To protect your assets, you need to predict. Manufacturers’ Monthly explores the distinction between predictive maintenance and condition monitoring, as well as the key components of a successful predictive maintenance program.
Condition monitoring is a maintenance approach that involves monitoring the performance and health of machines or equipment continuously to identify signs of potential issues or deviations from normal operation.
Predictive maintenance is a proactive approach that utilises data analysis to predict when maintenance will be needed, allowing manufacturing companies to reduce downtime and minimise maintenance costs.
While both approaches hold value, predictive maintenance stands out as a proactive strategy that leverages data analysis to predict maintenance needs.
Freddie Coertze, the national business manager for ifm Australia, explains, “If I were to summarise the key differences it would be to say that condition monitoring is a reactive approach to maintenance management that focuses on identifying problems as they occur, while predictive maintenance is a proactive approach that uses data analysis to predict when maintenance will be needed.
“Both approaches can be valuable, but predictive maintenance is defined by the word ‘predict’. It has the potential to provide greater benefits by reducing downtime and minimising maintenance costs.”
According to Coertze, there are three components that are crucial to ensure the success of a predictive maintenance program an Internet of Things (IoT) platform, condition-monitoring hardware, and –predictive formulas provided by artificial intelligence.
“To have visibility of assets, you need the connectivity and integration that digitalisation provides,” Coertze remarks. “To help our customers simplify this integration, ifm created moneo – a self- service software platform that acts as a middleware to existing systems such as SCADA.”
The moneo solution by ifm combines operating and information technology to bring the power back into the engineer’s hands. It has an inbuilt DataScience Toolbox that enables operators and engineers to leverage their knowledge of equipment with the benefits of AI- assisted predictive analytics and machine learning.
“Successful predictive maintenance is about detecting anomalies to machine performance early and automating the actions surrounding that deviation so that not only are notifications sent, but actions are prescribed to fix the issue,” explains Coertze.
“It’s having full integration from the shop floor to the top floor.”
Currently, while condition monitoring is widely used, there is a disconnect between the data collected by sensors and the workflow, says Coertze.
“Many industrial businesses still rely on manual processes in terms of how their sensor data is analysed and maintenance is performed. Many businesses are still using a preventative or scheduled maintenance approach – they might look at vibration on componentry but it’s not going to give them the insight or ability to solve real equipment issues in advance,” he explains.
“I am of the opinion that this type of condition monitoring – where it is applied within a manual context of action – provides information too late to make a significant difference.”
The moneo solution supports successful predictive maintenance by providing the platform, the sensor hardware, and the software that supports AI-assisted predictive formulas.
“To explain simply how it works, we would first connect the sensors to the equipment, then we would collect data from the sensors and the moneo software will identify baselines and set parameters and limits as to how the assets should be performing,” he says.
“If the system detects an anomaly, a ticket will be sent to staff to investigate with a prescribed action to first check and then rectify the problem. This same predictive maintenance workflow is then applied to everything within the operation or plant.”
There are many benefits that come with implementing successful predictive maintenance – namely better longevity of equipment, improved productivity onsite, and improved efficiency, particularly in the use of energy.
Predictive maintenance will do more than prevent downtime, it will help keep equipment always running optimally.
“Condition monitoring with vibration analysis is simply not enough – by the time vibration has started, it’s often already too late to intervene and save the machine. To protect your assets, you need to predict. That’s why having a predictive maintenance program is important and moneo can ensure that this program is implemented with success,” Coertze concludes.
By adopting a predictive maintenance program supported by tools like ifm’s moneo, manufacturers can achieve optimal equipment performance, prevent downtime, and ensure efficient operations.