DataXpert

DataXpert is designed as a complete platform for developing and managing condition monitoring and expert systems that provide real-time recommendations and notifications. DataXpert consists of three main modules, BIXpert, AIXpert and RulesXpert, and is coupled with a powerful time series database. Each platform tool is modular and can be extended to include additional functions via plugins, e.g., new data connectors, new visualization types, modules with new function blocks, etc. Thanks to the use of web technologies, the platform can be deployed in the cloud and on a server close to the plant.

The platform provides software solutions for:

• Data acquisition, storage, description, validation, and replication (data lake).
• Logic/model generation and testing (RulesXpert Designer, AIXpert)
• Logic/model execution (RulesXpert Scheduler)
• Data visualization (BIXpert)

DataXpert is an integral part of the Smart Maintenance program, and is the asset optimization platform at SMS digital for steelmaking processes. It is a combination of three software modules that enable the optimization of industrial processes through digitalization for process engineers as well as optimization of equipment maintenance for maintenance engineers. Digitalized process and maintenance knowledge form the basis of process and maintenance optimization.

Machine learning enables DataXpert to draw better conclusions in data-rich decision areas and helps it to assess equipment health and detect complex anomalies from real-time sensor data. Thanks to machine learning, equipment failures can be predicted or, at the very least, a probability of failure assessed days in advance. It uncovers characteristics and sensor-level factors that cause a failure. Finally, it reduces huge volumes of data down to a specific amount of relevant information. By providing real-time insights into the plant’s condition and process deficiencies as well as predicting machine failures, maintenance measures can be taken proactively and production can be rescheduled and productivity increased. Own models for process predictions or predictive maintenance can be trained intuitively.