Machine Condition Monitoring for Improving OEE

Machine maintenance is an essential aspect of any industrial operation, as it helps to ensure the longevity and reliability of machines. One of the best ways to ensure equipment uptime and maximize productivity is to consistently monitor the condition of your machines.

Machine condition monitoring can help to prevent unexpected breakdowns, increase machine performance, extend machine life, and minimize downtime. In this post, we will explore the importance of machine condition monitoring and the various methods used to evaluate machine performance.

Machine condition monitoring is a technique for tracking a machine’s health and performance over time. With condition monitoring, technicians or operators measure various parameters like temperature, current, vibration, and RPM against set thresholds in order to pinpoint signs of degradation.

Condition monitoring is an important tool in the machine monitoring and continuous improvement toolkit, and there are many benefits to tracking machine conditions in real-time.

Download our Complete Guide to OEE to Improve the Way You Measure & Analyze Equipment Data →

Condition monitoring is an important focus for any manufacturer using industrial equipment within their operations. Some of the key benefits that come with machine condition monitoring include:

1. Early Detection of Issues: By monitoring the condition of a machine, it is possible to identify potential issues before they cause significant damage. This allows for timely repair or maintenance, which can save money and prevent downtime.
2. Improved Machine Performance: Regular monitoring of a machine's performance can help to identify areas where improvements can be made. This can lead to increased efficiency, higher productivity, and reduced energy consumption.
3. Increased Machine Life: Regular monitoring of a machine's condition can help to identify potential issues early and prevent them from causing damage. This can help to prolong the life of the machine and reduce the need for costly replacements.
4. Predictive Maintenance: Machine condition monitoring can provide information about the current state of a machine and its future performance. This information can be used to plan maintenance activities more effectively, reducing the need for unscheduled repairs and minimizing downtime.

Part of the reason condition monitoring is such an effective technique is that it can be applied to a wide variety of scenarios. What you measure depends on the assets in your factories and the kinds of data that will prove most useful to your operations.

Many organizations measure the current and voltage moving through their transformers to understand how efficiently their assets consume energy.

Others measure oil temperature to track whether a machine is cooling effectively, or track external surface temperature to see if heat is dissipating as expected.

Organizations who rely on motors may also measure vibration to make sure that rotating components are properly balanced and aren’t contributing to the premature degradation of other parts.

This is a partial list, and what you measure depends on the questions you want to answer.

It’s easy to get started with a machine condition monitoring program. Most conditions can be easily measured by affordable sensors. These are often mounted directly onto an asset, and connected with a manufacturer’s data collection infrastructure to track important condition parameters.

The advent of the Industrial Internet of Things has made it possible to monitor machine conditions without being physically present during uptime. With IoT, sensors communicate the data they record to designated storage spaces. If a parameter exceeds a certain threshold, the system can send an alert to the relevant specialist.

Further, analytics dashboards can display this data in real time, allowing engineers and operators to visualize previously invisible aspects of condition on the shop floor.

The real value of a machine monitoring program comes when you can track many conditions simultaneously.

This data lets you isolate the root cause of problems, and prevents unnecessary failures by providing a complete view of asset health over time.

Not only can you plan maintenance schedules in response to designated health and performance thresholds. You can also revise those thresholds based on the usage of a machine in your operations.

Perhaps you work in a hot, humid facility. Machines may degrade faster and require more frequent maintenance. Maybe your machines don’t need maintenance as frequently as prescribed. With condition monitoring, you can tailor your schedules to real performance, maximizing uptime and avoiding unnecessary expenditures (the classic example here is changing a car’s oil every 3,000 miles vs. when it needs to be changed).

We’ve written before that predictive maintenance–a goal many organizations aspire to during Industry 4.0–is impossible without granular, well-structured historical data.

Condition monitoring is an essential place to start for organizations interested in using predictive algorithms to optimize their training schedules. Without adequate data–data that gives a complete enough picture for machine learning and AI to infer the causality of problems–the most sophisticated AI in the world won’t be able to help.

The important thing is to start soon and to create a solid infrastructure that will allow you to capture the most complete machine data possible.

Curious how you can get started with condition monitoring in your operations? Get in touch and a Tulip expert can show you how to get the most out of your machine monitoring.

Tulip can help you measure your machines’ health and performance. Get in touch for a machine monitoring consultation today.