Machine Learning Based Predictive Maintenance Strategy: A Super Learning Approach with Deep Neural Networks

Butte, Sujata; Prashanth, A.; Patil, Sainath

doi:10.1109/wmed.2018.8360836

Cited by 38 publications

(17 citation statements)

References 16 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%

“…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. Some authors [65,66,79,109,112,117] carried out studies by use of DT technique.…”

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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].…”

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

et al. 2020

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“…In addition to the review works, some recent works proposed to perform a comparative analysis of their PdM strategy to different classical ML algorithms [ 48 , 49 , 50 , 51 ]. Given the steadily increasing use of sensors and the amount of data produced by them, and the fact that these data are often materialized as real-time time series DL methods will undoubtedly be among the future PdM tools.…”

Section: Data-driven Pdmmentioning

confidence: 99%

A Survey on Data-Driven Predictive Maintenance for the Railway Industry

Davari

Veloso

Costa

et al. 2021

Sensors

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In the last few years, many works have addressed Predictive Maintenance (PdM) by the use of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The monitoring and logging of industrial equipment events, like temporal behavior and fault events—anomaly detection in time-series—can be obtained from records generated by sensors installed in different parts of an industrial plant. However, such progress is incipient because we still have many challenges, and the performance of applications depends on the appropriate choice of the method. This article presents a survey of existing ML and DL techniques for handling PdM in the railway industry. This survey discusses the main approaches for this specific application within a taxonomy defined by the type of task, employed methods, metrics of evaluation, the specific equipment or process, and datasets. Lastly, we conclude and outline some suggestions for future research.

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“…Hay técnicas que en la actualidad son relevantes para predecir fallos inminentes de los sistemas, técnicas para la clasificación de variables en el tiempo, de series de tiempo multivariadas (Westholm, 2018), pasar de pensar en un sistema lineal a uno interconectado, acompañando la adopción de los avances recientes en los métodos de análisis de big data que ayudarán a desarrollar una estrategia efectiva de mantenimiento preventivo (Sujata et al, 2018). Por ejemplo ya se están desarrollando máquinas inteligentes que gracias al despliegue de sensores de medición permiten comunicar el fallo en tiempo real (Exner, 2018).…”

Section: Discusionunclassified

Predicción de Fallos Mecánicos en Equipos de Envoltura

Rodas

Castrillón

2019

Inf. tecnol.

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El objetivo de este trabajo es el pronóstico de fallos mecánicos en equipos de envoltura partiendo de datos históricos para mejorar la programación de intervenciones preventivas y disminuir el número de correctivas. Para esto se debe recolectar y clasificar datos históricos de tiempos entre fallos, realizar modelos de correlación y a partir de estos resultados definir cuál modelo de pronóstico es mejor, validando los datos mediante pruebas de hipótesis que reduzcan al mínimo la elección de este. Por último, aplicarlo, llegar a resultados y determinar tiempos aptos de programación para mantenimientos preventivos. La metodología empleada condujo al planteamiento de una ecuación que se adapta a las variables que afectan de manera directa el pronóstico del fallo. Se concluyó el impacto positivo de los paros programados en una organización, reduciendo el intervalo de tiempo de intervenciones preventivas y proyectando los tiempos de intervención para el siguiente año.

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Machine Learning Based Predictive Maintenance Strategy: A Super Learning Approach with Deep Neural Networks

Cited by 38 publications

References 16 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 Survey on Data-Driven Predictive Maintenance for the Railway Industry

Predicción de Fallos Mecánicos en Equipos de Envoltura

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