This decade witnesses the privilege of deep learning in intelligent fault diagnosis. However, the limited interpretability poses significant challenges to comprehending and trusting the decision-making mechanisms. To tackle these issues, this paper proposes a knowledge distillation framework that synthesizes dynamic and causal a priori, aiming to capture the fault mechanisms while diminishing confounding side-effect embedded in data. Firstly, this study models rolling bearings using dynamic a priori knowledge, and the simulated data imbued with fault dynamics are then fed into the teacher model training pipeline to yield a deeper understanding of mechanical failures. Secondly, in cope with the probability drift and inconsistency in data collected from variable operating conditions, features undergo weighted fusion according to the causal a priori amongst variables to avoid “correlation trap” in fault classification. Finally, the knowledge distillation module enioys the above two steps adjusting parameters according to both priori simultaneously to achieve a better interpretability in fault diagnosis. In the experiment, the study demonstrates that the proposed framework enhances both the accuracy and interpretability of the model, achieving a remarkable accuracy rate of 95.8% under variable operating conditions.

In modern manufacturing, significant advances in rolling bearing fault diagnosis have been driven by artificial intelligence. However, current intelligent fault diagnosis frameworks struggle to effectively capture causal relationships between time-series data and labels within multicause systems (i.e., complex industrial systems where equipment failures arise from the interplay of multiple coupled causes rather than a single factor), as well as represent the varying significance of features throughout fault progression. These issues lead to a considerable decrease in the diagnostic effectiveness of the model. To address these issues, a causal discovery method combined with a feature weighting strategy is proposed to enhance the model’s generalizability. Specifically, this article proposes the information flow and weighting for domain adaptation framework. First, this study uses information flow techniques to quantify the transfer of information between time-series data variables, thereby determining the causal direction among them. Second, a weighting strategy is developed based on the causal force between different causes. This approach enhances the model’s ability to accurately identify fault characteristics. Finally, data processing and feature weighting modules are integrated into practical applications, resulting in a comprehensive fault diagnosis framework powered by adversarial neural networks. Experimental results demonstrate that this method significantly enhances the fault recognition rate and generalization performance of the model.

Few-shot learning (FSL) techniques have been introduced to address the challenge of limited datasets in mechanical fault diagnosis. However, most existing FSL methods primarily focus on input–output correlations and neglect causal relationships, which limits the interpretability and robustness of diagnostic results. To tackle this issue, we propose a causal disentanglement few-shot relation metric network for mechanical fault diagnosis, comprising feature encoding, causal intervention, causal disentanglement, and relation metric modules. The causal intervention module performs linear interpolation on amplitude information (encoding low-level statistics) while preserving phase information (encoding high-level semantics) to intervene causally on the frequency-domain image. Fault features are extracted via the feature encoder module, and a factor disentanglement loss in the causal disentanglement module transforms them into independent causal features with explicit causal relationships. The relation metric module learns pairwise causal feature distances through meta-task training, thus constructing a trainable similarity metric space. This approach can effectively capture the differences in causal fault features between samples, enhancing the interpretability and generalization ability of the model. Experiments on both public and laboratory datasets demonstrate superior performance over state-of-the-art methods.

Existing few-shot cross-component fault diagnosis methods primarily focus on the correlation between input data and fault classes, neglecting causal relationships. This limits the model's ability to separate and eliminate confounding factors, limiting the improvement of cross-component prediction accuracy. To address this issue, this paper proposes a Causal Intervention Network for Cross-Component Few-Shot Fault Diagnosis (CINet), which constructs a causal structure model to perform causal decomposition, extracting and decoupling the instrumental variable, confounding variable, and adjustment variable from vibration signals, thus enabling the modeling of cross-component causal relationships, which enhances diagnostic accuracy under few-shot conditions. Specifically, the CINet is composed of three main modules: a feature encoding module, a causal disentanglement module, and a relation metric module, jointly optimizing the fault diagnosis and relation metric selection loss functions through multi-task learning. Experimental results on multiple fault diagnosis datasets demonstrate that the CINet significantly outperforms existing methods, especially in handling cross-component fault diagnosis problems, particularly in few-shot scenarios, by better capturing causal relationships and improving prediction accuracy and model interpretability.

