In the modern industry, the availability of reliable and efficient machines is essential. Two key strategies have emerged to minimize downtime and maximize the service life of machines: predictive maintenance and condition monitoring. Both approaches are essential for proactive maintenance, but differ in their methodology and objectives.
Optimized maintenance
through predictive maintenance and condition monitoring
Table of content
Predictive maintenance
Predictive maintenance aims to forecast the most appropriate time to carry out maintenance in order to avoid unplanned downtime and thus to increase machine efficiency. Instead of carrying out repairs according to a fixed schedule or only taking action when damage has already occurred, machine data is used to identify patterns and anomalies. Advanced algorithms and machine learning are used to predict the remaining useful life of components and determine the ideal maintenance time.
Economic benefits of predictive maintenance
Reduced
operating costs
Repairs are only carried out when they are actually necessary, reducing costs for spare parts and working time
Increased
machine reliability
By proactively identifying problems and taking action on time, the reliability of the machines can be improved, leading to an increase in productivity
Increased
machine reliability
By proactively identifying problems and taking action on time, the reliability of the machines can be improved, leading to an increase in productivity
Extended
machine service life
Proper maintenance and timely replacement of parts prevents unnecessary wear and tear
Extended
machine service life
Proper maintenance and timely replacement of parts prevents unnecessary wear and tear
Reduced
operating costs
Repairs are only carried out when they are actually necessary, reducing costs for spare parts and working time
Challenges
during implementation
Ensuring data quality
High data quality is crucial to enable correct predictions. Poor data can lead to inefficient maintenance decisions
Integration into existing systems
The successful implementation of predictive maintenance requires the integration of the required technologies into existing maintenance systems and processes, which entails careful planning and realisation
By using predictive maintenance, companies can optimize their maintenance strategies, increase the reliability and efficiency of their machines and boost their competitiveness. Together with condition monitoring, predictive maintenance forms a comprehensive strategy that aims to maximize machine reliability and minimize downtime.
Condition monitoring
In contrast to predictive maintenance, condition monitoring refers to the continuous real-time monitoring of the current condition of machines. The aim is to detect immediate deviations from normal operation and initiate preventative measures on time. Various sensors are used to monitor parameters such as temperature, vibration, pressure and noise levels. This data is continuously collected and analyzed. If defined limit values are exceeded, alarms are triggered immediately, enabling a rapid response. By detecting and rectifying problems immediately, preventing serious damage and improving the safety and reliability of machines, condition monitoring makes a significant contribution to reducing unexpected downtime.
Apart from our impedance measurement, there are other methods for monitoring the condition of bearings. Some of these condition monitoring methods are presented below:
Oil analysis
This method examines the quality and composition of the lubricating oil in bearings. By analyzing particles that are distributed in the oil, information about the wear condition of the bearings can be obtained. Changes in the oil composition can indicate potential problems at an early stage. However, this involves machine downtime or specially designed test benches
Vibration
Vibration analysis is one of the most common methods for monitoring bearing conditions. Sensors record the vibrations of machines and analyze the frequency patterns to detect irregularities. The frequency patterns can be used to identify various sources of error such as imbalance, misalignment or bearing defects. The installation effort is reasonable. Nevertheless, damage is only identified once it has already occurred.
Ultrasound
This method uses high-frequency sound waves to detect anomalies and defects in bearings. Ultrasound can detect problems at an early stage that go unnoticed with other methods, such as microcracks or friction. However, this method also only detects damage once it has already occurred and is therefore unavoidable. Furthermore, extensive calibration measures are necessary in advance in order to determine useful values from the ultrasonic frequencies.
Impedance measurement by HCP Sense
The impedance measurement method combines all of the benefits described above. Furthermore, it can also detect damage before it occurs. This is possible because the measurement technology has been trained using machine learning. The impedance analysis can be used to identify the load, wear and lubrication condition, among other things. Another advantage of this method is that only minimal adjustments need to be made to the design of the machine and a teach-in phase for the sensors is not necessary.
Direct comparison of measurement methods
The following diagram illustrates the advantages of our technology. Whereas our solution can be used to prevent bearing damage, the other three methods can only identify the damage in retrospect. Thus, in those cases a bearing replacement can not be avoided.
Summarized comparison between
predictive maintenance and condition monitoring
While both predictive maintenance and condition monitoring aim to increase the reliability and efficiency of machines, they differ in their approach and implementation. Predictive maintenance is future-oriented and focuses on predicting maintenance needs, while condition monitoring is present-oriented and monitors the current condition of machines in real time. Predictive maintenance is based on extensive data analysis and the use of advanced algorithms to predict failures. Condition monitoring uses real-time data and relies on immediate reactions to deviations. With predictive maintenance, maintenance measures are based on predictions in order to prevent unplanned downtime, while with condition monitoring, measures are taken as soon as deviations are detected in order to rectify direct problems.
predictive maintenance and condition monitoring
While both predictive maintenance and condition monitoring aim to increase the reliability and efficiency of machines, they differ in their approach and implementation. Predictive maintenance is future-oriented and focuses on predicting maintenance needs, while condition monitoring is present-oriented and monitors the current condition of machines in real time. Predictive maintenance is based on extensive data analysis and the use of advanced algorithms to predict failures. Condition monitoring uses real-time data and relies on immediate reactions to deviations. With predictive maintenance, maintenance measures are based on predictions in order to prevent unplanned downtime, while with condition monitoring, measures are taken as soon as deviations are detected in order to rectify direct problems.
In reality, both approaches can and should be used together. Condition monitoring can help to identify and resolve immediate problems, while predictive maintenance optimizes the maintenance strategy in the long term and improves efficiency. Both methods focus on generating knowledge and the ability to act from data. Together, they form a comprehensive strategy for maximizing machine reliability and minimizing downtime, which is why they are indispensable for future-oriented production.