A reliable degradation indicator (DI) and an effective degradation model are essential prerequisites for accurate remaining useful life (RUL) prediction of rolling bearings. However, numerous existing DI construction methods and degradation models overlook the fatigue damage mechanism—a core physical mechanism governing rolling bearing failure—which hinders the effective extraction of key degradation information from raw monitoring signals. To address this gap, this paper proposes a physics-driven RUL prediction method for rolling bearings that incorporates Paris' fatigue damage mechanism. Specifically, a multi-scale stackable network model constrained by Paris' fatigue damage mechanism-based soft labels is constructed to resolve the issues of poor interpretability and lack of physical constraints in conventionally built DIs. Furthermore, a rolling bearing degradation model is established by integrating the Paris' fatigue damage mechanism with stochastic processes, which mitigates the problem of weak correlation between the DI and the degradation model. The proposed method is validated through two sets of experiments and compared with other state-of-the-art methods. The comparative results demonstrate that the DI constructed under the constraint of Paris' fatigue damage mechanism exhibits excellent comprehensive performance, and the physics-driven prediction method achieves high accuracy in rolling bearing RUL estimation.

Domain adaptation (DA) enhances the scalability of remaining useful life (RUL) prediction technologies, providing a reliable foundation for maintenance decisions across diverse equipment within Industrial Internet of Things (IIoT). Traditional DA approaches typically necessitate simultaneous access to both source and target domain data, which often conflicts with data privacy concerns prevalent in IIoT. Furthermore, the substantial storage resources required for source domain data hinder the implementation of efficient DA on resource-constrained edge devices. To address these challenges, we propose a source-free DA (SFDA) framework leverages uncertainty estimation pseudo-labels to conduct cross-domain RUL prediction in the absence of source domain data. Initially, we develop a self-supervised knowledge distillation framework that adapts efficiently to domain shift based on pseudo-labeling. Building on this, we propose an uncertainty estimation pseudo-label guided loss reweighting strategy to mitigate the impact of pseudo-label noise. This strategy prioritizes highly reliable pseudo-labels by assessing discrepancies in feature distributions from different augmented views of the input samples. Additionally, a reconstruction-based training method is designed to align the feature distributions. Extensive experimental evaluations demonstrate that our proposed method outperforms current state-of-the-art techniques, ensuring reliable RUL predictions in scenarios characterized by domain shift and the absence of source domain data.

Developing a trustworthy framework for intelligent fault diagnosis (IFD) of machines has two major challenges: confidently recognizing known faults and precisely detecting novel faults. However, current IFD frameworks are typically based on the closed-world assumption and tackle the two issues independently, making it hard to meet the expectations of reliable diagnosis in complicated working situations. In this paper, aProbabilistic framework forMechanical fault diagnosis (ProMo) is proposed to integrate misidentification and out-of-distribution (OOD) detection for high-speed train bogie. ProMo uses variational parameters to capture uncertainties at both the feature and prediction levels, in this way the misclassified and OOD samples with high predictive uncertainty are differentiated. To begin, a Bayesian deep neural network with a hierarchical classifier is built, offering diverse predictions and enabling ensemble uncertainty estimate. Then, a probabilistic null space analysis technique for post hoc OOD detection is presented, in which the magnitude of feature projections indicates the OOD-ness. Additionally, a novel metric assessing classification correctness and prediction reliability simultaneously is proposed. Extensive experiments revealed that ProMo outperforms state-of-the-art methods and achieves reliable fault diagnosis, even in the presence of covariate shift in monitoring data.

Deep learning has demonstrated splendid performance in mechanical fault diagnosis on condition that source and target data are identically distributed. In engineering practice, however, the domain shift between source and target domains significantly limits the further application of intelligent algorithms. Despite various transfer techniques proposed, either they focus on single-source domain adaptation (SDA) or they utilize multisource domain globally or locally, which both cannot address the cross-domain diagnosis effectively, especially with category shift. To this end, we propose globally localized multisource DA for cross-domain fault diagnosis with category shift. Specifically, we construct a GlocalNet to fuse multisource information comprehensively, which consists of a feature generator and three classifiers. By optimizing the Wasserstein discrepancy of classifiers locally and accumulative higher order multisource moment globally, multisource DA is achieved from domain and class levels thus to reduce the shift on domain and category. To refine the classifier at sample level, a distilling strategy is presented. Finally, an adaptive weighting policy is employed for reliable result. To evaluate the effectiveness, the proposed method is compared with multiple methods on four bearing vibration datasets. Experimental results indicate the superiority and practicability of the proposed method for cross-domain fault diagnosis.

