Predictive Maintenance of Mining Machinery Using Machine Learning Approaches

2019 ◽  
Author(s):  
Amir Taghizadeh Vahed ◽  
Behzad Ghodrati ◽  
Nuray Demirel ◽  
Morteza Hosseini Yazdi
Keyword(s):  
Machine Learning ◽  
Predictive Maintenance ◽  
Learning Approaches ◽  
Mining Machinery

scholarly journals Modeling System Based on Machine Learning Approaches for Predictive Maintenance Applications

2020 ◽  
Author(s):  
João Pedro Serrasqueiro Martins ◽  
Filipe Martins Rodrigues ◽  
Nuno Paulo Ferreira Henriques
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Test Bench ◽  
Industrial Sector ◽  
Predictive Maintenance ◽  
Learning Approaches ◽  
Learning Capacity ◽  
Overall Performance ◽  
The Evolution Of Industry ◽  
Self Learning

Industry 4.0 must respond to some challenges such as the flexibility and robustness of unexpected conditions, as well as the degree of system autonomy, something that is still lacking. The evolution of Industry 4.0 aims at converting purely mechanical machines into machines with self-learning capacity in order to improve overall performance  and contribute to the optimization of maintenance. An important contribution of Industry 4.0 in the industrial sector is predictive maintenance and prescriptive maintenance. This article should be analysed as a methodology proposal to implement an automatic forecasting model in a test bench for the recognition of a machine’s failure and contribute to the development of algorithms for preventive and descriptive maintenance. Keywords: Industry 4.0, Artificial intelligence, Machine learning, Predictive maintenance, Prescriptive maintenance

scholarly journals Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5967
Author(s):  
Mohamed Benbouzid ◽  
Tarek Berghout ◽  
Nur Sarma ◽  
Siniša Djurović ◽  
Yueqi Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Wind Power ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Critical Evaluation ◽  
Health Assessment ◽  
Predictive Maintenance ◽  
Learning Approaches ◽  
Wind Power Systems

Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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