Kobe Steel has been granted a patent for a damage estimation device designed for work machines. The device utilizes machine learning to estimate damage based on operational parameters and specific machine configurations, enhancing predictive maintenance and operational efficiency. GlobalData’s report on Kobe Steel gives a 360-degree view of the company including its patenting strategy. Buy the report here.
According to GlobalData’s company profile on Kobe Steel, Ore pre-treatment was a key innovation area identified from patents. Kobe Steel's grant share as of July 2024 was 44%. Grant share is based on the ratio of number of grants to total number of patents.
Damage estimation device for work machine operations
The patent US12065809B2 describes a damage estimation device designed to assess damage in specific components of work machines, such as excavators or cranes. The device comprises several key components, including an operation parameter acquisition unit that gathers operational data, a damage estimation model storage unit that holds machine learning-based models for damage estimation, and an estimation unit that calculates damage parameters based on the acquired operational data. The device is tailored to different specifications of work machines, utilizing a variety of damage estimation models that correspond to specific combinations of boom length, arm length, and tip attachment specifications. Additionally, the device includes a specification parameter acquisition unit to identify the relevant specifications of the work machine being evaluated.
Further enhancing its functionality, the device can also estimate operational parameters such as pressure values and lengths of various cylinders, as well as the operation pressure and slewing angle of the slewing motor. The damage parameters assessed may include strain, stress, and lifespan metrics for the machine's components. The patent also outlines a machine learning device that refines the damage estimation models through training data, ensuring that the models remain accurate and effective. This training process involves minimizing errors between predicted and actual damage parameters, thereby improving the reliability of the damage assessments. Overall, the invention aims to provide a sophisticated tool for monitoring and predicting damage in work machines, potentially leading to better maintenance and operational efficiency.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
