Indian Journal of Science and Technology
Year: 2023, Volume: 16, Issue: Special Issue 2, Pages: 70-75
Sankhapani Neog1*, Kaushik Das1
1Department of Computer Science and Engineering, Institute of Engineering and
Technology, Institute of Engineering and Technology, Dibrugarh University, Assam, India
Email: [email protected]
Received Date:23 March 2023, Accepted Date:26 June 2023, Published Date:02 November 2023
Objectives: The objective of the research is to study the existing predictive maintenance solutions, find more efficient solutions that can help industries improve their efficiency, and study the advantages of decentralized supply chains. The efficiency of predictive maintenance systems can be improved by multiple sensor inputs. Also, a user-defined safe limit value system will improve the efficiency of the predictive maintenance system. Methods: Time-series data is used in our machine-learning-based forecasting operations on Google Collaboratory. Long Short-Term Memory (LSTM) and Prophet models are used in our study for time-series forecasting. Findings: For the predictive maintenance system, multiple sensor inputs will lead to more efficient results. Instead of predicting a single sensor value, we can take the idea from Google's weather forecast to predict future assets' health by taking multiple inputs. Novelty: Predicting maintenance systems lack the feature of a user input for safe or unsafe state. By giving a user input for safe or unsafe state the system will understand the failure. Improvements can be done on predictive maintenance systems by giving multiple sensor inputs.
Keywords: Blockchain, Corrosion, Maintenance, Machine Learning, Predictive maintenance
© 2023 Neog & Das. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)
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