Unplanned machine downtime costs many industrial companies millions of euros every year. It is estimated that on average around 8% of a company’s annual turnover is lost due to unplanned downtime.
However, the cost-intensive downtimes have other negative effects in addition to the financial losses. In most cases, a machine breakdown creates considerable stress situations for all employees involved in rectifying the downtime.
In this article, we take a closer look at the definitions and how they work. And we will use a practical example to show you how you can easily implement condition control and predictive maintenance in your operations.
What is “condition monitoring”?
Condition monitoring, often also referred to as “condition control”, refers to the detection of operating states and potentially critical events. The monitoring of operating states and the diagnosis of faults is the core of condition monitoring.
Abnormal or critical developments in machine conditions can thus be detected immediately and responded to quickly. The negative effects can be kept to a minimum thanks to the rapid findings.
What is predictive maintenance?
In contrast to condition monitoring, which is based on monitoring the current status of machines and systems, predictive maintenance focuses on forecasting future events.
Predictive maintenance is therefore the prediction of the probability of equipment and machine failure.
Predictive maintenance is also often referred to as “predictive maintenance”. The prediction is usually based on data and empirical values such as the expected service life or trend analyses.
‘Condition monitoring’ and ‘predictive maintenance’ in industrial companies.
The starting point for industrial companies aiming for predictive maintenance is to obtain condition data from machines. In order to then subsequently plan and carry out the maintenance of machines and systems before predicted failures occur.
Condition monitoring generally provides the basis for predictive maintenance. This is because the collected data and empirical values are the information basis for future forecasts.
In the best case scenario, a combination of sensors and data collected by humans is used. In order to obtain as comprehensive and accurate a picture as possible of the condition of the machines.
In the long term, the two trends bring two main advantages for companies:
- Faster detection of faults, wear or changing conditions in machines, systems and devices.
- Reliable prediction or forecasting of the productivity of machines, systems and devices.
The result: losses due to downtime can be reduced or avoided.
Practical example Testify: How to use smart checklist software to avoid machine failures.
Smart checklist software such as Testify can help you to improve your maintenance processes based on data.
Generate data to prevent machine failures.
The digital checklists can be used to continuously collect data on the condition of systems and machines. Condition monitoring then works via dashboards.
The data collected on systems, devices and machines is used to detect critical events and predict the probability of a failure. This means that cost-intensive system 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 simpler implementation
In addition to the collection of machine-related data, the use of software to improve machine maintenance has many other benefits. Above all, the focus here is on better support for service personnel on site.
Here are a few examples of how employees can be supported digitally on site:
- With a mobile app, maintenance technicians and maintenance engineers always have relevant data and information at their fingertips.
- Simpler communication channels that make it possible to record defects quickly and forward them to the responsible persons.
- Digital work instructions for ongoing maintenance guide you step-by-step through the process.
- Standardized checklists that make consistent and high-quality documentation effortless. Maintenance and servicing processes can be recorded virtually on the go using a smartphone or tablet.
This ensures that workers are optimally positioned for maintenance work and that the work can be carried out safely and quickly.
Testify also brings added value at the organizational level with the scheduled assignment of tasks to employees. Maintenance tasks can be easily organized through periodic assignment and predefined workflows. The status and results of checks can also be checked quickly.
Conclusion
Condition monitoring gives companies the ability to react more quickly to changing machine conditions. Predictive maintenance is used to predict failures based on collected data. Unplanned downtimes can thus 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 both financial losses and stressful situations for everyone involved.
