Hyundai Motor Company

Samsung Electronics Co., Ltd.

Kia Motors Corporation

Kia Corporation

Industry-University Cooperation Foundation Hanyang University

Seoul National University

Kookmin University

In-Soo Jung

Deok-Hyung Lee

Si-Young Choi

Byeong-Chan Lee

Dong-Chul Lee

Yong-Hoon Son

Seung-Hyun Lee

Jae-Min Jin

Jong-Wook Lee

Jong-Ryeol Yoo

Young-Eun Lee

Yu-Gyun Shin

Joon-Hyuk Chang

Sun-Ghil Lee

Chul-Sung Kim

Jin-Hwa Heo

Taek-Jung Kim

Sung-Min Kim

Dong-Won Kim

Kyoung-hwan Yeo

Dong-Chan Lim

Byeong-Yun Nam

Yong-Joo Lee

Kyoung-Doug Min

Seung-Il Lee

Min-Ho Han

Bo-Yong Kim

Young-Ju Noh

Soo-Ik Jang

Sung-Hwan Shin

Kyoung-Jin Noh

Dong-Yeoup Jeon

An embodiment method of predicting vehicle clutch life based on real-time judder diagnosis includes storing to a cloud server actual road driving data measured by an actual road driving information collection device while a vehicle travels, extracting a judder occurrence entry condition by downloading the actual road driving data from the cloud server and checking judder occurrence information in a clutch life prediction device, calculating a judder index by checking judder index information from the actual road driving data in the clutch life prediction device, predicting a clutch useful life of a double clutch transmission (DCT) using a clutch useful life prediction curve for the judder index, and generating a maintenance notification at a time of clutch maintenance.

Method of predicting vehicle clutch life based on real-time judder diagnosis and system for diagnosing vehicle judder in real time

A vehicle judder diagnostic method using artificial intelligence applied to a mobile-based GDS according to the present disclosure is characterized in that the mobile-based GDS samples a plurality of sensor signals of a sensor mounted in a vehicle in a vehicle during operation in a judder evaluation mode to quickly, separately diagnose whether the judder phenomenon of the vehicle is a geometric judder or a friction judder by mounting a deep neural network (DNN) model, developed by the trial and error process of a DNN by using the plurality of sensor signals of a test vehicle mounted with a double clutch transmission (DCT), as a judder determination artificial intelligence modelin the mobile-based GDS.

Vehicle judder diagnostic method using artificial intelligence and mobile-based GDS

A state diagnosis apparatus of a moving system part is provided and includes a sensor unit that measures and collects state data of a moving system part relevant to an engine. A graphic controller primarily diagnoses classification data generated by classifying the state data according to a predetermined filtering condition as a normal state or an abnormal state using a Deep Learning model.

State diagnosis apparatus and method of moving system part

Embodiments of the present disclosure relate to an AI-based noise generation diagnosis apparatus and method of diagnosing noise of PE components for an electric vehicle, in which a plurality of microphones are disposed in a vehicle interior, and a calculation unit measures positions of noise sources from any one or more among the PE components comprising a main motor, a decelerator, an inverter, and an auxiliary motor from front, rear, and upward directions of the microphone, and at the same time, estimates a noise transmission position where the noise is transmitted.

AI-based noise generation diagnosis apparatus and method of PE components for electric vehicle

An automation method of an artificial intelligence (AI)-based diagnostic technology for equipment application includes receiving one or more pieces of data among vibration data, noise data, and controller area network (CAN) data, a data input processing operation of trimming the input data, an operation of extracting features from the trimmed data, setting a setting value of a hyper-parameter with respect to the one or more pieces of data thereamong, and generating a total of N models to include both of machine learning (ML) and deep learning (DL) as N individual models and generating ensemble prediction model structures for the N individual models. As a parameter updating is being proceeded due to the hyper-parameter so as to minimize values of cost functions of the N individual models, a reward for model accuracy performance is optimized and the ensemble prediction model structures of the N individual models change.

Automation method of AI-based diagnostic technology for equipment application

An information sharing platform of providing bidirectional vehicle state information between a driver and a vehicle, the information sharing platform may include a communication controller which collects measured data and vehicle Controller Area Network (CAN) information by sensors installed to components capable of diagnosing a vehicle state; and a graphic controller which provides a driver with diagnosis result output information that is generated based on a predetermined selection criterion among the components through Deep Learning based diagnosis using big data having the collected data.

Information sharing platform and method capable of providing bidirectional vehicle state information and system having information sharing platform

A method for controlling a tone of an electric vehicle (EV) based on motor vibration may include: calculating an order component from a vibration signal of an EV motor of an electric vehicle, extracting a first order component with the greatest linearity for motor output torque among the calculated order component, then calculating an order frequency by transforming revolutions per minute (RPM) of the EV motor into frequency, setting an EV mode tone by applying a vibration level of the first order component to a level of the order frequency to be output and rearranging the order component, and outputting the set EV mode tone, and may apply an LMS filter algorithm, FFT/IFFT transforms, and an order tracking algorithm in extracting the first order component.

