深度学习在故障诊断领域中的研究现状与挑战

现代工业系统已呈现出向大型化、复杂化的方向发展,使得针对工业系统的故障诊断方法遇到一系列的技术难题.近年来,深度学习(deep learning)在特征提取与模式识别方面显示出独特的优势与潜力,将深度学习应用于解决复杂工业系统故障诊断的研究已初现端倪.为此,首先介绍几种典型的基于深度学习方法实现工业系统故障诊断方法;然后对基于深度学习实现故障诊断的主要思想和建模方法进行描述;最后总结和讨论了复杂工业系统故障的特点,并探讨了深度学习在实现复杂工业系统故障诊断方面所面临的挑战,展望了未来值得继续研究的方向.

Deep learning for fault diagnosis: The state of the art and challenge

Modern industrial system has been developed into the direction of more and more larger and complex, which makes the fault diagnosis for industrial system present a series of technical problems. In recent years, deep learning has shown its unique potentials and advantages in feature extraction and pattern recognition. And the application of deep learning to achieve fault diagnosis of complex industrial systems has begun on its initial exploration stage. In this paper, several typical methods based on deep learning have been introduced first, which can be employed to realize the fault diagnosis for industrial system. And then, the main idea and model approach for fault diagnosis based on deep learning have been described. Finally, the characteristics of faults in complex industrial system have been summarized and discussed, and the challenge of deep learning in realizing the fault diagnosis for complex industrial system has also been studied and discussed.