Study on prediction method of fault channel numbers in radar antenna array

Hou, Xiaodong; Yang, Jianxun; Zhang, Yu; Bi, Zengjun

doi:10.1109/phm.2017.8079205

Cited by 1 publication

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“…This can lead to safety accidents causing severe casualties, as well as significant time and financial losses of manufactoring process. Consequently, several studies have been conducted to evaluate and predict the performance of radar antennas [21][22] [23]. In recent, research and development to predict and analyze product defects through correlation analysis between manufacturing process data and product performance using artificial intelligence technologies such as deep learning and machine learning and maximize yield to reduce product disposal costs due to defects.…”

Section: Previous Studymentioning

confidence: 99%

An AutoML-driven Antenna Performance Prediction Model in the Autonomous Driving Radar Manufacturing Process

2023

KSII TIIS

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This paper proposes an antenna performance prediction model in the autonomous driving radar manufacturing process. Our research work is based upon a challenge dataset, Driving Radar Manufacturing Process Dataset, and a typical AutoML machine learning workflow engine, Pycaret open-source Python library. Note that the dataset contains the total 70 data-items, out of which 54 used as input features and 16 used as output features, and the dataset is properly built into resolving the multi-output regression problem. During the data regression analysis and preprocessing phase, we identified several input features having similar correlations and so detached some of those input features, which may become a serious cause of the multicollinearity problem that affect the overall model performance. In the training phase, we train each of output-feature regression models by using the AutoML approach. Next, we selected the top 5 models showing the higher performances in the AutoML result reports and applied the ensemble method so as for the selected models' performances to be improved. In performing the experimental performance evaluation of the regression prediction model, we particularly used two metrics, MAE and RMSE, and the results of which were 0.6928 and 1.2065, respectively. Additionally, we carried out a series of experiments to verify the proposed model's performance by comparing with other existing models' performances. In conclusion, we enhance accuracy for safer autonomous vehicles, reduces manufacturing costs through AutoML-Pycaret and machine learning ensembled model, and prevents the production of faulty radar systems, conserving resources. Ultimately, the proposed model holds significant promise not only for antenna performance but also for improving manufacturing quality and advancing radar systems in autonomous vehicles.

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Section: Previous Studymentioning

confidence: 99%

An AutoML-driven Antenna Performance Prediction Model in the Autonomous Driving Radar Manufacturing Process

2023

KSII TIIS

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Study on prediction method of fault channel numbers in radar antenna array

Cited by 1 publication

References 0 publications

An AutoML-driven Antenna Performance Prediction Model in the Autonomous Driving Radar Manufacturing Process

An AutoML-driven Antenna Performance Prediction Model in the Autonomous Driving Radar Manufacturing Process

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