scholarly journals Diagnosing Automotive Damper Defects Using Convolutional Neural Networks and Electronic Stability Control Sensor Signals

2020 ◽  
Vol 9 (1) ◽  
pp. 8 ◽  
Author(s):  
Thomas Zehelein ◽  
Thomas Hemmert-Pottmann ◽  
Markus Lienkamp
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Defect Detection ◽  
Stability Control ◽  
Driving Safety ◽  
Electronic Stability Control ◽  
Fast Fourier Transformation ◽  
Vehicle Acceleration ◽  
In Series ◽  
Structured Analysis

Chassis system components such as dampers have a significant impact on vehicle stability, driving safety, and driving comfort. Therefore, monitoring and diagnosing the defects of these components is necessary. Currently, this task is based on the driver’s perception of component defects in series production vehicles, even though model-based approaches in the literature exist. As we observe an increased availability of data in modern vehicles and advances in the field of deep learning, this paper deals with the analysis of the performance of Convolutional Neural Networks (CNN) for the diagnosis of automotive damper defects. To ensure a broad applicability of the generated diagnosis system, only signals of a classic Electronic Stability Control (ESC) system, such as wheel speeds, longitudinal and lateral vehicle acceleration, and yaw rate, were used. A structured analysis of data pre-processing and CNN configuration parameters were investigated in terms of the defect detection result. The results show that simple Fast Fourier Transformation (FFT) pre-processing and configuration parameters resulting in small networks are sufficient for a high defect detection rate.

scholarly journals Deep Learning Convolutional Neural Networks for Pharmaceutical Tablet Defect Detection

2020 ◽  
Vol 26 (S2) ◽  
pp. 1606-1609
Author(s):  
Xiangyu Ma ◽  
Nada Kittikunakorn ◽  
Bradley Sorman ◽  
Hanmi Xi ◽  
Antong Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Defect Detection ◽  
Pharmaceutical Tablet

Wafer Map-based Defect Detection Using Convolutional Neural Networks

2018 ◽  
Vol 44 (4) ◽  
pp. 249-258
Author(s):  
Jaesun Park ◽  
Junhong Kim ◽  
Hyungseok Kim ◽  
Kyounghyun Mo ◽  
Pilsung Kang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Defect Detection

Application of Deep Learning Convolutional Neural Networks for Internal Tablet Defect Detection: High Accuracy, Throughput, and Adaptability

2020 ◽  
Vol 109 (4) ◽  
pp. 1547-1557 ◽  
Author(s):  
Xiangyu Ma ◽  
Nada Kittikunakorn ◽  
Bradley Sorman ◽  
Hanmi Xi ◽  
Antong Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Defect Detection ◽  
High Accuracy

scholarly journals Automating Visual Inspection with Convolutional Neural Networks

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Sreerupa Das ◽  
Christopher D Hollander ◽  
Suraiya Suliman
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Defect Detection ◽  
Surface Defects ◽  
Training Data ◽  
Subject Matter Experts ◽  
Data Set ◽  
Analysis Task ◽  
Aerial Vehicle ◽  
Initial Results

Convolutional Neural Networks (CNNs) have become the recent tool of choice for many visual detection tasks, including object classification, localization, detection, and segmentation. CNNs are specialized neural networks composed of many layers and specifically designed to analyze grid-like data, e.g. images. One of the key features of a CNN is its ability to automatically detect important features within an image (e.g. edges, patterns, shapes); prior to CNNs, these features had to be manually engineered by subject matter experts. Inspired by the significant achievements and success that CNNs have experienced in the domain of computer vision, we examine a specific convolutional neural network (CNN) architecture, U-Net, suited for the task of visual defect detection. We identify and discuss situations for the use of this architecture in the specific context of external defect detection on aircraft and experimentally discuss its performance across a dataset of common visual defects. One requirement of training Convolution Networks on an image analysis task is the need for a large image (training) data set.  We address this problem by using synthetically generated images from computer models of jets with varying angles and perspectives with and without induced faults in the generated images.  This paper presents the initial results of using CNNs, specifically U-Net, to detect aerial vehicle surface defects of three categories.  We further demonstrate that CNNs trained on synthetic images can then be used to detect faults in real images of jets with visual damages.  The results obtained in this research, indicate that our approach has been quite effective in detecting surface anomalies in our tests.

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