Learning to Detect Local Overheating of the High-Power Microwave Heating Process With Deep Learning

As a new kind of heating technology, microwave heating could replace traditional heating methods, because it has the advantages of high efficiency, no secondary pollution, and rapid heating. But the microwave heating process, which involves complex coupling between time-varying electromagnetic field and thermal field, is extremely complicated. At this point, the heated medium may produce local overheating. Worse, it may cause unexpected safety accidents, such as burning and even explosion. However, the temperature variation during the period of microwave heating could barely be obtained. In order to solve the problem of local overheating, this paper proposes a deep learning algorithm based on multidimensional data to construct an anomaly detection model for detecting local overheating. The algorithm consists of convolutional neural networks (CNNs) and unsupervised learning method named isolation forest algorithm (IFA). First, CNNs is utilized to extract features of the data collected from a WXD15S microwave heating system. Then, IFA detects the local overheating. Compared with the algorithm with common model, experiment results show that the proposed algorithm owns better measurement performance and higher precision.