论文标题
潜在的，挑战和未来的预测和健康管理应用中的深入学习方向
Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications
论文作者
论文摘要
在过去的十年中，深度学习应用在许多不同的领域中一直在蓬勃发展，包括计算机视觉和自然语言的理解。深度学习充满活力的驱动因素是大量数据，算法突破以及硬件的进步。尽管已经对复杂的工业资产进行了广泛的监控并收集了大量的状况监测信号，但深度学习方法用于检测，诊断和预测复杂工业资产的故障的应用已受到限制。当前的论文对应用于预测和健康管理（PHM）应用的深度学习领域的当前发展，驱动因素，挑战，潜在的解决方案以及未来的研究需求进行了详尽的评估。
Deep learning applications have been thriving over the last decade in many different domains, including computer vision and natural language understanding. The drivers for the vibrant development of deep learning have been the availability of abundant data, breakthroughs of algorithms and the advancements in hardware. Despite the fact that complex industrial assets have been extensively monitored and large amounts of condition monitoring signals have been collected, the application of deep learning approaches for detecting, diagnosing and predicting faults of complex industrial assets has been limited. The current paper provides a thorough evaluation of the current developments, drivers, challenges, potential solutions and future research needs in the field of deep learning applied to Prognostics and Health Management (PHM) applications.
