Machine Learning approach for Predictive Maintenance in Industry 4.0

Paolanti, Marina; Romeo, Luca; Felicetti, Andrea; Mancini, Adriano; Frontoni, Emanuele; Loncarski, Jelena

doi:10.1109/mesa.2018.8449150

Cited by 176 publications

(70 citation statements)

References 19 publications

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“…Some authors [10,30,[46][47][48][75][76][77]107,114,[118][119][120][121] focused on ANN ML algorithms. 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].…”

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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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“…Of particular interest here, are the solutions based on AI because, due the improvement in this area in the last years, lots of new AI-based solutions for PdM have been proposed. Some examples are discussed in [42][43][44]. The adoption of AI-based solutions for predictive maintenance perfectly fits the vision of the fourth industrial revolution [45].…”

Section: Maintenance Decision-makingmentioning

confidence: 88%

A Model for Predictive Maintenance Based on Asset Administration Shell

Cavalieri

Salafia

2020

Sensors

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Maintenance is one of the most important aspects in industrial and production environments. Predictive maintenance is an approach that aims to schedule maintenance tasks based on historical data in order to avoid machine failures and reduce the costs due to unnecessary maintenance actions. Approaches for the implementation of a maintenance solution often differ depending on the kind of data to be analyzed and on the techniques and models adopted for the failure forecasts and for maintenance decision-making. Nowadays, Industry 4.0 introduces a flexible and adaptable manufacturing concept to satisfy a market requiring an increasing demand for customization. The adoption of vendor-specific solutions for predictive maintenance and the heterogeneity of technologies adopted in the brownfield for the condition monitoring of machinery reduce the flexibility and interoperability required by Industry 4.0. In this paper a novel approach for the definition of a generic and technology-independent model for predictive maintenance is presented. Such model leverages on the concept of the Reference Architecture Model for Industry (RAMI) 4.0 Asset Administration Shell, as a means to achieve interoperability between different devices and to implement generic functionalities for predictive maintenance.

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“…The authors tested SVMs ensembles Predictive Maintenance with K-Nearest Neighbor (KNN) and Predictive Maintenance with SVM; the predictive maintenance with SVM gave slightly better results than the KNN approach. In [22], the authors used the random forest (RF) ML technique to generate a predictive maintenance approach for a cutting machine. The RF model used different rotor status of the cutting machine to perform classification in the predictive maintenance scheme.…”

Section: Predictive Maintenance Approaches Using Machine Learning mentioning

confidence: 99%

“…After solving (22), eleven values are found: λ 1 , λ 2 , λ 3 , λ 4 , λ 5 , λ 6 , λ 7 , λ 8 , λ 9 , λ 10 and λ 11 ; these are the Eigenvalues of our dataset matrix. The next step is to find the corresponding Eigenvectors for each Eigenvalues.…”

Section: ) Generate the Variance-covariance Matrix Of The Dataset Dmentioning

confidence: 99%

An Effective Predictive Maintenance Framework for Conveyor Motors Using Dual Time-Series Imaging and Convolutional Neural Network in an Industry 4.0 Environment

Kiangala

Wang

2020

IEEE Access

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The ascent of Industry 4.0 and smart manufacturing has emphasized the use of intelligent manufacturing techniques, tools, and methods such as predictive maintenance. The predictive maintenance function facilitates the early detection of faults and errors in machinery before they reach critical stages. This study suggests the design of an experimental predictive maintenance framework, for conveyor motors, that efficiently detects a conveyor system's impairments and considerably reduces the risk of incorrect faults diagnosis in the plant; We achieve this remarkable task by developing a machine learning model that classifies whether the abnormalities observed are production-threatening or not. We build a classification model using a combination of time-series imaging and convolutional neural network (CNN) for better accuracy. In this research, time-series represent different observations recorded from the machine over time. Our framework is designed to accommodate both univariate and multivariate time-series as inputs of the model, offering more flexibility to prepare for an Industry 4.0 environment. Because multivariate time-series are challenging to manipulate and visualize, we apply a feature extraction approach called principal component analysis (PCA) to reduce their dimensions to a maximum of two channels. The time-series are encoded into images via the Gramian Angular Field (GAF) method and used as inputs to a CNN model. We added a parameterized rectifier linear unit (PReLU) activation function option to the CNN model to improve the performance of more extensive networks. All the features listed added together contribute to the creation of a robust future proof predictive maintenance framework. The experimental results achieved in this study show the advantages of our predictive maintenance framework over traditional classification approaches.

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Machine Learning approach for Predictive Maintenance in Industry 4.0

Cited by 176 publications

References 19 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

A Model for Predictive Maintenance Based on Asset Administration Shell

An Effective Predictive Maintenance Framework for Conveyor Motors Using Dual Time-Series Imaging and Convolutional Neural Network in an Industry 4.0 Environment

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