Corrosion, fatigue and corrosion-fatigue cracking are the most pervasive types of structural problems experienced by ship structures. These damage modes, can potentially lead to unanticipated out of service time or catastrophic failure. Acoustic Emission is gaining ground as a complementary Structural Health Monitoring (SHM) technique, since it can offer real-time damage detection. Deep learning, on the other hand, has shown great success over the last years for a large number of applications. In this paper, the SHM on ship hulls is treated as a classification problem. Firstly, the AE signals are transformed, using the Discrete Cosine Transform, followed by a dimensionality reduction stage. Afterwards, a Deep Neural Network is employed by the classification module. The proposed approach was validated and the results indicate that our proposed method can be very effective and efficient, selecting the optimum AE sensor positions and providing almost perfect localisation results.

In the present paper, temperature measurements are utilized to develop health indicators based on principal component analysis towards the probabilistic estimation of the Remaining Useful Life (RUL) of reciprocating compressors in service. Temperature degradation histories obtained from thirteen actual valve failure cases constitute the training data in a data-driven prognostic approach. Two data-driven prognostic methodologies are presented and proposed based on probabilistic mathematical models i.e. Gradient Boosted Trees (GBTs) and Non-Homogeneous Hidden Semi Markov Models (NHHSMM). The training and testing process of all models is described in detail. RUL prognostics in unseen data are obtained for all models. Beyond the mean estimates of the RUL, the uncertainty associated with the point prediction is quantified and upper/lower confidence bounds are also estimated. Prediction estimates for twelve real-life failure cases are presented and the pros and cons of each model's performance are highlighted. Several metrics are utilized to assess the performance of the prognostic algorithms and conclusions are drawn regarding the prognostic capabilities of each of them.