Improving ship yard ballast pumps’ operations: A PCA approach to predictive maintenance

Kimera, David; Nangolo, Filemon N.

doi:10.1016/j.martra.2020.100003

Cited by 6 publications

(5 citation statements)

References 36 publications

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“…Among the sources of literature where software is used for floating docks [21][22][23], the following studies should be highlighted. The papers [24][25][26][27][28][29][30] present different systems for collecting and processing measurement data for remotely conducting docking operations, regarding the raising or lowering of a ship.…”

Section: Introductionmentioning

confidence: 99%

A Mobile Application of a Decision Support System for Ballasting a Floating Dock

Topalov,

Kondratenko,

Shevchenko

et al. 2024

JMM

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This paper is devoted to increasing the automation level of docking operations through the development of a mobile application for a decision support system capable of determining the optimal option for loading ballast into floating dock compartments when setting up and launching ships. The proposed decision support system and the mobile application allow implementing effective ballasting of the floating dock before docking the ship based on mathematical calculations of the impact of the ship on the floating dock and the ballasting rules. To test the developed system, the paper presents the functioning of the mobile application for a case of calculation of the docking up process of a 4,100-ton ship. In particular, the load forces of this ship are calculated with a graphical display of the distribution of the influence of 20 sections of the ship on 6 pontoons of the floating dock. As a result, this decision support system with the implemented mobile application has the characteristics of a convenient interface and is easy to operate. It can provide the dockmaster data and suggestions for decision-making, and during its operation, it is actively replenished with a database of the docked up ships.

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Section: Introductionmentioning

confidence: 99%

A Mobile Application of a Decision Support System for Ballasting a Floating Dock

Topalov,

Kondratenko,

Shevchenko

et al. 2024

JMM

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“…Dai et al 10 use fuel pump data to forecast the degradation trend of airborne fuel pumps based on the singular value trend decomposition, using an algorithm combining the least squares support vector machine (LSSVM) and particle swarm optimization (PSO). Kimera and Nangolo 11 use the SVM to predict terminal pump failure trends, pointing out that the quality and quantity of historical data sets significantly impact forecast accuracy.…”

Section: Introductionmentioning

confidence: 99%

RSF-based model for predicting pump failure trends in tunnels

Wu,

Chen,

et al. 2023

Advances in Mechanical Engineering

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The water pump is a piece of crucial electromechanical equipment to ensure the safety of tunnels. Therefore, it’s essential to master the performance trend of pumps to prevent the occurrence of failure. In this paper, essential information and failure records of pumps in 15 operating tunnels for many years were collected. According to the data characteristics, a data-filling model based on XGBoost is developed to address the issue of the censored data. Considering that most pumps are still in operation, a failure prediction model based on Random Survival Forest (RSF) is designed by incorporating survival analysis principles. The proposed Pump Failure Trend Prediction Model (PFTPM) overcomes difficulties caused by the lack of previous data and the small number of old pumps. We identify two phases of failure: the first phase exhibits a bathtub-shaped failure rate curve, while the second phase is characterized by a lower failure risk. The importance of considering rainfall, pump operating time, and performance changes for effective maintenance planning is emphasized. Furthermore, we summarize the failure evolution law of various types of pumps to amend maintenance cycle in the existing specification. Overall, this paper integrates innovative big-data technologies into the traditional maintenance data of tunnel pumps.

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“…Among other factors, a key quality in PCA is the inherent capability of processing large multivariate datasets as customary in industrial equipment sensor networks. As a result, PCA formed a field of choice in predictive analytics in several use cases, e.g., maritime and transport applications, as well as decision support systems in healthcare [1,2].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in Photovoltaic Production Factories via Monte Carlo Pre-Processed Principal Component Analysis

Arena

Corsini

Ferulano³

et al. 2021

Energies

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This paper investigates a use case of robust anomaly detection applied to the scenario of a photovoltaic production factory—namely, Enel Green Power’s 3SUN solar cell production plant in Catania, Italy—by considering a Monte Carlo based pre-processing technique as a valid alternative to other typically used methods. In particular, the proposed method exhibits the following advantages: (i) Outlier replacement, by contrast with traditional methods which are limited to outlier detection only, and (ii) the preservation of temporal locality with respect to the training dataset. After pre-processing, the authors trained an anomaly detection model based on principal component analysis and defined a suitable key performance indicator for each sensor in the production line based on the model errors. In this way, by running the algorithm on unseen data streams, it is possible to isolate anomalous conditions by monitoring the above-mentioned indicators and virtually trigger an alarm when exceeding a reference threshold. The proposed approach was tested on both standard operating conditions and an anomalous scenario. With respect to the considered use case, it successfully anticipated a fault in the equipment with an advance of almost two weeks, but also demonstrated its robustness to false alarms during normal conditions.

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Improving ship yard ballast pumps’ operations: A PCA approach to predictive maintenance

Cited by 6 publications

References 36 publications

A Mobile Application of a Decision Support System for Ballasting a Floating Dock

A Mobile Application of a Decision Support System for Ballasting a Floating Dock

RSF-based model for predicting pump failure trends in tunnels

Anomaly Detection in Photovoltaic Production Factories via Monte Carlo Pre-Processed Principal Component Analysis

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