An ensemble deep transfer learning method for fault diagnosis based on soft voting is proposed for diagnosing waterjet pump faults under variable working conditions. The source domain and few target domain data samples are normalized after FFT transformation and then fed into three deep transfer learning diagnosis models for training: the CORAL based deep transfer metric learning model, the MMD based deep transfer metric learning model, and the transfer component-based deep belief network. The target domain test samples are diagnosed and analyzed based on this approach. An ensemble deep transfer diagnosis model is subsequently established by combing the soft voting method to obtain the final diagnosis results. Through the diagnosis of three different types of faults in waterjet pumps under variable working conditions, the results show that the proposed ensemble deep transfer diagnosis model not only effectively addresses the high-precision fault diagnosis of waterjet pumps under variable working conditions, but also has better diagnostic accuracy than the single deep transfer fault diagnosis model.

Fire compartmentation is one of the most important and complicated ship general design work, especially for the passenger ships, such as the cruise ship and Ropax with large number of rooms. The traditional design method, which is based on two-dimensional (2D) drawings and human subjective judgement, costs heavy workload and is likely to induce mistakes. A three-dimensional (3D) assisted fire compartmentation method for ship cabins has been proposed based on the secondary development of the 3D modelling software UG through the programming analysis of the specification requirement logics. This method embeds the relevant knowledge requirements of the fire protection rules. It fulfils the functions of customizing the categories of the ship cabins, automatically creating the common interface of adjacent cabins, and automatically setting the common interface’s fire protection level, which is highlighted in different colors, according to the regulations. It also counts the usage of different fire protection levels, which can lay the foundation for helping the designers to optimize the cabin arrangement based on the objective of reducing fire protection materials.