An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
Volume-3 | Issue-3
Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
Volume-2 | Issue-1
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
Volume-3 | Issue-3
A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
Volume-3 | Issue-3
An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4
An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
Volume-3 | Issue-4
Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
Volume-3 | Issue-4
Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
Volume-3 | Issue-4
Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 2 | Issue - 4 | december 2020
Published
27 January, 2021
Predictive maintenance is the way to improve asset management in every manufacturing industry. While handling advance costlier machinery in the industry, the predictive maintenance knowledge will be essential to protect the machinery before gets degradation performance. Recently, the emergence of business in manufacturing industry deals with good systems, regular intervals maintenance process, predictive maintenance (PdM), machine learning (ML) approaches are extensively applied for handling the health standing of business instrumentation. Now the digital transformation towards I4.0, data techniques, processed management and communication networks; it��s doable to gather huge amounts of operational and processes conditions information generated type many items of kit and harvest information for creating an automatic fault detection and diagnosing with the aim to attenuate period of time and increase utilization rate of the parts and increase their remaining helpful lives. The predictive maintenance is inevitable for property good producing in I40. This paper aims to provide a comprehensive review of the recent advancements of metric capacity unit techniques wide applied to PdM for good producing in I4.0 by classifying the analysis consistent with metric capacity unit algorithms, ML class, machinery and instrumentation used device employed in information acquisition, classification of knowledge size and kind, and highlight the key contributions of the researchers and so offers pointers and foundation for additional analysis. In this research paper we constructed a Random Forest model to predict the failure of the various machine in manufacturing industry. It compares the prediction result with Decision Tree (DT) algorithm and proves its superiority in accuracy and precision.
KeywordsIndustry 40 Analytic Models Machine Learning IIoT Sensors
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