Predictive maintenance uses various technologies to monitor the condition of machines and thus predict maintenance measures, for example. Here are just a few of the technologies:
Sensors: Vibration sensors, temperature sensors, pressure sensors and other specialized sensors are used to collect relevant data in order to continuously monitor the condition of the machine.
Data analysis and processing: Analysis tools and algorithms are used to evaluate the collected data. Data analysis methods such as machine learning, artificial intelligence, and statistical models enable the recognition of patterns, the prediction of failures, and the identification of maintenance needs.
Cloud computing: Cloud platforms provide the ability to store, process, and analyze large amounts of data. The use of cloud computing enables efficient data processing and analysis, regardless of the size and complexity of the data collected.
Internet of Things (IoT): By integrating IoT devices and sensors, machine data is captured in real time and transmitted to central systems. This ensures continuous monitoring and analysis of the machine condition as well as a quick reaction to deviations.
Big data analysis: Using big data analysis methods, large amounts of data are analyzed to identify patterns or trends. Combining predictive maintenance data with other enterprise data provides additional insights that can be used to improve prediction accuracy.