A novel methodology for marine dual fuel engines sensors diagnostics and health management

Abstract

The sensors abnormalities, faults, failure detection and diagnosis for marine engines are considered crucial for ensuring the engine safe and smooth operation. The development of such system(s) is typically based on the manufacturers experience on sensors and actuators faults and failure events. This study aims to introduce a novel methodology for the sensors diagnostics and health management in marine dual fuel engines by employing a combination of thermodynamic, functional control and data-driven models. The concept of an Engine Diagnostics System (EDS) is developed to provide intelligent engine monitoring, advanced sensors’ faults detection as well as timely and profound corrective actions. This system employs a neural networks (NN) Data-Driven (DD) model along with appropriate logic controls. The DD model is set up based on the derived steady state data from a thermodynamic model of high fidelity and is capable of real-time prediction of the engine health condition behaviour. The concept of a novel Unified Digital System (UDS) is proposed that combines the engine’s existing control and diagnostic systems with the EDS. The functionality of the UDS system is validated by employing a digital twin of the considered marine dual fuel engine by investigating scenarios for assessing the engine performance that entail abnormalities in the engine’s speed and boost pressure sensors. The simulation results demonstrate that the developed UDS is capable of sufficiently capturing the engine’s sensors abnormalities and applying appropriate corrective actions to restore the engine operation in its original state. This study benefits the development future systems facilitating the engines condition assessment and self-correction of the engine sensors’ abnormalities, which will be required for smart and autonomous shipping.

Safety performance assessment of a marine dual fuel engine by integrating failure mode, effects and criticality analysis with simulation tools

“…On the contrary, detectability may be improved by either focussing on redundancy of components to ascertain the measurements (or generally signal) trustworthiness or by introducing intelligent systems for the engine and its components health assessment such as the unified diagnostic system (UDS) described in Stoumpos and Theotokatos. 55…”

“…This study employs a digital twin (DT) of a marine four-stroke DF engine (W9L50DF) that has been previously developed and validated for steady state and transient conditions (including mode switching). 53,54 The latest version of this digital twin is described in detail in Stoumpos and Theotokatos 55 and apart from the engine thermodynamic modelling, it accommodates the Faulty Operation Simulator (FOS) interface, the Alarms and Monitoring System (AMS) and the Engine Safety System (ESS), which are grouped and linked with the Engine Control System (ECS), forming the unified engine controls (UEC; brief descriptions on these models are provided in Section 3. Phase 1: Phase 1 focuses on the FMECA implementation for identifying the most critical DF engine components.…”

“…To reduce the RPN for the investigated scenarios, the risk reduction measures presented in Table 8 are recommended. In specific, for the scenario S-1, redundant sensors and/or intelligent health monitoring systems with corrective actions capabilities (such as UDS 55 ) may be employed to improve the detectability and reduce the scenario severity. These risk reduction measures are expected to provide an additional layer for the gas (fuel) pressure evaluation, when the ESS compares these measurements against the pre-set values recorded from the engine shop-trials.…”

Mapping the combustion modes of a dual-fuel compression ignition engine

“…While diesel is the standard HRF, the typical LRF's studied in the literature vary by the type of engine being studied. Light-duty land vehicle engines typically use gasoline/ethanol as the LRF, [7][8][9] while marine engines typically usually use methane/natural gas, 10,11 and heavy-duty land vehicle engines have been used to study both of these primary fuel types, gasoline/ethanol, 12,13 and methane/natural gas. [14][15][16][17][18] These are general only general trends; a wide variety of novel LRF's have been studied in a wide variety of engine types, such as a gaseous dimethyl ether (DME)/propane mixture in a light-duty engine.…”

“…19 In addition to ITE and NO x /soot benefits, recent topics of RCCI research in just this journal include compositional effects of premixed LRF, 14 its effects on diesel spray ignition 10 and cyclic variability, 15 and the ability to operate these engines with constrained stochastic control 16 and sensor-based health management. 11 This paper intends to build on the existing state of the art by bridging the advanced mode of RCCI to the conventional mode of CDC via the intermediate mode of PDFC in an optimized multi-mode strategy. This builds off previous work by the same authors 19 to illustrate a continuous link between these modes, while also extending the continuity to other advanced modes such as Premixed Charge Compression Ignition (PCCI).…”