Fault Prognostics Using LSTM Networks: Application to Marine Diesel Engine

Abstract

Maintenance is the key to ensuring the safe and efficient operation of marine vessels. Currently, reactive maintenance and preventive maintenance are two main approaches used onboard. These approaches are either cost-intensive or labor-intensive. Recently, Prognostics and Health Management has emerged as an optimal way to manage maintenance operations. In such a system, fault prognostics aims to predict the remaining useful life based on the sensor measurements. In this paper, the feasibility of applying data-driven fault prognostics to marine diesel engines is investigated. Real-world run-to-failure data of two independent fault-types in two different engine load profiles are collected from a hybrid power lab. The first profile is used for training and validation, while the second is used for testing. The LSTM networks are used to construct the fault prognostics model. Experiments and comparisons are performed to obtain the optimal structure of the networks. Results show that the proposed method generalizes well on the second profile and provides remaining useful life predictions with high accuracy.

Health assessment framework of marine engines enabled by digital twins

“…International J of Engine Research 00(0) where in the matrix G, _ Q e, loss denotes the heat losses from the exhaust manifold and h AC denotes the air cooler enthalpy, calculated by considering a typical air cooler effectiveness model as a function of air mass flow rate. 35 Subsequently, equation (13), can be combined with the multi-cylinder model formulation in equation ( 12), to provide the thermodynamics model formulation of a turbocharged engine with N cylinders, according to equation ( 14):…”

“…These equations are presented in detail in Appendix I. Subsequently the turbocharger and manifold equations are gathered in a matrix system according to equation (13).…”

Instillation checking using long short‐term memories for ophthalmology patients

“…The system 14 is evaluated, using an actual case application (i.e., the orders of IC trays for semiconductor production plants). In Reference 15, the feasibility of applying data‐driven fault prognostics to marine diesel engines is investigated, using the fault prognostics model constructed with LSTMs. This model achieves high accuracy for remaining useful life predictions of the engine.…”

Combination of Optimized Variational Mode Decomposition and Deep Transfer Learning: A Better Fault Diagnosis Approach for Diesel Engines

“…The parameters of the CNN were optimized by Zhou et al [24], by utilizing the sorting method of input measurement parameters, which was applied to detect the fault of the gas circuit of an engine with a relatively ideal diagnosis impact. Han et al [25] constructed a data-driven fault prediction model using an LSTM network and applied it to marine diesel engines, and obtained better fault prediction results.…”