In recent years, several few-shot learning (FSL) approaches for industrial equipment fault diagnosis have emerged to tackle the challenges posed by small fault diagnosis datasets. However, the existing FSL approaches model the correlation between input and output variables while ignoring causality, which cannot ensure that the diagnosis results are interpretable and robust. To tackle this problem, this article introduces a causal intervention relation network for cross-component few-shot fault diagnosis from the causal perspective. The model comprises a feature encoding module, a causal intervention module, and a relation measure module. The feature encoding module and the relation measure module establish a trainable similarity metric space through the training of multiple metatasks, where they learn the feature distances between sample pairs. Importantly, in causal intervention module, we model the causal structure of the metalearning process of few-shot fault diagnosis to find the causal fault features and the confounder factor, i.e., the metatraining diagnosis knowledge. Correspondingly a backdoor adjustment approach via a combination of class-based adjustment and feature adjustment is designed to realize the causal calibration of the few-shot fault diagnosis model. In such way, the model can capture causal invariant features between various components with significant distributional differences, thus enhancing the model's interpretability and its capacity for generalization. We perform experiments on two openly accessible datasets and a dataset constructed in our laboratory. The experimental results demonstrate that the model outperforms existing state-of-the-art approaches.

As a new federated learning(FL) paradigm, clustered federated learning (CFL) could effectively address the issue of model training accuracy loss due to different data distribution in FL. However, the introduction of the clustering process also brings new risks. Adversaries can implement model poisoning by adding crafted perturbations with clients' model parameters, potentially resulting in overall clustering failure. To tackle this problem, we propose a client detection and parameter correction framework in this paper. Our approach aims to identify malicious clients by analyzing the difference in vector parameter density distribution between malicious and benign clients. We precisely locate malicious perturbations in the parameters and recover them, enabling the server to effectively utilize benign updates for normal clustering and training within the CFL framework. Experiment results show that our defense algorithm outperforms others, consistently improving training accuracy by an average of 30% under various kinds of attacks.

Several deep learning methods have emerged for fault diagnosis of industrial equipment in recent years. However, the realistic dataset is often much smaller than the benchmark diagnostic dataset due to the difficulty of fault data collection and labeling in realistic scenarios. Moreover, the collected fault data may come from various components with different fault categories. Therefore, existing deep-learning-based models have poor generalization capabilities for cases with only a few data when faced with new components. Herein, a triplet relation network (TRNet) is proposed for cross-component few-shot fault diagnosis by learning from several related meta-tasks iteratively. We construct a dual-channel feature embedding module with shared weights to extract fault features and a relation metric module to adaptively measure the feature similarity of sample pairs. Furthermore, in order to distinguish the most dissimilar samples in the same category (i.e., hard positive samples) and the most similar samples in different categories (i.e., hard negative samples), the hard sample recognition module is designed, combined with a triplet loss, to weaken the hard-to-discriminate feature of hard sample pairs, such that the TRNet are capable for task learning and feature learning to improve classification accuracy on target components. We conduct experiments on two publicly available datasets and one lab-built datasets. We validate the proposed method to classify with one, three, or five instances in each category of the target component. The results demonstrate that the fault diagnosis performance of our model is superior to the state-of-the-art methods.

Due to less degradation data and the inconsistent data distribution of different bearings, remaining useful life (RUL) prediction methods based on deep learning still do not yield satisfactory predictive results. Using RUL prediction model trained with one bearing sample but tested with another bearing sample is challenging. To solve this problem, in this paper a new deep transfer learning-based RUL prediction method (DTL-RULPM) is proposed. We adopt min-max normalization to normalize the original vibration data of bearing. A three-layer sparse autoencoder is designed to extract the deep features of the source domain. Random data with standard normal distribution is generated with the consistent dimension of the high-dimensional features of the source domain. Maximum mean discrepancy (MMD) is used to minimize the probability distribution distance between the features of the source domain and the randomly generated data, such that the model can learn domain-invariant features of different bearings. Then we adopt a bi-directional long and short-term memory (Bi-LSTM) network to predict the RUL of the bearing. We use the IEEE PHM Challenge 2012 dataset to verify the proposed method. The results demonstrate that the proposed method improves the RUL prediction accuracy and robustness of different bearings.

