Predictive Maintenance
Predictive Maintenance is one of the central applications within Industrie 4.0.
Analysing vast amounts of data collected from a network of connected sensors installed in production facilities, enables companies to make reliable predictions on how the conditions of a machine or facility will develop over time and when maintenance will be required. The condition of production facilities, however, exerts a direct influence on the quality of the final product.
Therefore a close nexus between Predictive Maintenance and Predictive Quality can be established. Last not least, these new technological scenarios provide for opportunities of developing innovative service models as well, enabling machine manufacturers to set new standards with regard to customer relationship managemen.
Model and Infrastructure
The prediction model is at the core of any predictive maintenance scenario: modelling starts with identification of relevant parameters, such as temperature, pressure, vibration or visual characteristics. The basis is the historical data. By applying the model on historical data, the model can be tested for aptitude an accuracy of predictions can be fine-tuned. Machine Learning technology can support this process, making the models continuously “smarter” and steadily increasing their prognostic power.
As a prerequisite, the IT infrastructure as well as the networks must be capable to handle high volumes of data. Internet of Things and Big Data are the main keywords in this regard. The harmonization of different types of data is of crucial importance to uncover hidden correlations between measured values and the propensity of defect. In this context Cloud technology offers some central advantages such as high scalability and ubiquitous accessibility via Internet.