Predicting ship machinery system condition through analytical reliability tools and artificial neural networks

Inadequate ship machinery maintenance can increase equipment failure posing a threat to the environment, affecting performance, having a great impact in terms of business losses by reducing ship availability and increasing downtime and moreover increasing the potential of major accidents occurring and endangering lives on-board. However, machinery condition and fault developing trends are often highly nonlinear and time-series dependent. This paper addresses the above by developing a neural network methodology alongside reliability analysis tools. Critical ship main engine systems/components are used as input in a dynamic time series neural network, in order to monitor and predict future values of physical parameters related to ship critical systems. The critical main engine systems/components and their relevant parameters to be monitored are identified though the combination of Fault Tree Analysis (FTA) and Failure Mode and Effects Analysis (FMEA). A case study of a Panamax size container ship is presented in which Artificial Neural Networks (ANN) are used to predict the upcoming future values of all main engine cylinders exhaust gas temperatures, identified as critical parameters through the FTA and FMEA tools. The suggested methodology alongside the case study results for the main engine system demonstrate that ANN predictions were accurate and can provide the platform for predictive maintenance strategies that can assist decision makers in selecting the correct maintenance actions for critical ship machinery. The case study results for the main engine system demonstrated that the ANN predictions were accurate based on past observations. The proposed methodology successfully presented a systematic approach for identifying critical systems/components through FTA/FMEA and monitoring their physical parameters through the ANN model.