Method for controlling tone of electric vehicle based on motor vibration

A device for AI-based vehicle diagnosis using CAN data may include an engine; a vibration sensor mounted in an engine compartment in which the engine is mounted and configured for detecting a vibration signal; and a controller area network (CAN) communicating with one or more of an environmental condition, a vehicle status, an engine status, and an engine control parameter, wherein data preprocessing from the vibration sensor and the CAN is performed to determine features in which correlation between vibration data (dB) exceeding a threshold value of irregular vibrations being generated by the engine and the CAN data is equal to or greater than 90%.

Method of AI-based vehicle diagnosis using CAN data and device thereof

A big data-based driving information provision system may include a sensor configured to measure and collect state monitoring data of an engine, vehicle monitoring data, and vibration data; an engine electronic control unit (ECU) configured to generate a combustion characteristic index (CCI) data of the engine; and a graphic controller configured to generate a primary deep learning model which classifies the big data including the state monitoring data, the vehicle monitoring data, the vibration data, and the CCI into at least two categories.

Big data-based driving information provision system and method thereof

A method for diagnosing a problematic noise source based on big data information include: measuring noise data of a powertrain of a vehicle by using a real-time noise measurement device, and converting the noise data into a signal that can be input to a portable device for diagnosing the problematic noise source through an interface device; analyzing a noise through a deep learning algorithm of an artificial intelligence on a converted signal, and diagnosing the problematic noise source as a cause of the noise; and displaying the cause of the noise by outputting a diagnostic result as the problematic noise source, and transmitting the diagnostic result to the portable device.

Method and device for diagnosing problematic noise source based on big data information

A noise data artificial intelligence learning method for identifying the source of problematic noise may include a noise data pre-conditioning method for identifying the source of problematic noise including: selecting a unit frame for the problematic noise among noises sampled with time; dividing the unit frame into N segments; analyzing frequency characteristic for each segment of the N segments and extracting a frequency component of each segment by applying Log Mel Filter; and outputting a feature parameter as one representative frame by averaging information on the N segments, wherein an artificial intelligence learning by the feature parameter extracted according to a change in time by the noise data pre-conditioning method applies Bidirectional RNN.

Noise data artificial intelligence apparatus and pre-conditioning method for identifying source of problematic noise

A method of controlling a tone of an electric vehicle (EV) includes calculating order components from vibration signals of a rotating motor of the EV, extracting an Norder component having a determination coefficient Rwhich represents linearity with respect to an output torque of the motor and is greater than or equal to a predetermined value, converting an RPM of the motor into a frequency and calculating an order frequency, arranging the order components by applying a vibration level of the Norder component to a level of the order frequency to be output and setting an EV mode tone, and in a deceleration of the EV, determining a deceleration condition and applying the vibration level of the Norder component to level control of the order frequency.

Method of controlling tone of electric vehicle based on motor vibration in deceleration or regenerative braking conditions

A mechanical diagnosis method for combustion abnormality using engine noise includes: calculating an Energy K and a Loudness standard deviation index (N) with Kurtosis analysis by a diagnosis controllerfrom noise data measured together with a signal component by rotation excitation of an engine; calculating a plurality of order frequency peak order component values by a Modulation Frequency Transform; and distinguishing a cylinder where abnormal combustion occurs from a cylinder where normal combustion occurs by applying a predetermined threshold to these calculated values, thereby classifying, by Modulation Frequency analysis, problem samples of the rotation excitation and combustion excitation influence of the engine in which the abnormality state determination of the engine has been difficult only with energy distribution while overcoming the limitation of Kurtosis analysis.

Mechanical combustion abnormality diagnosis determination method and combustion abnormality diagnosis system using engine noise

An injector failure cylinder diagnosis method based on signal deviation of an injector failure diagnosis system measures, by a controller, noise/vibration signals due to the combustion of an engine, separates an injector abnormal frequency band signal as an injector signal from the noise/vibration signals, divides cylinder number time series data, which use a signal maximum value of the noise/vibration signals as a cylinder #1, into segments, extracts a feature vector after confirming a segment number of the cylinder #1 with a vibration reduction signal of the noise/vibration signals re-measured using one of the cylinders as an idle cylinder and sorting it by injector causing vibration time series data, and confirms a failure injector with the feature vector, thereby independently diagnosing whether the injector for each cylinder is normal or abnormal considering the deviation of the noise/vibration signals between the injectors for each cylinder from the measured noise and vibration signals.

Growing community of inventors

Gyeonggi-do, South Korea

In-Soo Jung