Determination of performance degradation of a marine diesel engine by using curve based approach

Kökkülünk, Görkem; Parlak, Adnan; Erdem, Hasan Hüseyin

doi:10.1016/j.applthermaleng.2016.08.019

Cited by 19 publications

(7 citation statements)

References 29 publications

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“…The original aspect of this model is that the TC dynamics are represented by the Büchi power and flow balance between compressor and turbine, and do not require compressor or turbine maps for calibration. By neglecting fast dynamics, the model's run-time is between 100 and 2500 times real-time, much faster than MVEMs using compressor and turbine maps, such as Nielsen et al (2017), Theotokatos and Tzelepis (2015), Sapra et al (2017), and Kökkülünk et al (2016).…”

Section: Physical Models (Pms)mentioning

confidence: 99%

Physical, data-driven and hybrid approaches to model engine exhaust gas temperatures in operational conditions

Coraddu

Oneto

Cipollini

et al. 2021

Ships and Offshore Structures

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Fast diesel engine models for real-time prediction in dynamic conditions are required to predict engine performance parameters, to identify emerging failures early on and to establish trends in performance reduction. In order to address these issues, two main alternatives exist: one is to exploit the physical knowledge of the problem, the other one is to exploit the historical data produced by the modern automation system. Unfortunately, the first approach often results in hard-to-tune and very computationally demanding models that are not suited for real-time prediction, while the second approach is often not trusted because of its questionable physical grounds. In this paper, the authors propose a novel hybrid model, which combines physical and data-driven models, to model diesel engine exhaust gas temperatures in operational conditions. Thanks to the combination of these two techniques, the authors were able to build a fast, accurate and physically grounded model that bridges the gap between the physical and data driven approaches. In order to support the proposal, the authors will show the performance of the different methods on real-world data collected from the Holland Class Oceangoing Patrol Vessel.

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Section: Physical Models (Pms)mentioning

confidence: 99%

Physical, data-driven and hybrid approaches to model engine exhaust gas temperatures in operational conditions

Coraddu

Oneto

Cipollini

et al. 2021

Ships and Offshore Structures

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“…According to the literature [10][11][12], the hydraulic precision pair is one in which two elements of barrel/cylinder (B) and plunger/piston (P) are separated by working fluid, for example fuel oil (F) forming the third structural element of machine and tribological node (FBP), as shown in Figure 2. Fuel oil in the FIP simultaneously satisfies the functions of lubricating, cooling, and sealing, working in an open system with recirculation allowing a continuous flow of fresh fuel corresponding to feed rate.…”

Section: Analysis Of Operational Problems Occurring In Fuel Injectionmentioning

confidence: 99%

The Use of Acoustic Emission to Diagnosis of Fuel Injection Pumps of Marine Diesel Engines

Bejger

Drzewieniecki

2019

Energies

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The article draws attention to the problems of maintaining fuel injection pumps of marine diesel engines in the conditions of the use of residual fuels in which the quality is steadily deteriorating. The analysis of tribological processes occurring in hydraulic precision pairs of fuel injection pumps, such as a barrel-plunger, is presented. Problems occurring regarding the operation of injection pumps and the possibilities of their avoidance on board are discussed. The means of condition monitoring, including the application of thermography methods, are characterized. The authors have done research concerning diagnosing injection systems of high and medium power engines by using acoustic emission (AE) signals. The experiment results obtained with the use of acoustic emission and wavelet analysis confirmed the dependency of the frequency components contained in the acoustic emission signal on the condition state of the injector pumps’ tribological pair.

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“…The original aspect of this model is that the turbocharger dynamics are represented by the Büchi power and flow balance between compressor and turbine and do not require compressor or turbine maps for calibration. As fast dynamics are neglected in this model, the model runs up to 2500 times real time, much faster than mean value engine models using compressor and turbine maps, such as Nielsen et al 2017, Theotokatos and Tzelepis (2015), Sapra et al (2017) and Kökkülünk et al (2016). Moreover, Machine Learning methods can be used to improve the mathematical relationships in the models, improving it's accuracy, while maintaining the run-time during the feedforward phase, after an initial learning phase, which also benefits from fast physical models.…”

Section: Engine Physical Modelmentioning

confidence: 99%

“…However, this combined approach has not yet been applied to improve diesel engine models, so they can be used for CBM. Currently, most diesel engine models focus on the prediction of fuel consumption, CO 2 , NO x and PM emissions and smoke production (Theotokatos and Tzelepis, 2015;Nielsen et al, 2017;Kökkülünk et al, 2016). However, temperatures of the exhaust valve, exhaust receiver gas flow and turbine entry gas flow are important indicators for thermal loading of an engine (Sapra et al, 2017;Geertsma et al, 2017;Grimmelius and Stapersma, 2000), and to indicate intercooler failure (Hountalas, 2000).…”

Section: Introductionmentioning

confidence: 99%

Ship diesel engine performance modelling with combined physical and machine learning approach

Coraddu

Kalikatzarakis

Oneto

et al. 2018

Proceedings of the International Ship Control Systems Symposium (iSCSS)

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Condition Based Maintenance on diesel engines can help to reduce maintenance load and better plan maintenance activities in order to support ships with reduced or no crew. Diesel engine performance models are required to predict engine performance parameters in order to identify emerging failures early on and to establish trends in performance reduction. In this paper, a novel approach is proposed to accurately predict engine temperatures during operational dynamic manoeuvring. In this hybrid modelling approach, the authors combine the mechanistic knowledge from physical diesel engine models with the statistic knowledge from engine measurements on a sound engine. This simulation study, using data collected from a Holland class patrol vessel, demonstrates that existing models cannot accurately predict measured temperatures during dynamic manoeuvring, and that the hybrid modelling approach outperforms a purely data driven approach by reducing the prediction error during a typical day of operation from 10% to 2%.

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Determination of performance degradation of a marine diesel engine by using curve based approach

Cited by 19 publications

References 29 publications

Physical, data-driven and hybrid approaches to model engine exhaust gas temperatures in operational conditions

Physical, data-driven and hybrid approaches to model engine exhaust gas temperatures in operational conditions

The Use of Acoustic Emission to Diagnosis of Fuel Injection Pumps of Marine Diesel Engines

Ship diesel engine performance modelling with combined physical and machine learning approach

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