Predictive maintenance takes machine service to a new level. Using weMonitor, machine manufacturers and machine operators can keep an eye on all relevant machine data. Machine and plant monitoring are based on the latest internet technologies and intelligent data processing. weMonitor is thus the perfect basis for monitoring of machines and systems using condition monitoring or predictive maintenance. In order to meet the individual requirements of the user, the software is individually configured and adapted. This ensures optimum machine and plant monitoring at all times.

In order to represent the entire system, weMonitor has future-oriented predictive maintenance functions. To implement predictive maintenance cost-efficiently, it is necessary to identify possible problems and malfunctions at an early stage. Therefore, machine learning algorithms are integrated which describe and analyze machines and plants with complex threshold values as a coherent system. In this way, machine learning enables knowledge to be generated independently, based on the experience gained. The predictive maintenance and repair reaches a new performance level, since the analysis of very large amounts of data is automated and solutions for new and previously unknown problems can be identified. In addition to machine learning methods that learn the vibration behavior of systems or components, we have also developed multidimensional anomaly detection methods whose learned patterns can be executed on our IoT Gateways. A wide range of options for recording and analyzing sensor data allows the weMonitor user to examine the system behavior to be monitored more closely during the development/commissioning phase. Predictive maintenance in conjunction with machine learning thus not only serves to increase plant availability, but also effectively reduces maintenance costs by predicting machine downtimes and defects.

An essential component of condition monitoring is the display of data and sensor values in real time. Possible deviations are detected while further data is filtered out for more detailed analysis. The basis for condition monitoring is therefore precisely defined and individual threshold values. A prerequisite for successful data acquisition are machines and systems equipped with sensors in order to evaluate parameters such as vibrations, temperatures or other process and performance parameters both online and offline. The operator can individually adapt the module to his needs and define precise limit values, which trigger automatic alerts in the event of exceeding or falling short and generate alarm messages that are clearly visualized in the service platform. weMonitors condition monitoring is therefore ideally suited for optimum monitoring.

weMonitor not only increases the net operating time of the machines, but also increases profit and satisfaction among machine operators and users.

Five arguments for weMonitor

- Predictive maintenance of machines and plants ensures long-term reduction of maintenance costs

- Predictive maintenance using machine learning prevents future machine failures and leads to significantly higher system availability

- Extensive performance spectrum for continuous or discontinuous monitoring

- Clear user interface with an informative display and analysis of alert messages

- Support of high sensor sampling rates as well as real-time monitoring of machines and systems distributed worldwide

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