A performance degradation evaluation method for a turbocharger in a diesel engine

Abstract

As one of the key systems of the marine power plant diesel engine, the turbocharger directly affects whether the diesel engine can continuously and stably provide the power required for the ship. Owing to a number of uncontrollable factors, such as harsh working conditions and complex structures, the turbocharger may have various failures, causing it to lose its intended function. At present, the fault diagnosis of the marine turbocharger has not been paid enough attention yet and in most cases, the method of ‘ex post diagnosis’ is still adopted. When analysing the nonlinear correspondence between the failure symptoms and failure causes, it is difficult for the existing theories to meet the actual diagnostic requirements. This paper introduces the concept of gas-path diagnosis into the condition monitoring for a marine turbocharger for the first time and proposes the flow capacity index which characterizes the flow capacity of the component and the isentropic efficiency index which characterizes the operating efficiency of the component as two dimensionless evaluation indicators for turbocharger health status. Moreover, the nonlinear mapping relationship between these two health parameters and the gas-path measurable parameters of the turbocharger is studied, and a novel performance degradation evaluation method for a turbocharger is established. The proposed method has been tested in three test cases where the degradation of a model turbocharger has been analysed. These case studies have illustrated that the proposed method can accurately isolate the degraded components and further quantify the degradation of the components.

Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy

“…Yang et al used the dual CNN model to intelligently predict RUL only using the original signal [205]. Cui et al proposed an adaptive performance degradation assessment method of marine turbochargers based on component generalized feature mapping [267]. Zhang et al used long short-term memory (LSTM) recurrent neural networks (RNN) to predict RUL independently of offline training data and can predict RUL earlier than traditional methods when offline data are available [268].…”

Integrated Simulation Framework for Assessing Turbocharger Fault Effects on Diesel-Engine Performance and Operability

“…Sakellaridis and Hountalas (2013) also developed a radial turbine mean line code for being a part in a T/C diagnostic tool with the ability of adapting to available measured data. Cui et al (2018) developed a gas-path diagnosis for diesel engine turbochargers, using health factors (flow capacity and isentropic efficiency), hence monitoring the T/C health status.…”