Predictive maintenance is one of the core applications in Industry 4.0

By analyzing vast amounts of data collected from a network of connected sensors installed in production systems, it enables companies to make reliable predictions of how the condition of a machine or system will develop over time and when maintenance is required. However, the conditions of the production systems have a direct influence on the quality of the final product.

Therefore, it is possible to establish a very close link between predictive maintenance and predictive quality. Finally, these new technological scenarios offer opportunities for the development of service models, allowing machine manufacturers to set new standards for managing customer relationships.

Model and infrastructure

The predictive model is at the heart of all predictive maintenance scenarios: the modeling starts with the identification of relevant parameters, such as temperature, pressure, vibration or visual characteristics. The basis is in the historical data. By applying the model to the historical data, the model can be tested to identify its capabilities and the forecast accuracy can be adjusted. Machine Learning technology can support this process, making the model more and more “smart” and increasing its predictive power.

As a prerequisite, the IT infrastructure and networks must be able 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 discover hidden correlations between measured values and the propensity to defect. In this context, cloud technology offers some central advantages such as high scalability and global accessibility via the Internet.


With JOULEHUB EXPERIENCE, JOULEHUB offers a production operations management platform that integrates all shopfloor equipment making data accessible to a wide range of applications. Through JOULEHUB EXPERIENCE, condition monitoring data from the equipment can be easily analyzed for predictive maintenance purposes.

JOULEHUB also supports industrial companies through tailor-made innovation and service design, developing new business models based on the latest technologies and management consulting, to successfully transform companies in line with the paradigms of Industries 4.0.

JOULEHUB can leverage extensive knowledge and experience in all relevant areas, such as Machine Learning, Cloud Computing, Data Science and IoT and Architecture.

Predictive maintenance in practice

The higher the quality requirements for a product, the less tolerable the deviations in production parameters become. A metallurgical production site producing high-precision components for the automotive, pharmaceutical, chemical or medical industries can predict material defects with high accuracy by closely monitoring production conditions and the status of production facilities.

These analyses allow rapid adjustments to ongoing production processes and to suspend subsequent production steps to save energy if necessary. On the other hand, the correlations between machine performance and defect susceptibility become visible. Maintenance can be scheduled accordingly to ensure adherence to previously defined thresholds. This proactive maintenance avoids unplanned and costly downtime and contributes to predictive quality assurance.

The main advantage for the manufacturing industry is superior overall equipment efficiency (OEE):

  • Increased availability due to more efficient maintenance planning;
  • Improved product quality through faster identification and removal of quality deviations;
  • Reduce warranty cases and rejects through improved product quality.