Model-Based Condition and State Monitoring of Large Marine Diesel Engines

Watzenig, Daniel; Sommer, Martin; Steiner, Gerald

doi:10.5772/55267

Cited by 7 publications

(8 citation statements)

References 17 publications

(24 reference statements)

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“…The compression and expansion processes are modelled using polytropic relationships according to equations (3) and ( 4), whilst the combustion process is modelled by using a function that interpolates between the other two processes. 18,46 Hence, by considering the interpolating function, the analytic in-cylinder pressure function for the closed cycle is represented by equation (5).…”

Section: Reference System and Direct Crankshaft Dynamics Modelmentioning

confidence: 99%

“…For a diesel engine, there are two types of interpolating functions which can be used in equation (5). The most simplistic one is the sigmoid function, 48 since it depends on two parameters according to equation (6).…”

Section: Reference System and Direct Crankshaft Dynamics Modelmentioning

confidence: 99%

“…However, the former does not pose a challenge for this study, as a relatively accurate estimate for the initial values of the unknown parameters vector can be obtained. This can be done by means of least squares fitting of equation (5) to the in-cylinder pressure diagrams, 15 which can be obtained from the manufacturer documentation (e.g. engine shop tests) and are available for a number of engine operating points.…”

Section: Extended Direct Crankshaft Dynamics Modelmentioning

confidence: 99%

“…2,3 However, under actual operating conditions, degradation effects and various faults significantly reduce the engine efficiency and availability, whilst leading to increased emissions and operational costs. [4][5][6] With the advent of digitalisation, monitoring and analysing various engine parameters, such as engine power, fuel consumption, working medium temperatures, scavenge air pressure and in-cylinder pressures, have become widespread among engine operators. 7,8 In this way, better insights for the engine's condition can be obtained, which prompts maintenance and repair actions in order to attain the engine optimal efficiency and reliability as well as to reduce the associated operating cost and gaseous emissions, with tangible benefits on the ship operations sustainability.…”

Section: Introductionmentioning

confidence: 99%

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A novel method for in-cylinder pressure prediction using the engine instantaneous crankshaft torque

Tsitsilonis

Theotokatos

2021

Proceedings of the Institution of Mechanical Engineers, Part M:

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Current practices of condition assessment in large marine engines are largely based on the measurement of cylinder pressure using external kits, which poses challenges due to sensors synchronisation and durability issues, as well as the inability to perform continuous monitoring. For addressing these challenges, this study aims at developing a novel method to solve the inverse problem of predicting the pressure variations in all engine cylinders, by using the Instantaneous Crankshaft Torque (ICT) measurement for large internal combustion engines. This method is developed by considering the Initial Value Problem (IVP) technique along with the integration of a direct crankshaft dynamics model incorporating the sensitivity parameters and stability criteria calculation based on the Lyapunov Exponent (LE) as well as a state-of-the-art Nonmonotone Self-Adaptive Levenberg-Marquardt (NSALMN) optimisation algorithm. The method is tested for a number of case studies using different combustion models based on the Weibe and sigmoid functions, as well as for healthy, degraded and faulty engine conditions. The derived results demonstrate adequate accuracy exhibiting a maximum error of 0.3% in the prediction of the mean peak in-cylinder pressure. The analysis of the calculated sensitivity parameters resulted in the identification of the parameters that significantly impact the solution, thus providing improved insights for selecting the developed method settings. The developed method renders the continuous and non-intrusive in-cylinder pressures monitoring feasible, by using a permanently installed shaft power metre sensor with higher sample rates.

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Section: Reference System and Direct Crankshaft Dynamics Modelmentioning

confidence: 99%

Section: Reference System and Direct Crankshaft Dynamics Modelmentioning

confidence: 99%

Section: Extended Direct Crankshaft Dynamics Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel method for in-cylinder pressure prediction using the engine instantaneous crankshaft torque

Tsitsilonis

Theotokatos

2021

Proceedings of the Institution of Mechanical Engineers, Part M:

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“…Condition monitoring on diesel engines is essential for their safety and reliability. In the condition monitoring system of diesel engines, reliable detection and classification of engine malfunctions is very important to schedule maintenance [ 1 ]. However, the ambiguity between different malfunctions is still the main challenge in any condition monitoring system.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals

et al. 2015

Sensors

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The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine.

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One Class Classification Based Anomaly Detection for Marine Engines

Smart

Grice²,

et al. 2020

Studies in Computational Intelligence

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The series "Studies in Computational Intelligence" (SCI) publishes new developments and advances in the various areas of computational intelligence-quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output.

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Model-Based Condition and State Monitoring of Large Marine Diesel Engines

Cited by 7 publications

References 17 publications

A novel method for in-cylinder pressure prediction using the engine instantaneous crankshaft torque

A novel method for in-cylinder pressure prediction using the engine instantaneous crankshaft torque

An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals

One Class Classification Based Anomaly Detection for Marine Engines

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