You need to detect fluctuations in your plant’s behavior early in order to identify critical process parameters. With the help of data analysis, our engineers and specialists detect deviations from the expected process data and locate potential for improvement. By integrating big data and machine learning, we now have new anomaly detection technologies – supported by neural networks and state-of-the-art robotics – to amplify the advanced pattern recognition used in the past.
Maintenance is no longer merely about reacting to problems, it’s now about taking action. And that will considerably lower your costs, even as your availability rises. Monitoring and diagnostic systems precisely monitor component performance, detecting significant deviations between the incoming data and the benchmark values. Key factors are filtered out of the performance data that automatically illustrate the actual operating conditions of the plant and how these conditions relate to other incoming data. The data is analyzed and any anomalies detected. This process acts as an early warning system for changes to the health of components and an important factor for predictive maintenance and performance optimization.