Performance Decay Analysis of a Marine Gas Turbine Propulsion System

Altosole, Marco; Campora, Ugo; Martelli, Michele; Figari, Massimo

doi:10.5957/josr.58.3.130037

Cited by 20 publications

(15 citation statements)

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“…The resistance increase due to fouling has been modeled utilizing a correction factor kH based on the information reported in Hadler et al (1962). Using the towing tank data provided by the ship owner the resistance has been evaluated after one and two years of vessel operations in agreement with the data available in the literature (Altosole et al, 2014).…”

Section: Hll Degradation Modelmentioning

confidence: 89%

“…The model described in Section 2.2 has been updated to take into account the performance decay of the following components: GT, GTC, HLL, and PRP. As reported in Altosole et al (2014) and Coraddu et al (2016), the time domain performance decay has been modeled using suitable coefficients embedded in the GTC low and high power modules of the model. This, allows quantifying the variation of performance of each component and consequently of the entire propulsion system.…”

Section: Degradation Modelmentioning

confidence: 99%

“…In agreement with previous work, (Coraddu et al, 2016), the effect of the fouling is simulated by reducing the numerical values of the airflow rate M c and of the isentropic efficiency η c with a reduction factor kM c and K ηc . The detailed description of the GTC and GT degradation model is provided in Coraddu et al (2016) and Altosole et al (2014). The authors applied a reduction factor, kM T , to the GT flow rate to represent the effect of fouling.…”

Section: Gtc and Gt Degradation Modelmentioning

confidence: 99%

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Condition-Based Maintenance of Naval Propulsion Systems with supervised Data Analysis

et al. 2018

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The behavior and interaction of the main components of Ship Propulsion Systems cannot be easily modeled with a priori physical knowledge, considering the large amount of variables influencing them. Data-Driven Models (DDMs), instead, exploit advanced statistical techniques to build models directly on the large amount of historical data collected by on-board automation systems, without requiring any a priori knowledge. DDMs are extremely useful when it comes to continuously monitoring the propulsion equipment and take decisions based on the actual condition of the propulsion plant. In this paper, the authors investigate the problem of performing Condition-Based Maintenance through the use of DDMs. In order to conceive this purpose, several state-of-the-art supervised learning techniques are adopted, which require labeled sensor data in order to be deployed. A naval vessel, characterized by a combined diesel-electric and gas propulsion plant, has been exploited to collect such data and show the effectiveness of the proposed approaches. Because of confidentiality constraints with the Navy the authors used a real-data validated simulator and the dataset has been published for free use through the UCI repository.

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Section: Hll Degradation Modelmentioning

confidence: 89%

Section: Degradation Modelmentioning

confidence: 99%

Section: Gtc and Gt Degradation Modelmentioning

confidence: 99%

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Condition-Based Maintenance of Naval Propulsion Systems with supervised Data Analysis

et al. 2018

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“…Figure 7 shows the overall Simulink scheme adopted for the supercharged Bergen engines. Similar simulation approaches have been recently used also by Tadros et al 25 and Altosole et al 26 for the performance representation of a two-stroke DE and a marine gas turbine, respectively.…”

Section: Simulation Modelsmentioning

confidence: 95%

Simulation and performance comparison between diesel and natural gas engines for marine applications

Altosole

Benvenuto

Campora

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part M:

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The article shows the performance comparison between two marine engines, fuelled by diesel oil and natural gas respectively. Two different simulation codes, each for engine type, have been developed to extend the comparison to the whole working area of the examined engines. Although the maximum continuous power is very similar (about 2 MW at the same rotational speed), some differences exist in size, efficiency and pollutant emissions. The reasons are investigated through a specific thermodynamic analysis, by comparing the respective real cycles at several power and revolution values. In detail, the two combustion modes are analysed to explain the main differences that are found mostly in nitrogen oxides emissions

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“…The core functions, corresponding to maintenance decisions among the architecture, can be summarized as condition monitoring, health assessment and prognostics (Coraddu et al 2015 ; Sun et al 2015 ). With the fast development of industry and the international market, especially the areas of complex plants such as shipbuilding and aircraft, CBM recommends timely and accurate maintenance actions based on the information obtained from condition monitoring (Altosole et al 2014 ). Therefore, more and more advanced machine learning approaches have been applied in CBM for better improve the reliability, such as support vector machine and information fusion algorithms (Coraddu et al 2014 ; Niu et al 2010 ; Widodo and Yang 2007 ).…”

Section: Introductionmentioning

confidence: 99%

Prognostic for hydraulic pump based upon DCT-composite spectrum and the modified echo state network

Sun

2016

SpringerPlus

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Prognostic is a key step of the condition-based maintenance (CBM). In order to improve the predicting performance, a novel method for prognostic for the hydraulic pump is proposed in this paper. Based on the improvement of the traditional composite spectrum, the DCT-composite spectrum (DCS) fusion algorithm is initially presented to make fusion of multi-channel vibration signals. The DCS composite spectrum entropy is extracted as the feature. Furthermore, the modified echo state networks (ESN) model is established for prognostic using the extracted feature. The reservoir is updated and the elements of the neighboring matrix are redefined for improving predicting accuracy. Analysis of the application in the hydraulic pump degradation experiment demonstrates that the proposed algorithm is feasible and is meaningful for CBM.

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Performance Decay Analysis of a Marine Gas Turbine Propulsion System

Cited by 20 publications

References 0 publications

Condition-Based Maintenance of Naval Propulsion Systems with supervised Data Analysis

Condition-Based Maintenance of Naval Propulsion Systems with supervised Data Analysis

Simulation and performance comparison between diesel and natural gas engines for marine applications

Prognostic for hydraulic pump based upon DCT-composite spectrum and the modified echo state network

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