ThyssenKrupp had five patents in artificial intelligence during Q2 2024. ThyssenKrupp AG filed patents in Q2 2024 for a method to train a machine-learning module to predict product quality parameters in chemical production plants using sensor data and chronological sequence information. Another patent relates to an aircraft equipped with inspection devices for detecting errors in coking plant equipment, with the ability to convert phototechnical and optical data into a 3D thermal point cloud for analysis. GlobalData’s report on ThyssenKrupp gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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ThyssenKrupp had no grants in artificial intelligence as a theme in Q2 2024.
Recent Patents
Application: Prediction model for predicting product quality parameter values (Patent ID: US20240144043A1)
The patent filed by ThyssenKrupp AG describes a method for training a machine-learning module to predict product quality parameter values for chemical products produced by a production plant. The method involves using training data from sensors in the plant, assigning time shifts between acquisition and production times, and training the module using this data. The process includes cleaning, aggregating, and extracting statistical features from the training data to improve accuracy. Additionally, the method involves providing test data for validation and detecting anomalies in predicted quality parameter values.
Furthermore, the patent details a computer system for implementing the training and prediction model. The system includes a processor and memory storing the prediction model and program instructions. The method involves providing process parameter values acquired by sensors, determining sensor-specific time shifts, and using this data to predict product quality parameter values. Anomalies in predicted values can be detected, and additional training of the machine learning module can be initiated based on predefined criteria. The system also allows for the identification of controllable process parameters that most strongly affect product quality, providing recommendations for adjusting these parameters to achieve target quality values.
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