Predictive Reliability Modelling of an Industrial System
Journal: International Journal of Science and Research (IJSR) (Vol.9, No. 9)Publication Date: 2020-09-05
Authors : Arnab Naskar;
Page : 1361-1364
Keywords : Industry 40; Predictive Analytics; Machine Learning Techniques; Diagnosis; Prognosis; Predictive Maintenance; Reliability; Maintainability; Availability; Recurrent Neural Network;
Abstract
Across the industries, there is a growing need of increased Operational Reliability, Availability and Maintainability of the equipment, which comprises of diagnosis and prognosis of a particular problem. As systems are evolving daily to a new level of sophistication, maintenance of those systems needs a critical approach. Hence, more industries are trying to adapt predictive maintenance policies in their critical area of operations. With the advantage of predictive analytics and machine learning techniques, predictive maintenance is gaining its momentum in different industries. In this paper, a framework of predictive reliability modelling has been discussed, which is a part of predictive maintenance. With the help of various machine-learning models along with Deep Neural Network, one predictive reliability model has been made. This paper also projects a model to calculate Remaining useful life (RUL) by incorporating Recurrent Neural Network (RNN).
Other Latest Articles
- Therapeutic Drug Monitoring of Levetiracetam by High - Performance Liquid Chromatography in Paediatric Epileptic Patients
- A Spectrum of Histopathological Changes in Different Thyroid Lesions and their Correlation with Age and Gender
- Association of Cardiovascular Complications and Gall Stone are Higher among Diabetics - A Comparative Study
- Strategic Methods for Carbon Capture and Correlation for CO2 Emission, GDP and GDP Per Capita
- The Effect of Using Letter Blocks Word Builder Game and Compound Word Game in Teaching Writing to Introvert and Extrovert Students (A Case of Writing Students of English Education Program of STKIP SoE)
Last modified: 2021-06-28 17:11:32