Improving the reliability of engineered systems is a crucial problem in many applications in various engineering fields, such as aerospace, nuclear energy, and water declination industries. This requires efficient and effective system health monitoring methods, including processing and analyzing massive machinery data to detect anomalies and performing diagnosis and prognosis. In recent years, deep learning has been a fast-growing field and has shown promising results for Prognostics and Health Management (PHM) in interpreting condition monitoring signals such as vibration, acoustic emission, and pressure due to its capacity to mine complex representations from raw data. This paper provides a systematic review of state-of-the-art deep learning-based PHM frameworks. It emphasizes on the most recent trends within the field and presents the benefits and potentials of state-of-the-art deep neural networks for system health management. In addition, limitations and challenges of the existing technologies are discussed, which leads to opportunities for future research.