An Efficient Deep Learning Technique for Improved Prognostics and Health Care Management and its Future Challenges

Abstract

Deep learning applications have been thriving over the last decade in many different domains, including computer vision and natural language understanding .Deep learning has attracted intense interest in Prognostics and Health Management (PHM),because of its enormous representing power, automatedfeature learningcapability andbest-in-classperformanceinsolvingcomplexproblems. The drivers for the vibrant development of deep learning have been the availability of abundant data, breakthroughs of algorithms and the advancements in hardware in multiple fields. Improving the reliability of engineered systems is a crucial problem in many applications in variousengineering fields, such as aerospace, nuclear energy, and water declination & Health care industries. This requiresefficient and effective system health monitoring methods, including processing and analyzing massive machinery data to detect a nomalies 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.