Predictive maintenance | why the time to start is now

Predictive maintenance: Why the time to start is now

Unplanned downtime isn’t just inconvenient, it can be a costly disaster. Every unexpected failure leads to lost production, costly repairs and operational disruption. That’s why companies are eager to get started with predictive maintenance,and move from reactive to proactive mode.

But here’s the catch: the success of predictive maintenance relies on having the right data. To effectively predict issues, you need both sensor data and maintenance history from your equipment. And it’s not just about having data; it’s about having enough data over time. The less frequent the faults, the longer the historical data you’ll need to identify patterns and build accurate models.

If you’re not actively preparing your data now, you’re already falling behind!

What is predictive maintenance?

Predictive maintenance uses sensor data, analytics, and machine learning models to detect early signs of equipment failure before it happens. By continuously monitoring machine data and linking it to past maintenance events, companies can:

• Minimize unplanned downtime and production losses
• Reduce maintenance costs by fixing issues before they escalate
• Extend equipment lifespan with smart maintenance decisions

However, successful predictive maintenance requires a well-structured historical data. 

How to get started?

1. Start gathering sensor data

Your equipment likely produces a lot of data already – from temperature and pressure to energy usage. But many organizations don’t capture it or store it in a way that’s accessible and usable. 

• Install or upgrade IoT sensors if they aren’t already in place. Most production lines, robot cells and other equipment already have these sensors installed and gather the required data. It might even be visible from a dedicated HMI or vendor-provided data platform. Especially usable data includes vibration, temperature, energy consumption and pressure.
• Ensure data is collected continuously and stored in a scalable and centralized platform, such as Owl.
• Begin archiving this data, even if you don’t have immediate plans for predictive maintenance. Historical data is needed when building predictive models later.

2. Organize maintenance records

By digitizing and standardizing your maintenance logs, you’ll create a stream of data that can be integrated to sensor data when starting to implement your predictive algorithms. When matching historical maintenance data with deviations in sensor data, we start to notice cause-and-effect patterns that can be used to predict future maintenance needs.

Include atleast the following data in your maintenance records:

• Failure types and causes
• Failure intervals and timestamps
• Maintenance actions taken and their outcomes
• Costs and downtime associated with each event. This helps you prioritize which models to focus on first.

Potential cost savings

Predictive maintenance is crucial for enhancing operational efficiency and reducing costs. By gathering and analyzing sensor data from production lines, companies can anticipate equipment failures before they occur, leading to significant cost savings.

Studies indicate that predictive maintenance can reduce machine downtime by 30% to 50% and increase machine life by 20% to 40%. How much does one hour of unplanned downtime cost for your company? Think about it.

Implementing a predictive maintenance with a platform like Owl enables manufacturers to:

• Monitor Equipment Health: Continuous data collection from machinery to detect early signs of wear or failure.
• Analyze Data for Insights: Utilize advanced analytics to predict potential issues before they escalate.
• Schedule Maintenance Proactively: Plan interventions at optimal times to prevent unexpected breakdowns.

By adopting Owl’s predictive maintenance solutions, companies can achieve improved operational efficiency, reduced maintenance costs, and extended equipment lifespan.

Investment in the future

Predictive maintenance is all about working smarter with the resources we have. By addressing issues before they escalate, predictive maintenance helps us avoid unnecessary part replacements, reduce waste, and minimize the environmental impact of excessive production and discarded materials. It’s not only better for business but also for the planet.

Time is another critical resource. Every moment spent on unplanned downtime or unnecessary repairs is time that could have been better spent on strategic initiatives. By focusing on predictive maintenance, companies can allocate their time and energy more efficiently, driving innovation and growth instead of firefighting operational problems.

By gathering and organizing sensor data and maintenance records now, you’re laying the foundation for a smarter, more sustainable future. This preparation will help you implement predictive maintenance faster, achieve better ROI, and operate with greater responsibility toward both your organization and the environment.

The time to start preparing is now.

Would you like to know more? Contact us!