In recent years, transfer learning has been widely used in cross-domain fault diagnosis to solve the problem of insufficient training data. Existing studies focus on the homogeneous transfer fault diagnosis of the same component with different operating conditions. However, when the source and target domain are from two different components, the feature space and category space of the two domains are completely different, which causes the challenging problem of unsupervised heterogeneous transfer fault diagnosis. We propose a graph Laplacian common subspace based unsupervised heterogeneous transfer learning model (GL-HTLM). Firstly, pseudo-labels are designed for the unlabeled samples of target domain using the Gaussian mixture model to learn the distribution characteristics of the original vibration signals. Secondly, a deep convolutional neural network is designed to extract the high-dimensional features of the labeled samples of source domain and the pseudo-labeled samples of target domain. Finally, a common latent attributes space (CLAS) is generated through near-binary feature representation learning to extract the latent attributes of the source and target domain. According to the similarity of any two samples in CLAS, we further define graph Laplacian loss to maximize the inter-category distances while minimizing the intra-category distances. Therefore, the two domains with different category spaces are strongly consistent in the CLAS, so as to classify samples of two domains. In order to validate the proposed method, four heterogeneous transfer fault diagnosis experiments are carried out using bearing dataset and gear dataset. Results demonstrate that our proposed model is superior to existing methods.

The compound fault signal of bearings is coupled and complex, thereby compound fault diagnosis is a difficult problem in bearing fault diagnosis. The existing deep learning models can extract fault features when there are a large number of labeled compound fault samples. In the industrial scenarios, collecting and labeling sufficient compound fault samples are unpractical. Using the model trained on single fault sample to identify unknown compound fault is challenging and innovative. To address this problem, we propose a Zero-shot Learning Compound Fault Diagnosis Model of bearing (ZLCFDM). First, we design a semantic encoding method to express the semantic vectors of single fault and compound fault according to the fault characteristics. Second, a convolutional neural network is designed to extract the time-frequency visual features of compound fault signal. Then we embed the semantic vector of the fault into the visual space of the fault data. The cosine distance is merged into K-nearest neighbor (KNN) to measure the distance between the visual features and the semantic vectors of the compound faults, such that the model can identify the categories of unknown compound faults. To validate the proposed method, we conduct experiments on self-built testbed. The results demonstrate that the identification accuracy of compound fault can reach 77.73% when the model trained without any compound fault samples. This is the first time to propose the compound fault diagnosis of bearing base on zero-shot learning.

With the emergence and development of various computer technologies, many jobs processed in cloud computing systems consist of multiple associated tasks which follow the constraint of execution order. The task of each job can be assigned to different nodes for execution, and the relevant data are transmitted between nodes to complete the job processing. The computing or communication capabilities of each node may be different due to processor heterogeneity, and hence, a task scheduling algorithm is of great significance for job processing performance. An efficient task scheduling algorithm can make full use of resources and improve the performance of job processing. The performance of existing research on associated task scheduling for multiple jobs needs to be improved. Therefore, this paper studies the problem of multijob associated task scheduling with the goal of minimizing the jobs’ makespan. This paper proposes a task Duplication and Insertion algorithm based on List Scheduling (DILS) which incorporates dynamic finish time prediction, task replication, and task insertion. The algorithm dynamically schedules tasks by predicting the completion time of tasks according to the scheduling of previously scheduled tasks, replicates tasks on different nodes, reduces transmission time, and inserts tasks into idle time slots to speed up task execution. Experimental results demonstrate that our algorithm can effectively reduce the jobs’ makespan.

Edge computing, which sinks a large number of complex calculations into edge servers, can effectively meet the requirement of low latency and bandwidth efficiency and can be conducive to the development of the Internet of Vehicles (IoV). However, a large number of edge servers mean a big cost, especially for the 5G scenario in IoV, because of the small coverage of 5G base stations. Fortunately, coherent beamforming (CB) technology enables fast and long‐distance transmission, which gives us a possibility to reduce the number of 5G base stations without losing the whole network performance. In this paper, we try to adopt the CB technology on the IoV 5G scenario. We suppose we can arrange roadside nodes for helping transferring tasks of vehicles to the base station based on the CB technology. We first give the mathematical model and prove that it is a NP‐hard model that cannot be solved directly. Therefore, we design a heuristic algorithm for an Iterative Coherent Beamforming Node Design (ICBND) algorithm to obtain the approximate optimal solution. Simulation results show that this algorithm can greatly reduce the cost of communication network infrastructure.