Predictive maintenance for wind turbine diagnostics using vibration signal analysis based on collaborative recommendation approach

Durbhaka, Gopi Krishna; Barani, S.

doi:10.1109/icacci.2016.7732316

Cited by 29 publications

(15 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some other authors [64][65][66][67]81,82,[84][85][86][87][88]90,116,117] studied RF technique. In the last years, use of SVM technique has received attention from authors [14,21,48,56,66,79,[93][94][95][96][97][98][99][100]102,107,111,115,121,124]. Some authors [21,56,82,112,120,124] have given attention to GBM technique.…”

mentioning

confidence: 99%

“…Based on the literature, it is observed that RF algorithm is the most extensively applied ML technique for PdM, as it has been applied on various industrial equipment, components, or systems, including rotating machineries, turbofan engines, rotor bar-LS-PMSM, aircrafts, production lines, semiconductors, industrial pumps, cutting machines, refrigeration systems of supermarket, hard drive disk (HDD), wind turbine, vending machines, and many others. However, authors usually focused on CNC Machines [46,78,79], wind turbine [10,89,99,124], aircraft [67,94,97,122,123], and semiconductor [13,[81][82][83].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

et al. 2020

View full text Add to dashboard Cite

Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.

show abstract

mentioning

confidence: 99%

mentioning

confidence: 99%

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

et al. 2020

View full text Add to dashboard Cite

show abstract

“…There are many results applying ML to the past performance data of the equipment, see surveys [10][11][12] for comprehensive overviews. The existing ML methodologies for PdM are based on methods such as Support Vector Machines [6,[13][14][15][16][17][18], k-Nearest Neighbors [6,13,16], Artificial Neural Networks and Deep Learning [16,19,20], stochastic processes [21], K-means [13,16,22], Bayesian reasoning [23]. Ensemble methodologies where several methods are used and the weighted average of their predictions are reported in e.g.…”

Section: Examplementioning

confidence: 99%

Relaxed Rule-Based Learning for Automated Predictive Maintenance: Proof of Concept

Razgon

Mousavi

2020

Algorithms

View full text Add to dashboard Cite

In this paper we propose a novel approach of rule learning called Relaxed Separate-and- Conquer (RSC): a modification of the standard Separate-and-Conquer (SeCo) methodology that does not require elimination of covered rows. This method can be seen as a generalization of the methods of SeCo and weighted covering that does not suffer from fragmentation. We present an empirical investigation of the proposed RSC approach in the area of Predictive Maintenance (PdM) of complex manufacturing machines, to predict forthcoming failures of these machines. In particular, we use for experiments a real industrial case study of a Continuous Compression Moulding (CCM) machine which manufactures the plastic bottle closure (caps) in the beverage industry. We compare the RSC approach with a Decision Tree (DT) based and SeCo algorithms and demonstrate that RSC significantly outperforms both DT based and SeCo rule learners. We conclude that the proposed RSC approach is promising for PdM guided by rule learning.

show abstract

“…The kernel functions used were polynomials and the Radial Basis Function (RBF) since, according to the author, they are more efficient. [122] applies SVM to the vibration signal to identify the failure patterns of the bearings in WTs. In [172], the authors process the vibration signal through the Hilbert-Huang transform (HHT) method and apply SVM to detect and diagnose the bearing failures.…”

Section: Support Vector Machines (Svms)mentioning

confidence: 99%

Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

et al. 2019

View full text Add to dashboard Cite

Wind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models.

show abstract

Predictive maintenance for wind turbine diagnostics using vibration signal analysis based on collaborative recommendation approach

Cited by 29 publications

References 9 publications

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

Relaxed Rule-Based Learning for Automated Predictive Maintenance: Proof of Concept

Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

Contact Info

Product

Resources

About