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Remaining useful life (RUL) prediction is an essential component for prognostics and health management of a system. Due to the powerful ability of nonlinear modeling, deep learning (DL) models have emerged as leading solutions by capturing temporal dependencies within time series sensory data. However, in RUL prediction tasks, data are typically collected from multiple sensors, introducing spatial dependencies in the form of sensor correlations. Existing methods are limited in effectively modeling and capturing the spatial dependencies, restricting their performance to learn representative features for RUL prediction. To overcome the limitations, we propose a novel LOcal–GlObal correlation fusion-based framework (LOGO). Our approach combines both local and global information to model sensor correlations effectively. From a local perspective, we account for local correlations that represent dynamic changes of sensor relationships in local ranges. Simultaneously, from a global perspective, we capture global correlations that depict relatively stable relations between sensors. An adaptive fusion mechanism is proposed to automatically fuse the correlations from different perspectives. Subsequently, we define sequential micrographs for each sample to effectively capture the fused correlations. Graph neural network (GNN) is introduced to capture the spatial dependencies within each micrograph, and the temporal dependencies between these sequential micrographs are then captured. This approach allows us to effectively model and capture the dependency information within the data for accurate RUL prediction. Extensive experiments have been conducted, verifying the effectiveness of our method.

Federated learning (FL) has emerged as an effective machine-learning paradigm for collaborative machine fault diagnosis in a privacy-preserving scheme. However, due to the perception limitation and different annotation criteria of annotators, the data in clients may have noisy labels with varied noise levels, leading to degraded FL performances. Most existing methods in FL for tackling the label noise issue, assume that there is label noise in all clients and treat all clients with the same denoising training. However, these methods may result in sub-optimization and even training instability of local models, so that they cannot perform well on heterogeneous label noise across clients in FL. To address this issue, we propose a curriculum-based federated learning (called FedCNL) method to combat the heterogeneous label noise in FL settings. First, our proposed FedCNL exploits a noise modeling module to adaptively estimate the clean clients and noisy clients, and identify the clean samples and noisy samples in noisy clients in an unsupervised manner. Then, a multi-stage curriculum learning is designed by regarding the noise level as learning complexity, where the model learns from clean to noisy samples, gradually improving the performance of the global model. Moreover, a mixed loss correction method is explored in the curriculum stage to maximize the utilization of data with noisy labels. Experiments performed on fault datasets in non-identically and independently distributed settings indicate that our proposed method addresses the label noise issue for machine fault diagnosis in heterogeneous FL with favorable effectiveness, achieving state-of-the-art performances.

Numerous approaches have been proposed for predicting machine remaining useful life (RUL), which helps prevent unnecessary downtime and reduces the maintenance cost in industrial systems. Most existing RUL methods rely on centralized learning and require large-scale datasets with manual labels, which are infeasible to collect. As a decentralized learning paradigm, federated learning (FL) has recently been integrated into these approaches, which aims to utilize the distributed data from local users for model training, while preserving their data privacy. However, data heterogeneity in the industry poses a critical challenge for FL, leading to model drifting issue and degraded global model performance. A straightforward method to tackle this problem is to estimate the data distribution of clients. However, it is difficult to apply this method to a regression task, since the prediction space is continuous, which increases the difficulty in estimating the data distribution. Motivated by the digital-to-analog converter in electronics, we propose a novel approach called FedCov, which involves a converter module that transforms continuous RUL values into discrete categories. Subsequently, a generator is trained to aggregate user information based on the discrete label distribution, and it is broadcasted to users as a data enhancement tool to address the data heterogeneity problem. Furthermore, to improve the performance and reliability of our FedCov, an uncertainty estimation module is proposed, which utilizes the confidence level of model predictions to adjust the training direction. Extensive experiments are conducted on the C-MAPSS benchmark, which demonstrates that our proposed FedCov effectively solves the model drift issues and improves the performances on the RUL task, achieving state-of-the-arts performances.

Transformer has shown impressive performance on global feature modeling in many applications. However, two drawbacks induced by its intrinsic architecture limit its application, especially in fault diagnosis. First, the quadratic complexity of its self-attention scheme extremely increases the computation cost, which poses a challenge to apply Transformer to a computationally limited platform like an industry system. In addition, the sequence-based modeling in the Transformer increases the training difficulty and requires a large-scale training dataset. This drawback becomes serious when Transformer is applied in fault diagnosis where only limited data is available. To mitigate these issues, we rethink this common approach and propose a new Transformer, which is more suitable for fault diagnosis. In this article, we first show that the attention module can be actually replaced with or even surpassed by a convolution layer under some conditions in mathematics and experiments. Then, we adopt the convolutions into the Transformer, where the computation burden issue is alleviated and the fault classification accuracy is significantly improved. Furthermore, to increase the computation efficiency, a lightweight Transformer called LiteFormer, is developed by utilizing the depth-wise convolutional layer. Extensive experiments are carried out on four datasets: Case Western Reserve University dataset; Paderborn University dataset; and two gearbox datasets of drivetrain dynamic simulator. Through our experiments, our LiteFormer not only reduces the computation cost in model training, but also sets new state-of-the-art results, surpassing other counterparts in both fault classification accuracy and model robustness.

Due to privacy issues, the data island problem of machine fault diagnosis widely exists in real industry. Federated learning (FL) has received much attention as a decentralized machine-learning paradigm that learns a global model in the server by iteratively aggregating the local model parameters in a privacy-preserving scheme to address the data island problem. However, the fault data in industry is inevitably scarce and class imbalanced, so the fault data in different clients are heterogeneous from each other in FL. The existing federated machine fault diagnosis methods are mostly based on the federated averaging (FedAvg) algorithm, which ignores the data heterogeneity issue for machine fault diagnosis. To address this issue for machine fault diagnosis, we propose a new model alignment method (called FedAlign) via data-free knowledge distillation, where a compact generator is trained in the server in a data-free manner to estimate the data feature distribution in a global view without access to the local data. Then, the generator can produce the pseudo features to convey the distribution knowledge to both the server and the client sides. Our proposed FedAlign utilizes the pseudo features to align the global model and the local models to finally address the inherent heterogeneity of local data for fault diagnosis in FL. Experiments performed on fault diagnosis datasets of nonidentically and independently (non-iid) settings indicate that our proposed method facilitates FL for machine fault diagnosis with favorable effectiveness and achieves significant performance gains compared with state-of-the-art methods.

Domain adaptation enables generalized learning in new environments by transferring knowledge from label-rich source domains to label-scarce target domains. As a more realistic extension, partial domain adaptation (PDA) relaxes the assumption of fully shared label space, and instead deals with the scenario where the target label space is a subset of the source label space. In this paper, we propose a Reinforced Adaptation Network (RAN) to address the challenging PDA problem. Specifically, a deep reinforcement learning model is proposed to learn source data selection policies. Meanwhile, a domain adaptation model is presented to simultaneously determine rewards and learn domain-invariant feature representations. By combining reinforcement learning and domain adaptation techniques, the proposed network alleviates negative transfer by automatically filtering out less relevant source data and promotes positive transfer by minimizing the distribution discrepancy across domains. Experiments on three benchmark datasets demonstrate that RAN consistently outperforms seventeen existing state-of-the-art methods by a large margin.