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.

在过去的十年中，深度学习应用在许多不同领域中蓬勃发展，包括计算机视觉和自然语言理解。深度学习蓬勃发展的驱动力是丰富数据的可用性，算法的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视，并且已经收集了大量的状态监视信号，但是深度学习方法在检测，诊断和预测复杂的工业资产的故障方面的应用仍然受到限制。本白皮书全面评估了应用于预测与健康管理（PHM）应用的深度学习领域中的当前发展，驱动因素，挑战，潜在解决方案和未来研究需求。