Large group decision-making (LGDM) involves multiple decision-makers (DMs) and criteria, frequently resulting in conflicts and inconsistencies. This study proposes a novel conflict resolution model based on dynamic trust relationships to effectively identify and address potential disputes in LGDM. First, hesitant fuzzy 2-tuple linguistic sets (HF2TLSs) are utilized to accurately capture DMs’ preferences in uncertain environments. Then, a Markov trust state transition model is developed to capture the dynamic evolution of trust relationships. Next, an enhanced PageRank algorithm, built on trust networks, is employed to determine the weights of DMs and subgroups, thereby improving decision quality. Additionally, multidimensional conflict detection indicators are introduced to quantify cognitive and interest conflicts among subgroups. Finally, an adaptive conflict resolution mechanism is presented to balance heterogeneous interests and achieve agreement. An illustrative example validates the model, with comparative analyses demonstrating its rationality and superiority.

Existing self-supervised multivariate time series anomaly detection methods struggle with interference among variables during reconstruction. They also tend to miss capturing critical anomaly information, resulting in unsatisfactory performance, especially in scenarios with strong mechanistic contexts. To this end, we propose a targeted anomaly detection algorithm called inference stacked recurrent autoencoder (ISRAE). Its key contribution lies in the design of a specific inference kernel, derived from specialist knowledge, which captures the strong mechanistic relationships among variables. This kernel is then fused with the multidimensional anomalies predicted by the SRAE, which mitigates interference among variables through the stacking technique. Furthermore, a novel differential constraint is introduced into the loss function, which not only highlights anomaly reconstruction errors, but also smooths the reconstructions, enhancing overall detection performance. Comprehensive comparison experiments and ablation studies show that ISRAE achieves superior anomaly detection performance under strong mechanistic contexts and highlight the importance of each key module in ISRAE.

In intelligent fault diagnosis, fault severity classification with class imbalance remains a tremendous challenge. To simultaneously consider relative natural order in fault severity and class imbalance, we propose the adaptive ordinal sample-weighted meta residual network (AOSW-MRN). The AOSW-MRN model uses a weighting network and a meta-model cloned from the residual network to create a nonlinear weighted mapping. It adaptively learns sample weights from a balanced and clean-label meta-dataset, training a model robust to imbalance and ordinal relationships. We validate its effectiveness in two real-world case studies with different imbalance rates. Experimental results demonstrate that our model outperforms several existing Start-of-the-Art models regardless of classification and regression performance since it considers the ordinality of samples in the feature space.

Rapid developments of offshore wind industry offer a strong demand opportunity for offshore wind turbine remote diagnosis. As offshore wind turbines are often located in harsh and communication-constrained environments, the collection and transmission of data is severely restricted, which poses a serious challenge to the conventional centralized diagnostic paradigm that relies on data aggregation. To address this challenge, we propose a novel event-triggered federated learning framework for decentralized fault diagnosis of offshore wind turbines. Specifically, federated learning is first employed to learn decentralized local knowledge from geographically distributed offshore wind turbines, so that the communication objects are transformed from massive raw data into learned parameters, thereby relieving the communication burden. Then, we design an event-triggered communication mechanism and incorporate it into federated learning, the core of which is to modify the communication requirement from uploading all trained parameters periodically to communicating only when necessary. The proposed framework is verified by a real-world offshore wind turbine dataset from six large wind farms in China. An ablation study shows that the proposed framework can maintain high diagnostic performance while reducing communication costs. A comprehensive comparison based on three benchmark models demonstrates that the proposed framework can reduce the communication burden by up to 63% while obtaining better diagnostic performance.Note to Practitioners—This study was motivated by the problem of collaborative diagnosis of distributed offshore wind turbines under the constraints of data privacy and communication overhead. The method employs a federated learning-based fault diagnosis framework, which permits to obtain global fault diagnosis knowledge without aggregating raw data scattered in each end device, thus avoids the risk of data leakage. Moreover, a strategy integrating parameter variation and accuracy gain is designed to avoid communication redundancy for collaborative training. The practicability and superiority of our proposed framework is demonstrated using extensive experiments against actual industrial data collected from six offshore wind farms.

Multivariate time-series (MTS) collected from multiple sensors on industrial pumps often exhibit concept drift and noise contamination due to variable working conditions and complex environments. To detect anomalies in such MTS, we propose a novel model called spectral residual with self-attention variational autoencoder (SR-SAVAE). Specifically, the spectral residual operation is used to mitigate concept drift, while the variational inference combined with a total variation regularization is used to address the issue of noise contamination. Experimental results on three public datasets indicate that the SR-SAVAE model achieves good anomaly detection results for general MTS. More importantly, compared to other state-of-the-art models on a private dataset about pumps, the results illustrate the superiority of the SR-SAVAE model in anomaly detection for MTS with concept drift and noise contamination. Finally, ablation studies on the SR-SAVAE model detail the efficacy of each component.

In real industrial processes, machines usually run under variable working conditions, which impose challenges for anomaly detection. To complete anomaly detection for machines under variable working conditions, we develop a reconstruction-based autoencoder called clustering-based contrastive learning autoencoder (CBCL-AE). It integrates clustering-based contrastive learning (CBCL) to perform clustering in the feature space and enhance the differentiation of features from different working conditions, thereby achieving adaptive working condition recognition. Considering the crucial role of the clustering of CBCL, we theoretically and experimentally demonstrate its convergence property during the training process, which directly determines the effectiveness of CBCL-AE. CBCL-AE's superiority has been validated on three public datasets and two private datasets collected from an actual industrial process. These validations highlight its superiority over five state-of-the-art models in unsupervised anomaly detection.

Non-rechargeable batteries remain as the main source of energy for small systems, owing to their unique advantages in energy density, safety, reliability and sustainability. Accurate prediction of the remaining useful life of the battery is not only beneficial to maintenance and production safety, but also can be regarded as a starting point for possible secondary life applications. In this study, an interactive attention sequence-to-sequence network is proposed for the remaining useful life prediction of the non-rechargeable batteries. The proposed approach can effectively extract the degenerate information of each variable-length sequence and dynamically weight the sequence features of different dimensions. For illustration, a case of primary battery dataset collected from the power supply system of 139 vibration sensors is utilized. The extensive experiments verify the effectiveness of the proposed approach.

Privacy protection as a major concern of the industrial big data enabling entities makes the massive safety-critical operation data of a wind turbine unable to exert its great value because of the threat of privacy leakage. How to improve the diagnostic accuracy of decentralized machines without data transfer remains an open issue; especially these machines are almost accompanied by skewed class distribution in the real industries. In this study, a class-imbalanced privacy-preserving federated learning framework for the fault diagnosis of a decentralized wind turbine is proposed. Specifically, a biometric authentication technique is first employed to ensure that only legitimate entities can access private data and defend against malicious attacks. Then, the federated learning with two privacy-enhancing techniques enables high potential privacy and security in low-trust systems. Then, a solely gradient-based self-monitor scheme is integrated to acknowledge the global imbalance information for class-imbalanced fault diagnosis. We leverage a real-world industrial wind turbine dataset to verify the effectiveness of the proposed framework. By comparison with five state-of-the-art approaches and two nonparametric tests, the superiority of the proposed framework in imbalanced classification is ascertained. An ablation study indicates that the proposed framework can maintain high diagnostic performance while enhancing privacy protection.