In mechanical fault diagnosis (MFD), intelligent fault diagnosis (IFD) methods perform excellently regarding diagnosis accuracy. However, those methods are generally constructed with an excessive number of unprincipled parameters, resulting in uninterpretable architecture, ambiguity in the diagnosis process, and unclear decision-making basis. Thus, a traceable algorithm unrolling (TAU) network for interpretable MFD is proposed to overcome the above limitations. First, a mechanism-driven feature extractor (FE) is constructed by unrolling the iterative algorithm of sparse coding, aiming at encoding interpretable features from vibration signals. Second, a theory-based feature clustering (FC) algorithm is executed through the dynamic routing mechanism of the capsule network (CN), where the inner product serves as a measure for the association between input and output features. Finally, a post hoc interpretability strategy based on the coupling matrix is introduced to investigate how the TAU generates diagnostic results from the learned features and to verify whether these features are associated with faults, thereby enhancing the credibility of the diagnosis results. In addition, the simulation and experiment are designed to verify the decision-making mechanism and diagnostic performance of TAU. The results demonstrate that TAU makes diagnostic decisions based on the high-dimensional feature mapping associated with fault characteristic frequencies. Meanwhile, TAU outperforms the compared methods in fault diagnosis performance.

The development of Internet of Things technology provides abundant data resources for prognostics health management of industrial machinery, and data-driven methods have shown their powerful ability in the field of fault diagnosis. However, these methods have several limitations: 1) Using less labeled data to obtain higher accuracy is a challenging task, which limits the application of diagnostic models in practical applications. 2) Physics-informed knowledge is largely ignored during the modeling process, which contains a wealth of information that can reflect the harmonic drive’s health status. To address these challenges, a self-supervised fault diagnosis framework is developed by integrating prior knowledge with deep learning to improve the accuracy and reliability of diagnosis models in industrial applications. Specifically, the physics-based knowledge including 32-dimensional time domain, frequency domain, and time-frequency domain features, is first designed to provide fault information and significantly reduce the amount of data required for deep learning. Furthermore, a self-supervised knowledge embedded auto-encoder network is built by employing the prior knowledge in the multi-scale convolutional auto-encoder. With the ability to integrate prior knowledge and the self-supervised learning mechanism, the proposed method can provide a strong tool for knowledge representation and an effective solution for fault diagnosis under a few-shot industrial scenario. The experimental results conducted on a real harmonic drive fault dataset prove that the proposed network framework provides effective insights on fault diagnosis and has excellent generalizability in practical industrial applications.

In real industries, there often exist application scenarios where the target domain holds fault categories never observed in the source domain, which is an open-set domain adaptation (DA) diagnosis issue. Existing DA diagnosis methods under the assumption of sharing identical label space across domains fail to work. What is more, labeled samples can be collected from different sources, where multisource information fusion is rarely considered. To handle this issue, a multisource open-set DA diagnosis approach is developed. Specifically, multisource domain data of different operation conditions sharing partial classes are adopted to take advantage of fault information. Then, an open-set DA network is constructed to mitigate the domain gap across domains. Finally, a weighting learning strategy is introduced to adaptively weigh the importance on feature distribution alignment between known class and unknown class samples. Extensive experiments suggest that the proposed approach can substantially boost the performance of open-set diagnosis issues and outperform existing diagnosis approaches.

In practical industrial applications, the working conditions of machinery are changing with long-term operation, and the health status is declining with the degradation of crucial components. When the working condition changes, prior diagnosis models cannot be generalized from one condition to another. To solve this challenging issue, in this article a robust weight-shared capsule network (WSCN) is introduced for intelligent fault diagnosis of machinery under varying working conditions. First, taking raw accelerometer signals as inputs, one-dimensional convolutional neural network is constructed to extract discriminative characteristics. Second, various capsule layers based on multistacked weight-shared capsules are developed to enhance the generalization performance for further fault classification. Finally, margin loss function as well as agreement-based dynamic routing algorithm are employed to optimize the WSCN. In this article, two diagnosis cases are carried out to demonstrate the generalization performance of the WSCN which obtains higher accuracy under varying working conditions than that of other state-of-the-art methods.