Delivers real-time condition monitoring
When the drive becomes a sensor: We are illustrating how smart Condition Monitoring can be realised that provides extensive information about the "state of health" of machines and plants without the need for any additional, costly sensor technology.
Collecting and interpreting the available data is already a sound way of monitoring the condition of machines. This requires a deeper understanding of the machines and processes so as to generate meaningful information from the "bare" data. Analyses based on Machine Learning (ML) and AI can help identify anomalies faster. In this showcase we show this and visualize the evaluation result with the help of a digital twin:
Model-based approach: Here the measured actual values are compared with those resulting from the assumed mathematical/physical description of the machine. If certain patterns are detected or tolerances are exceeded, this is interpreted as an anomaly.
Data-based approach. A machine learning algorithm learns the normal state of the system based on the measured data, for example motor speed, acceleration, torque, position and current consumption. The real values are compared with this learned description to detect deviations.
The two Condition Monitoring approaches differ not only in terms of their concept. The question as to how this data is evaluated also has different answers. The model-based evaluation usually takes place on the control system because it does not require any significant computing power. ML and AI analyses used for data-based evaluations are normally implemented as a Cloud application.
Lenze's portfolio gives the OEM complete freedom of choice. This includes a number of different three-dimensional PLCs for model-based Condition Monitoring. Data-based evaluation can also be carried out locally if the powerful c750 cabinet controller is used. Alternatively, a route to the Cloud can be provided using the x500 gateway. Combined with the x4 platform, mechanical engineers have a turnkey Cloud solution that covers not only Condition Monitoring but also remote maintenance for the machine and user-friendly Asset Management.
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The trick with this solution is to extract the added information value from data sources that are already available. No additional sensors are needed Lenze provides pre-tested algorithms for various applications and helps mechanical engineers turn their process expertise and knowledge of machines into a Condition Monitoring model that will improve efficiency.