Forecasting faults of industrial equipment using machine learning classifiers

Kolokas, Nikolaos; Vafeiadis, Thanasis; Ioannidis, Dimosthenis; Tzovaras, Dimitrios

doi:10.1109/inista.2018.8466309

Cited by 42 publications

(29 citation statements)

References 9 publications

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“…Some authors [21,56,82,112,120,124] have given attention to GBM technique. Some authors [65,66,79,109,112,117] carried out studies by use of DT technique. However, it is observed that there is less consideration given to XGBoost technique and there are only a few studies found in the literature [62,81,120].…”

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confidence: 99%

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

et al. 2020

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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.

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confidence: 99%

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

et al. 2020

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“…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

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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.

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“…Several studies [40][41][42][43] have shown that when you have available data related to anomalies, or you are able to simulate anomalies (as in this case), anomaly detection algorithms, based on neural networks, have obtained good results. For that, the second problem was dealing with an MLP neural network (see Figure 8).…”

Section: Anomaly Detection Platformmentioning

confidence: 99%

Retrofitting a Process Plant in an Industry 4.0 Perspective for Improving Safety and Maintenance Performance

2021

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The transformation from traditional industry to Industry 4.0 can bring many benefits in various spheres, from efficiency to safety. However, this transition involves adopting technologically advanced machinery with a high level of digitization and communication. The costs and time to replace obsolete machines could be unsustainable for many companies while retrofitting the old machinery. To make them ready to the Industry 4.0 context, they may represent an alternative to the replacement. Even if there are many studies related to retrofitting applied to machinery, there are very few studies related to the literature process industry sector. In this work, we propose a case study of a two-phase mixing plant that needed to be enhanced in the safety and maintainability conditions with reasonable times and costs. In this regard, the Digital Twin techniques and Deep Learning algorithms will be tested to predict and detect future faults, not only already visible and existing malfunctions. This approach strength is that, with limited investments and reasonable times, it allows the transformation of an old plant into a smart plant capable of communicating quickly with operators to increase its safety and maintainability.

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Forecasting faults of industrial equipment using machine learning classifiers

Cited by 42 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

Retrofitting a Process Plant in an Industry 4.0 Perspective for Improving Safety and Maintenance Performance

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