Industrial Internet of Things (IIoT) is mainly a data-oriented network, so intelligent processing of massive data is desiderated to realize the interconnection between machines. Currently, deep-learning-based methods are widely applied for intelligent construction of the IIoT, so as to maximize the self-monitoring and self-management capabilities of various machines. However, the quantity and quality of data and the optimization of parameters greatly limit the properties of such methods. As a breakthrough of artificial intelligence (AI), deep reinforcement learning (DRL) provides inspiration and direction, which combines the advantages of deep learning and reinforcement learning to construct an end-to-end fault identification system. Therefore, a novel deep dual reinforcement learning model was proposed, which consisted of an actor model and a critic model. The dual structures avoid the over-self-optimization of the network. The action model continually learns the knowledge of identifying unknown samples by the$\varepsilon $-$greedy$algorithm, while the critic model dynamically adjusts the policy to guide the action model in right training direction. The effectiveness of the proposed method was verified by three bearing data sets. The results indicate that the proposed method enables agents to independently realize precise fault quantitative identification. The establishment of an experience storage unit overcomes the problem of insufficient samples, which avoids blind trial and error of the proposed mode.

Class imbalance issue has been a major problem in mechanical fault detection, which exists when the number of instances presenting in a class is significantly fewer than that in another class. This article focuses on the problem of zero-shot fault detection of rolling bearing, which is the extreme case of class imbalance. Aiming at this problem, a two-stage zero-shot fault recognition method is proposed. First, inspired by the conditional generative adversarial network, a novel feature generating network which is composed of a feature extractor, a discriminator, and a generator is designed to capture the potential distribution of normal samples. Then, the generator will generate abundant pseudofault features by adding an additional sequence to the condition. Second, an improved deep neural network is trained with these synthetic pseudofault features as the classifier. Specially, a condition index is designed to represent different fault classes so that it can recognize the unseen fault samples. Finally, the effectiveness of the proposed method is verified by three datasets and a comparison method is also given to show the superiority. Results show that the feature generation network can effectively detect the typical faults even though the fault data are unavailable during training, which is practical for industrial application.

Data-driven intelligent method has shown superior performance in remaining useful life (RUL) prediction. However, the model training is difficult due to the limited degradation data. To address the challenges of small data set, a Sequence Adaptation Adversarial Network (SAAN) is proposed in this paper. SAAN could expand training data with auxiliary set by sequence domain adaption. We verify the proposed method with C-MAPSS dataset. By comparing with the literature methods, results show SAAN could significantly improve the accuracy of RUL prediction under small data set, and also keeps a competitive performance on sequence life prediction.

Recently, intelligent fault diagnosis has achieved fruitful research results. However, the small sample is still the major problem in fault diagnosis owing to lacking fault data of machines. In view of this, a signals augmented semi-supervised learning scheme is proposed for intelligent fault diagnosis in the case of small sample. In the proposed method, fault signal samples are generated by generative adversarial networks (GAN). The fault classifier is trained in a semi-supervised way using the generated samples and a small number of real samples. Besides, attention mechanism is applied in the fault classifier for sensitive feature extraction. The trained fault classifier is capable of accurate fault classification. Results indicate that the proposed method is effective in mechanical fault diagnosis under the small sample condition.

Data imbalance is a major problem in intelligent fault diagnosis. Aiming at this problem, the paper proposed a novel adversarial learning framework for zero-shot fault recognition of mechanical systems. The proposed network consists of three parts which are the feature extractor, the generator and the discriminator. Trained with normal samples, the proposed method is capable of generating unseen fault samples by changing the condition of the generator. After, these synthetic samples are used to train an improved deep neural network for fault recognition. Results show that the proposed method can recognize the unseen faults even though none of fault samples are available during training, which is meaningful for industry application.

Intelligent fault detection is an important application of artificial intelligence and has been widely used in many mechanical systems. The shipborne antenna that is a typical and an important mechanical system plays an irreplaceable role in ships. Considering the tough working environment and heavy background noise, fault detection is difficult for the shipborne antenna. Therefore, the paper presents an intelligent fault detection method via multiscale inner product with locally connected feature extraction for shipborne antenna fault detection. Inspired by inner product principle, this paper takes advantage of inner product to capture fault information in the vibration signals and detect the faults in rolling bearing of the shipborne antenna. Meanwhile, multiscale analysis is employed in two layers of the network to improve the feature extraction ability. The local features under different scales are collected and used for fault classification. Finally, the proposed method is verified by three datasets and comparison methods are also developed to show its superiority. Results show that the proposed method can learn sensitive features directly from raw vibration signals and detect the faults in rolling bearing of shipborne antenna effectively.