Unplanned machine breakdowns cost many industrial companies millions of euros every year. It is estimated that on average about 8% of a company's annual turnover is lost due to unplanned downtime.
However, the cost-intensive shutdowns have other negative effects besides the financial losses. In most cases, a machine breakdown creates considerable stress situations for all employees involved in resolving the breakdown.
In this article, we will go into more detail about the definitions and functionality. And with the help of a practical example, we show you how you can easily implement condition control and predictive maintenance in your operation.
Content
Definition of Condition Monitoring
Definition of Predictive Maintenance
Advantages
Practical example
Conclusion
What is „Condition Monitoring”?
Condition monitoring, often also called “condition control”, refers to the detection of operating conditions and potentially critical events. Monitoring operating conditions and diagnosing faults is the core of condition monitoring.
Abnormal or critical developments in machine conditions can thus be detected immediately and reacted to quickly. Due to the quick insights, the negative effects can be kept to a minimum.
What is „Predictive Maintenance“?
In contrast to condition monitoring, which is based on monitoring the current condition of machinery and equipment, predictive maintenance focuses on forecasting future events.
Predictive maintenance is therefore the prediction of the probability of failure of equipment and machines.
tsch also often referred to as “predictive maintenance”. Prediction is usually based on data and experience, such as expected service life or trend analyses.
‘Condition Monitoring’ and ‘Predictive Maintenance’ in Industrial companies.
The starting point for industrial companies seeking predictive maintenance is to obtain condition data from machines. In order to then subsequently plan and carry out maintenance of the machines and equipment before predicted failures.
Condition monitoring usually provides the basis for predictive maintenance. This is because collected data and empirical values are the information basis for future forecasts.
In the best case, a combination of sensors and data collected by humans is used here. To get the most comprehensive and accurate picture of the condition of the machines.
In the long term, the two trends bring two main benefits for companies:
- Faster detection of faults, wear or changing conditions in machines, plants and equipment.
- Reliable prediction or prognosis of the productivity of machines, plants and equipment.
The result: losses due to downtime can be reduced or avoided.
Practical example Testify: How to use smart checklist software to avoid machine downtimes.
Checklist software such as Testify can help you in the data-based improvement of your maintenance processes.
Generate data to avoid machine breakdowns.
The digital checklists can be used to continuously collect data on the condition of plants and machines. Condition monitoring then works via dashboards.
The collected data on plants, equipment and machines is used to detect critical events or to predict the probability of a failure. In this way, cost-intensive plant failures can be avoided in the future!
For example, limit values or similar can be stored in order to keep a better eye on the condition of the machines. If the condition of a machine changes critically, an inspection can be ordered immediately via the app.
Better support for workers and easier execution.
Besides the collection of machine-related data, the use of software to improve machine maintenance has many other benefits. Above all, the better support of the service personnel on site is in the foreground here.
Here are a few examples of how on-site workers can be digitally supported:
- With a mobile app, maintenance staff and maintenance engineers always have relevant data and information at their fingertips.
- Simpler communication channels that make it possible to quickly record defects and forward them to the responsible persons.
- Digital work instructions for ongoing maintenance guide you step-by-step through the process.
- Standardised checklists that make consistent and high-quality documentation effortless. Maintenance and servicing processes can be recorded virtually on the fly via smartphone or tablet.
This ensures that workers are well supported during maintenance work and that the work can be carried out safely and quickly.
Testify also brings added value at the organisational level with the scheduled assignment of tasks to employees. Maintenance tasks can be easily organised through periodic assignments and predefined workflows. Likewise, the status and results of checks can be checked quickly.
Conclusion
Condition monitoring gives companies the ability to react more quickly to changing machine conditions. With predictive maintenance, failures are predicted based on collected data. In this way, unplanned downtime can be reduced or avoided.
Predictive maintenance and condition monitoring will be essential for manufacturing companies in the future! Because there is a lot of (savings) potential here to prevent financial losses as well as stressful situations for everyone involved.
Would you like to digitalise your maintenance processes?
We show you how to gain control over the maintenance processes of your machines, plants and buildings with Testify.
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