintenance, equipment conditions can be monitored remotely through the use of sensors and other IoT (Internet of Things) devices.
Data is at the heart of predictive maintenance. While the following are not straightforward examples of predictive maintenance, they do show how real organizations apply and use data to make more informed decisions and take action where it’s needed.
Example 1: BOS Solutions, a liquid solids separation organization in the oil and gas industry, used its data to predict equipment lifespan. This resulted in less defective or deteriorating equipment being used by workers, which led to a decrease in equipment-related injuries.
Example 2: National Grid UK, a natural gas and electricity transmission company, used their data to spot areas of poor performance, potentially risky areas, and areas of good performance.
By dividing its data into separate categories, the company was able to dive deeper into areas of non-conformance. Within these areas, they identified high-risk issues requiring immediate action. As a result, proactive behavior within the company increased and quality improved.
As seen with the examples in the previous section, using data for actions and decisions reaps huge benefits. Given the impact of data on business performance, maintenance managers should consider pursuing predictive maintenance as a proactive maintenance strategy.
While the other types of maintenance, such as preventive, corrective, planned, and condition-based, will not become obsolete in the near future, it’s wise for organizations to get a headstart in applying innovative ideas to their processes.
Below are the top 5 predictive maintenance benefits:
While applying predictive maintenance leads to concrete results, as stated in the section above, some maintenance managers may find it intimidating or overly complicated. To help clarify how predictive maintenance works, here is a simple outline of the process in 4 steps:
Step 1: Sensors collect real-time data on equipment conditions
Step 2: Data from sensors is processed by a predictive algorithm
Expected performance – For example, after 3 years in operation, equipment should still be producing at a rate of 1.5 units per minute
Past failure data – For example, before equipment failed, it produced only 0.3 units per minute and showed the following deterioration signs
Step 3: Powered by machine learning, the predictive algorithm generates predictions on when equipment will fail
Step 4: Based on these predictions, maintenance managers schedule maintenance to occur right before equipment failure or at the time recommended by the algorithm
Though how predictive maintenance works has been explained in general terms in the previous section, maintenance managers may still have difficulty seeing how they can apply a predictive maintenance strategy to their existing maintenance workflow.
This brief guide gives maintenance managers options on how to use predictive maintenance without the technical resources:
Empower your team with SafetyCulture to perform checks, train staff, report issues, and automate tasks with our digital platform.
SafetyCulture (formerly iAuditor) is a digital operations platform that assists maintenance teams in following or implementing standards, and monitoring maintenance tasks. Using SafetyCulture, maintenance managers can assign maintenance tasks and other repair jobs to service technicians.
Service technicians are able to document maintenance work completion. And, maintenance workers are able to perform investigations with the assistance of a digital checklist.
Quickly capture issues, such as equipment malfunction and failures. With SafetyCulture, the entire team can be empowered to work together and solve problems quicker.
Erick Brent Francisco
Erick Brent Francisco is a content writer and researcher for SafetyCulture since 2018. As a content specialist, he is interested in learning and sharing how technology can improve work processes and workplace safety. His experience in logistics, banking and financial services, and retail helps enrich the quality of information in his articles.
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