Recent years have seen a substantial increase in the application of machine learning (ML)
for automated analysis of nondestructive examination (NDE) data. One of the applications of …

This work studies methods for efficient extraction and selection of features in the context of a
decision support system based on neural networks. The data comes from ultrasonic testing …

This work investigates the application of extreme learning machine, a fast training neural
network model, for an ultrasound nondestructive evaluation decision support system. A …

We investigated and compared various algorithms in machine learning for anomaly
assessment with different feature analyses on ultrasonic signals recorded by sensor …

The identification of welding defects in Non-Destructive Testing (NDT) data is a complex
task, achieved by experienced analysts. Recent deep learning implementations enable the …

Ultrasound nondestructive testing is commonly applied in industry to guarantee structural
integrity. HP steel pyrolysis furnaces are used in petrochemical industry for lightweight …

Ultrasound testing has been widely applied for material characterization. The method
accuracy usually relies on operator experience, considering this, an automatic decision …

The morphology of welding pool (fusion zone and heat affected zone) includes a variety of
microstructural constitutions (the formation of new boundaries, pores, and textures) that can …

On‐time water pump malfunction, thermostat sticking, fan defect, and radiator clogging
severity estimation in a vehicle cooling system prevents system breakdown and saves repair …

通过定义超声回波相关时频特征量, 提出基于超声回波时频特征量的焊点质量自动评价方法.
通过数据统计分析, 建立了便于在线自动检测的量化焊点质量评价标准, 实现了汽车点焊质量的 …