Empowered by the huge amounts of sensor data in Industrial Internet of Things (IIOT), deep learning models have made remarkable achievements in the field of rotating machinery fault diagnosis. To improve the diagnosis performance under unknown working conditions, domain generalization technologies have been extensively studied. However, the existing methods predominantly gather the sensor data from multiple source domains together for model training, which poses a threat to data privacy in the IIOT. To address this problem, this paper proposes a novel gradient alignment federated domain generalization (GAFedDG) framework for rotating machinery fault diagnosis. In the proposed GAFedDG, an intra-domain gradient aligning mechanism is designed to minimize the gradient discrepancy between the current classifier on raw signals and augmented signals, effectively preventing the local model from overfitting the domain-specific fault knowledge. In addition, to bridge the domain shifts across multiple scattered source domains, an inter-domain gradient aligning mechanism is implemented to minimize the gradient discrepancy between the current classifier and other domain classifiers. By combining the two mechanisms above, a domain-agnostic model that can generalize well on unseen working conditions is established. Extensive experimental results on two self-built test rigs show that the GAFedDG possesses superior generalization capability in privacy-preserving scenarios.

To assess the health condition of industrial robots roundly and make hierarchical maintenance decisions, a multimodel fusion health assessment method is proposed for multistate industrial robots. Herein, many symptom parameters (SPs) are used to reflect the operation state of the industrial robot from aspects of vibration, temperature, and torque. Then, fuzzy deep residual shrinkage network is proposed to establish the SP-based status membership function as a single assessment model. The probabilities of robot operation states are determined and formulated as the hesitation fuzzy number (HFN). These HFNs from multiple assessment models are integrated into a collective hesitation fuzzy assessment matrix. Thus, the best worst method is adopted to estimate the confidence of each assessment model, and TOPSIS is used to judge the impact of different operation states on the industrial robot's behavior. Finally, a novel health index is defined for industrial robot, and robot health degree is identified by versatile cluster for hierarchical maintenance decisions. A self-built industrial robot test stand is adopted to validate the effectiveness of the proposed method, and sensitivity and comparison analysis results demonstrated that our method has advantages in terms of the situation adaptability and performance stability.

This paper proposes a novel hybrid scheme through read-first-LSTM (RLSTM) encoder-decoder and broad learning system (BLS) for bearings degradation monitoring and remaining useful life (RUL) estimation, which aims to describe the nonlinear characteristics of the degradation process. Firstly, the raw signals are processed premier by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a novel dimensionality reduction method composed of t-distribution stochastic neighbor embedding (t-SNE) and density-based spatial clustering of application with noise algorithm (DBSCAN). Then, the health indicator is constructed with the Hilbert-Huang transform (HHT) corresponding to the bearings’ natural fault frequency, which can be employed as the hybrid scheme training label. Linear rectification technology (LRT) and exponentially weighted moving average (EWMA) control chart are adapted to define the exact process of the degradation. Secondly, a novel RLSTM is proposed. And simultaneously, an encoder-decoder model, where RLSTM is utilized as an encoder, and LSTM is adopted as a decoder, is designed for degradation monitoring. Finally, a broad learning system (BLS), which differs from deep learning with a deeper structure, is established in a flat network to estimate the RUL of bearings. Compared with the state-of-the-art techniques, the better efficacy of the proposed hybrid scheme is illustrated using the PRONOSTIA platform dataset.

Remaining useful life (RUL) prediction of aircraft engine (AE) is of great importance to improve its reliability and availability, and reduce its maintenance costs. This article proposes a novel deep bidirectional recurrent neural networks (DBRNNs) ensemble method for the RUL prediction of the AEs. In this method, several kinds of DBRNNs with different neuron structures are built to extract hidden features from sensory data. A new customized loss function is designed to evaluate the performance of the DBRNNs, and a series of the RUL values is obtained. Then, these RUL values are reencapsulated into a predicted RUL domain. By updating the weights of elements in the domain, multiple regression decision tree (RDT) models are trained iteratively. These models integrate the predicted results of different DBRNNs to realize the final RUL prognostics with high accuracy. The proposed method is validated by using C-MAPSS datasets from NASA. The experimental results show that the proposed method has achieved more superior performance compared with other existing methods.

Deep learning (DL) method are extensively used for bearing fault diagnosis (BFD). Due to severe data distribution difference under variable working conditions, they have unsatisfactory performance of the BFD. Although the existing transfer learning (TL) methods might improve the diagnostic performance in different data distributions, fault data from these different domains in training have to be obtained. When a given bearing operates in a new working condition and fault data are not available, the TL methods might be invalid, and the BFD would be postponed. To solve the above problem, a novel zero-shot learning method named deep attention relation network (DARN) is proposed for the BFD under multiple unknown domains. The built DARN only trained by the data from a known domain might be used to diagnose fault types from unknown, but related domains without prior data input. In this method, a feature extraction module is constructed to generate representations of input samples, and a relation module is designed to calculate the relation score between the sample pairs to determine their categories. Meanwhile, a parallel attention mechanism is introduced into the DARN so as to enhance the representative ability of the built model. The results of experimental study indicate that the proposed method can make use of fault knowledge learnt from the single known domain for the BFD in the several unknown domains. The proposed DARN significantly outperforms the existing popular TL methods in diagnostic performance.

Machine health monitoring is of great importance in industrial informatics field. Recently, deep learning methods applied to machine health monitoring have been proven effective. However, the existing methods face enormous difficulties in extracting heterogeneous features indicating the variation until failure and revealing the inherent high-dimensional features of massive signals, which affect the accuracy and efficiency of machine health monitoring. In this paper, a novel data-driven machine health monitoring method is proposed using adaptive kernel spectral clustering (AKSC) and deep long short-term memory recurrent neural networks (LSTM-RNN). This method include three steps: First, features in the time domain, frequency domain, and time-frequency domain are, respectively, extracted from massive measured signals. And, an Euclidean distance based algorithm is designed to select degradation features. Second, the AKSC algorithm is introduced to adaptively identify machine anomaly behaviors from multiple degradation features. Third, a new deep learning model (LSTM-RNN) is constructed to update and predict the failure time of the machine. The effectiveness of the proposed method is validated using a set of test-to-failure experimental data. The results show that the performance of the proposed method is competitive with other existing methods.