utc phm delta prognostic health monitoring

#Utc phm delta prognostic health monitoring Offline#

Zhao R et al (2019) Deep learning and its applications to machine health monitoring. Lee GY, Kim M, Quan YJ et al (2018) Machine health management in smart factory: a review. Lei Y, Li N, Guo L, Li N, Yan T, Lin J (2018) Machinery health prognostics: a systematic review from data acquisition to RUL prediction. Khan S, Yairi T (2018) A review on the application of deep learning in system health management. Wang J, Ma Y, Zhang L, Gao RX, Wu D (2018) Deep learning for smart manufacturing: methods and applications. Įmmert-Streib F, Yang Z, Feng H, Tripathi S, Dehmer M (2020) An introductory review of deep learning for prediction models with Big Data. ĭargan S, Kumar M, Rohit M, Gulshan A (2019) A survey of deep learning and its applications: a new paradigm to machine learning. Kaur P, Kumar R, Kumar M (2019) A healthcare monitoring system using random forest and internet of things (IoT). īansal M, Kumar M, Kumar M (2020) 2D Object recognition techniques: state-of-the-art work. Īrora M, Kumar M, Garg KN (2018) Facial emotion recognition system based on PCA and gradient features. Gupta S, Thakur K, Kumar M (2020) 2D-human face recognition using SIFT and SURF descriptors of face’s feature regions.

#Utc phm delta prognostic health monitoring Offline#

Kumar M, Jindal MK, Sharma RK et al (2020) Performance evaluation of classifiers for the recognition of offline handwritten Gurmukhi characters and numerals: a study. Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Conclusionsįurther research works are required to encourage industrial field failure data acquisition and to unveil the black-box nature of deep learning algorithms to make an intelligible prognostic platform with automated hyper-parameter selection to instigate industrialists to set about an autonomous machinery health management system. Hyper-parameter optimization, architecture design, and data training of deep learning algorithms are still challenging and unpredictable, which can pull back industrialists from implementing intelligent health management of industrial machinery. The mandate to have large-size machinery failure data for training deep learning algorithm can be regarded as a major limitation. Finally, the paper provides the state-of-art contribution of various researchers on implementing deep learning approaches and optimized hyper-parameter selection for accurate machinery health diagnostics and prognostics. Then, outline the characteristics of a few benchmark time-series machinery failure datasets available in open repositories that are widely utilized in the literature for validating deep learning algorithms. First, to address the most popularly used deep learning architectures and their significance in machinery health prognostics. The review show-up the recent research works focused on the implementation of deep learning algorithms for developing an intelligent predictive maintenance model for future industries. To motivate industrial practitioners for developing a machine degradation data acquisition system and employ deep learning model training algorithms for machinery health management thus fit themselves into an Industry 4.0 era. However, the implementation of deep learning algorithms for machinery health prognostics has enormous challenges, which have a certain scope for discussion in the present era of Industry 4.0. Deep learning is a promising computational tool for machinery health prognostics. The machinery health failure/degradation data acquired as time-series sensor signals are analyzed for fault detection, prediction, and maintenance decision-making. An intelligent predictive health management paradigm for industrial machinery is inevitable in Industry 4.0.