Optimized Performance through Sentian ICA

Industrial AI is not only about predictive maintenance. It is also supposed to make your machines and plants run closer to optimum. It is often found that there are a substantial performance differences between different teams and machine operators - with varying levels of waste and output quality.  Using advanced algorithms taking in data from control systems, automation systems, machines, ambient sensors, KPI’s and operators Sentian can improve machine and plant performance.

Sentian’s solution is an advanced deep learning and multi AI solution for pattern recognition, predictions and optimization.

Using the latest in AI we can find the formulas, actions and expertise to improve operations. The solution is fast to implement, will learn quickly and will offer fast ROI. It can be implemented on single machines, plant wide and even across plants.

• Single or fleet of machines

• Integrate to existing systems

• Cloud and Edge

• Increase Performance by reducing waste, improve quality and increase production

• Improved control of operations

Example: Capturing Manufacturing Expertise

Experienced machine operators and production managers have often considerably better output volumes, higher quality and less downtime than inexperienced operators. Use ICA to improve performance for all operators.

Example: Control system optimization

Complex models have often been created when there was less data available and without modern AI. Using more data and machine Learning these models can often be optimized.


• The deep learning and multi AI platform handles multiple types of data such as machine data, weather, new sensor data and other sources

• Automatic learning

• Localized high performance solution for real-time response and cloud integration

• Generalization of models for multi site implementation

Control and Automation systems

Industry 4.0 and industrial AI has for a long time been focused on predictive maintenance and anomaly detection. However that only makes machines more available. The next step is to make them operate closer to optimum. There are several aspects to this. At the core control and automation systems have a fundamental limitation of being programmed by people. Machine Learning can in most cases improve the models and target values, that are used in production today. Another level is people. Expert machine operators can have a much higher performance than others operators.

The same logic goes for full shifts of larger production sites where some teams perform much above the others. Using modern AI we can improve the performance of the machines and plants. This may also be key for corporations with an aging workforce that find it hard to replace the employees that are retiring.

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