Hyundai motor company (20240096142). APPARATUS, METHOD AND COMPUTER READABLE STORAGE MEDIUM FOR DIAGNOSING FAULT OF VEHICLE COMPONENT USING ACCELERATION SENSOR simplified abstract
Contents
- 1 APPARATUS, METHOD AND COMPUTER READABLE STORAGE MEDIUM FOR DIAGNOSING FAULT OF VEHICLE COMPONENT USING ACCELERATION SENSOR
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 APPARATUS, METHOD AND COMPUTER READABLE STORAGE MEDIUM FOR DIAGNOSING FAULT OF VEHICLE COMPONENT USING ACCELERATION SENSOR - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
APPARATUS, METHOD AND COMPUTER READABLE STORAGE MEDIUM FOR DIAGNOSING FAULT OF VEHICLE COMPONENT USING ACCELERATION SENSOR
Organization Name
Inventor(s)
Tae Woong Park of Hwaseong-si (KR)
Jae Hun Kim of Hwaseong-si (KR)
APPARATUS, METHOD AND COMPUTER READABLE STORAGE MEDIUM FOR DIAGNOSING FAULT OF VEHICLE COMPONENT USING ACCELERATION SENSOR - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240096142 titled 'APPARATUS, METHOD AND COMPUTER READABLE STORAGE MEDIUM FOR DIAGNOSING FAULT OF VEHICLE COMPONENT USING ACCELERATION SENSOR
Simplified Explanation
The patent application describes an apparatus for diagnosing a fault of a vehicle component using an acceleration sensor, feature extraction unit, and machine learning model.
- The apparatus includes a processor, memory storing programs, and instructions for detecting an acceleration signal, extracting features related to a fault from the signal, and diagnosing the fault based on the extracted features.
- The machine learning model is selected based on a preset evaluation index from multiple models trained on training data sets.
Potential Applications
This technology could be applied in the automotive industry for diagnosing faults in various vehicle components, such as engines, transmissions, or suspension systems.
Problems Solved
This technology helps in quickly and accurately identifying faults in vehicle components, leading to timely repairs and maintenance, which can improve overall vehicle performance and safety.
Benefits
The benefits of this technology include increased efficiency in diagnosing vehicle faults, reduced downtime for repairs, and potentially lower maintenance costs for vehicle owners.
Potential Commercial Applications
A potential commercial application for this technology could be in the development of diagnostic tools for automotive service centers or as an integrated feature in vehicle onboard diagnostic systems.
Possible Prior Art
One possible prior art for this technology could be traditional diagnostic methods used in the automotive industry, which may rely on manual inspection or basic sensor data analysis.
What are the limitations of this technology in real-world applications?
One limitation of this technology in real-world applications could be the need for accurate and reliable training data sets to ensure the machine learning model's effectiveness.
How scalable is this technology for use in different types of vehicles and components?
This technology's scalability for use in different types of vehicles and components may depend on the adaptability of the machine learning model to various fault patterns and the availability of relevant training data sets for each specific application.
Original Abstract Submitted
an apparatus for diagnosing a fault of a vehicle component, may include: a processor; a memory storing one or more programs configured to be executed by the processor; and the one or more programs include instructions for: an acceleration sensor for detecting an acceleration signal; feature extraction unit for extracting features related to a fault of a vehicle component from the detected acceleration signal; and a machine learning model for diagnosing a fault of the vehicle component based on the extracted features, wherein the machine learning model may be a model selected according to a preset evaluation index among a plurality of machine learning models trained based on training data sets.
