The data-driven methods in machinery fault diagnosis have become increasingly popular in the past two decades. However, the wide applications of this scheme are generally compromised in real-world conditions because of the discrepancy between the training data and testing data. Although the recently emerging transfer fault diagnosis can learn transferable features from relevant source data and adapt the diagnostic model to the target data, these methods still only work on the target domain with a priori data distribution. The generalization capability of the transferred model cannot be guaranteed for unseen domains. Since the working conditions of machinery are varying during operation, the generalization capability of the diagnosis methods is crucial in this case. To tackle this challenge, this article proposes a domain generalization-based hybrid diagnosis network for deploying to unseen working conditions. The main idea is to regularize the discriminant structure of the deep network with both intrinsic and extrinsic generalization objectives such that the diagnostic model can learn robust features and generalize to unseen domains. The triplet loss minimization of intrinsic multisource data is implemented to facilitate the intraclass compactness and the interclass separability at the class level, leading to a more generalized decision boundary. The extrinsic domain-level regularization is achieved by using adversarial training to further reduce the risk of overfitting. Extensive cross-domain diagnostic experiments on planetary gearbox demonstrate the effectiveness of the proposed method.

Automated Driving Systems ( ADS ) have made great achievements in recent years thanks to the efforts from both academia and industry. A typical ADS is composed of multiple modules, including sensing, perception, planning, and control, which brings together the latest advances in different domains. Despite these achievements, safety assurance of ADS is of great significance, since unsafe behavior of ADS can bring catastrophic consequences. Testing has been recognized as an important system validation approach that aims to expose unsafe system behavior; however, in the context of ADS, it is extremely challenging to devise effective testing techniques, due to the high complexity and multidisciplinarity of the systems. There has been great much literature that focuses on the testing of ADS, and a number of surveys have also emerged to summarize the technical advances. Most of the surveys focus on the system-level testing performed within software simulators, and they thereby ignore the distinct features of different modules. In this article, we provide a comprehensive survey on the existing ADS testing literature, which takes into account both module-level and system-level testing. Specifically, we make the following contributions: (1) We survey the module-level testing techniques for ADS and highlight the technical differences affected by the features of different modules; (2) we also survey the system-level testing techniques, with focuses on the empirical studies that summarize the issues occurring in system development or deployment, the problems due to the collaborations between different modules, and the gap between ADS testing in simulators and the real world; and (3) we identify the challenges and opportunities in ADS testing, which pave the path to the future research in this field.