Health assessment framework of marine engines enabled by digital twins

Abstract

The advancements in digital twins when combined with the use of the machine learning tools can facilitate the effective health assessment and diagnostics of safety critical systems. This study aims at developing a framework to address the health assessment of marine engines utilising digital twins based on first-principles. This framework follows four distinct stages, with the former two including the marine engine digital-twin set up by customising the required thermodynamic models, as well as its calibration using tests trials data representing the engine healthy conditions. In the third stage, measurements from actual operating conditions are corrected and subsequently employed to develop the digital twin representing the prevailing conditions. The fourth stage deals with the engine health assessment by assessing health metrics derived from the developed digital twins. This framework is demonstrated in a case study of a large marine four-stroke nine-cylinder propulsion engine. The results demonstrate that three cylinders are identified to be underperforming leading to an average increase of the engine Brake Specific Fuel Consumption (BSFC) by 2.1%, whereas an average decreases of 6.8% in Indicated Mean Effective Pressure (IMEP) and 6.1% in the Exhaust Gas Temperature (EGT) are exhibited for the underperforming cylinders across the entire operating envelope. The developed digital twins facilitate the effective mapping of the engine performance for the entire operating envelope under several health conditions, providing enhanced insights for the current engine health status. The advantages of the proposed framework include the use of easily obtained data, and its application to several engine types including two and four-stroke engines for both propulsion and auxiliary use.

A Framework to Assure the Trustworthiness of Physical Model-Based Digital Twins for Marine Engines

“…The DT sub-models are calibrated by employing an optimisation method, which uses the reference performance parameters and calculates the optimal values for the sub-model constants to achieve the lowest value of an objective function [15,39]. GT-SUITE software provides the Advanced Direct Optimiser (ADO) that employs the Non-dominated Sorting Genetic Algorithm NSGA-III [40].…”

“…To develop DTs, models (physical and/or data-driven) for the reference physical system components must be developed and integrated, whereas their validity can be appraised based on acquired datasets [14]. Moreover, DTs can be employed in health monitoring applications, when reference physical system data for current conditions are provided [15]. The DT is a useful tool for generating datasets that represent an extended envelope of operating conditions.…”

“…Tsitsilonis et al [15] employed a marine engine physical model to generate performance parameters under both healthy conditions and current conditions, facilitating engine health assessment. Altosole et al [18] employed a marine engine physical model to generate performance parameters considering ten degradation types, subsequently developing engine anomaly diagnosis models.…”

Digital twins for internal combustion engines: A brief review

“…This physics-based technique allows engineers to get a thorough understanding of engine performance under an array of operating circumstances. As a result, reliable predictions and optimization techniques can be developed, enhancing effectiveness and reducing emissions (Söderäng et al, 2022) (Tsitsilonis et al, 2023). A typical schematic of a digital twin for an IC engine is depicted in Figure 1.…”

Development of a Zero-Dimensional Model for a Low-Speed Two-Stroke Marine Diesel Engine with Exhaust Gas Bypass and Performance Evaluation

“…The shipping industry faces a number of challenges from policies, regulations, and operation competition, including decarbonization, non-GHG emissions reduction, the rise in fuel price, and the increasingly stringent IMO and regional (emission control zones) emission regulations [1][2][3][4][5]. The advancement in digital twins can be combined with health management, fault diagnosis, engine energy efficiency evaluation, and optimization to help deal with the faced challenges in maritime industry [6][7][8][9]. The diesel engine performance model with fast response speed and high accuracy is vital and the basis of digital twins.…”