Feature Extraction from Indirect Monitoring in Marine Oil Separation Systems

Marichal, G.N.; Ávila, Deivis; Hernández, Alberto Miguel Brito; Padrón, Isidro; Castejón, Cristina

doi:10.3390/s18093159

Cited by 11 publications

(13 citation statements)

References 32 publications

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“…Marichal et al [8] proposed a fault detection solution based on a genetic-neuro-fuzzy system used to predict the early faults of marine oil separation systems.…”

Section: Literature Review Summary On Predictive Maintenancementioning

confidence: 99%

SOON: Social Network of Machines Solution for Predictive Maintenance of Electrical Drive in Industry 4.0

Iantovics

Gligor

Montequín

et al. 2022

Acta Marisiensis. Seria Technologica

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Predictive methods represent techniques commonly met in Industry 4.0 that offer a way to early predict or detect faults of machines, devices or tools. This is useful to anticipate failures with the main goal of improving maintenance planning. Making such predictions could decrease the unexpected malfunction operation or manufacturing downtime and consequently the overall maintenance costs. In this paper we present the basis of the architecture designed for predictive maintenance in the project Social Network of Machines (SOON) under the paradigm of Industry 4.0, as well as a brief literature state-of-the-art survey of the topic. A particular implementation of this architecture, a testbed for electrical motors failure detection, is shown and evaluated.

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“…Marichal et al [8] proposed a fault detection solution based on a genetic-neuro-fuzzy system used to predict the early faults of marine oil separation systems.…”

Section: Literature Review Summary On Predictive Maintenancementioning

confidence: 99%

SOON: Social Network of Machines Solution for Predictive Maintenance of Electrical Drive in Industry 4.0

Iantovics

Gligor

Montequín

et al. 2022

Acta Marisiensis. Seria Technologica

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“…With the application of novel innovative techniques, predictive maintenance has achieved better performance than conventional preventive approaches. There are several technologies used for predictive maintenance such as the model-based approach with the application of artificial intelligence (AI) and a data-driven approach, among others [12,13]. Some of these studies are mentioned in the following subsections.…”

Section: Related Workmentioning

confidence: 99%

“…Marichal et al [12] proposed a fault detection system based on AI techniques. In this paper, vibration characteristics of a marine oil separation system were analyzed.…”

Section: Related Workmentioning

confidence: 99%

Predictive Maintenance with Sensor Data Analytics on a Raspberry Pi-Based Experimental Platform

Chuang

Sahoo

Lin

et al. 2019

Sensors

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Predictive maintenance techniques can determine the conditions of equipment in order to evaluate when maintenance should be performed. Thus, it minimizes the unexpected device downtime, lowers the maintenance costs, extends equipment lifecycle, etc. Therefore, this article developed a predictive maintenance mechanism with the construction of a test platform and data analysis along with machine learning. The information transmission of sensors was based on Raspberry Pi via the TCP/IP (Transmission Control Protocol/Internet Protocol) communication protocol. The sensors used for environmental sensing were implemented on the programmable interface controller and the data were stored in time sequence. A statistical analysis software platform was adopted for data preprocessing, modelling, and prediction to provide necessary maintenance decision. Using multivariate analysis users can obtain more information about the equipment’s status, and the administrator can also determine the operational situation before unexpected device anomalies. The developed modules are decisively helpful in preventing unpredictable losses, thus improving the quality of services.

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“…It is well known that there are filtration systems on ships intended for the cleaning of lube oils for diesel engines. In particular, the centrifuge separator systems for oil lubricants allow the oil to be cleaned both of solid particles and the possible existence of water and sludge [12].…”

Section: Introductionmentioning

confidence: 99%

Data Preprocessing for Vibration Analysis: Application in Indirect Monitoring of ‘Ship Centrifuge Lube Oil Separation Systems’

Zamorano

Ávila

Marichal

et al. 2022

JMSE

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Air quality can be affected by merchant ships, so it is important to regulate emissions that are produced, as well as to use energy efficiently. In this sense, the cleanliness of the oil used in lubrication is essential to achieve a better use of energy and reduce losses in marine engines. For that, it is vital to carry out good maintenance strategies. Therefore, it is important to develop techniques that allow condition monitoring during engine operation. In order to detect potential problems as soon as possible, it is common to analyze vibratory signals, since sustainable changes in the rotating frequency and its harmonics can be detected, which was the objective of this work, by analyzing the time-frequency domain using wavelet packet transform. A methodology to select the optimal function (mother wavelet) and the best patterns to monitor, in order to determine the state of the purifiers of the marine lube oils, was carried out, including intelligent classification systems. Specifically, this document considers centrifugal oil lubricant separators systems, since the monitoring of these systems can determine the condition of different mechanical systems.

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Feature Extraction from Indirect Monitoring in Marine Oil Separation Systems

Cited by 11 publications

References 32 publications

SOON: Social Network of Machines Solution for Predictive Maintenance of Electrical Drive in Industry 4.0

SOON: Social Network of Machines Solution for Predictive Maintenance of Electrical Drive in Industry 4.0

Predictive Maintenance with Sensor Data Analytics on a Raspberry Pi-Based Experimental Platform

Data Preprocessing for Vibration Analysis: Application in Indirect Monitoring of ‘Ship Centrifuge Lube Oil Separation Systems’

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