Deep learning based intelligent fault detection for mechanical equipment has become an important research fields. However, due to various equipment and working conditions, it is difficult to collect sufficient fault samples by monitoring sensors, which restricts the accuracy of existing intelligent fault diagnostic approaches. In this paper, we propose a novel generative adversarial networks (GAN) based on local weights-shared multi-generator for the generation of fault data and building a fault diagnosis model. Unlike existing GAN based data augmentation methods, the proposed model contains multiple generators which are divided into several groups with each groups’ prior layers of generators weights-shared. By adversarial training, the discriminator gradually improves the performance of judging authenticity and fault categories with each group of generators acquiring the ability to generate a type of fault data. In this way, the proposed local shared multiple-generator GAN method effectively reduces mode collapse and generates more diverse fault data. The performance of the proposed method was validated on the fault datasets that are not applied in training stage. Compared with traditional deep learning diagnosis methods, the accuracy of fault diagnosis could be improved up to 30%, which indicates that the data generation model can generate diverse data and expand the fault data space.

Bearing faults are among the most common causes of machine failures. Therefore, bearing fault diagnosis should be performed reliably and rapidly. Currently, many types of modal data for monitoring the running state of bearings are available. They include data from different kinds of sensors and various domains (such as time and frequency domains). However, obtaining fault features with high quality from single-modal data is difficult because of the complex working conditions. Therefore, extracting and integrating complementary fault features from multimodal monitoring data are problems that remain to be solved. To address these issues, this study considers vibration and torque signals and proposes a novel three-stage feature fusion method of multimodal data for the fault diagnosis of bearings. The method is called attention-based multidimensional concatenated convolutional neural network. The attention mechanism can learn global information and assign different weights to feature maps to highlight important features. The fused features are then used in fault classification via softmax regression. The effectiveness of the proposed method is verified through the Paderborn data set. Results show that diagnostic accuracy is significantly improved from 96.4% to 99.8%.

Maritime transport plays a vital role in economic development. To establish a vessel scheduling model, accurate ship maneuvering models should be used to optimize the strategy and maximize the economic benefits. The use of nonparametric modeling techniques to identify ship maneuvering systems has attracted considerable attention. The Gaussian process has high precision and strong generalization ability in fitting nonlinear functions and requires less training data, which is suitable for ship dynamic model identification. Compared with other machine learning methods, the most obvious advantage of the Gaussian process is that it can provide the uncertainty of prediction. However, most studies on ship modeling and prediction do not consider the uncertainty propagation in Gaussian processes. In this paper, a moment-matching-based approach is applied to address the problem. The proposed identification scheme for ship maneuvering systems is verified by container ship simulation data and experimental data from the Workshop on Verification and Validation of Ship Maneuvering Simulation Methods (SIMMAN) database. The results indicate that the identified model is accurate and shows good generalization performance. The uncertainty of ship motion prediction is well considered based on the uncertainty propagation technology.

Intelligent fault diagnosis is an important subject of mechanical system maintenance. Domain adaptation is a method to solve the problem that the model trained on the training set (source domain) is not suitable for the test set (target domain) due to different working conditions in fault diagnosis. In industrial scenarios, there may be multiple-source domains. For this reason, we proposed an intelligent fault diagnosis system (IFDS) with multisource unsupervised domain adaptive network that adapts to single- or multiple-source domains. The proposed method considers the differences between sources and uses source domain data and a small amount of unlabeled target domain data to mine the feature information contained in the data. IFDS uses a feature extractor to learn the feature representations, constructs a domain discriminator for each source domain, and learns domain-invariant features through adversarial training to diagnose target domain faults. The validity of the method in fault diagnosis is verified by various transfer tasks of two public fault-bearing datasets.