A review in guided-ultrasonic-wave-based structural health monitoring: From fundamental theory to machine learning techniques.

Impact identification of composite cylinder based on improved deep metric learning model and weighted fusion Tikhonov regularized total least squares

Quantification, Localization, and Reconstruction of Impact Force on Interval Composite Structures

A novel unsupervised directed hierarchical graph network with clustering representation for intelligent fault diagnosis of machines

Federated transfer learning (FTL) can effectively address the data silos and domain shift that exist in data-driven rotating machinery fault diagnosis (RMFD). However, in FTL used for RMFD, the huge communication overhead, idle waiting between the source clients and the target client, and negative transfer caused by model aggregation are all pressing challenges. Therefore, a ring-based decentralized federated transfer learning (RDFTL) method for intelligent fault diagnosis is proposed. Firstly, a ring-based decentralized federated transfer learning framework is designed, which can be fully integrated with the bandwidth-optimal Ring-AllReduce algorithm, thereby greatly reducing the communication overhead. Secondly, an asynchronous domain adaptation strategy is proposed, which can effectively avoid idle waiting between the source clients and the target client in the collaborative model training, thereby improving the overall training efficiency of FTL. Thirdly, a multi-perspective distribution discrepancy aggregation (MPDDA) strategy is proposed to alleviate the negative transfer caused by model aggregation. The diagnosis performance of the local model of a source client on the target domain is evaluated from the three perspectives of statistical distance, domain adversarial, and stability, and these three evaluation metrics are jointly used to determine the aggregation weights, which can effectively improve the diagnosis performance of the global model. Finally, a series of experiments are carried out to verify the effectiveness of the proposed method. The results demonstrate that the proposed method can obtain a cross-domain fault diagnosis model with excellent performance in RMFD with data privacy at a fast training speed. 

Intelligent fault diagnosis via ring-based decentralized federated transfer learning

With the coming of the industrial big data era, data-driven fault diagnosis models emerge recently and show potential results in many studies. However, it is impractical to collect massive high-quality data in actual industrial applications, which strongly reduces the performance of fault diagnosis models. Therefore, multiple data owners are motivated to share their own data for training a brilliant model with the aggregated dataset. Notwithstanding, it is unrealistic to share data due to the potential conflict of interest. For addressing the issue of data island, a new federated framework namely FedCAE is proposed for fault diagnosis. In this framework, each client is equipped with a convolutional autoencoder (CAE) trained by its corresponding local data. Then, all CAEs are uploaded to a server and aggregated to a global CAE according to an adaptive weighted selection mechanism. Afterwards, the global CAE is downloaded to all clients for extracting low-level features from local datasets by its encoder and uploading these features to train a global fault diagnosis classifier in the server. At last, the classifier is downloaded to all clients for completing their own diagnosis tasks. Experiment results on two bearing datasets suggest FedCAE offers a promising solution on confidential decentralized learning. 

FedCAE: A New Federated Learning Framework for Edge-cloud Collaboration Based Machine Fault Diagnosis

Impact load is the load that machines frequently experienced in engineering applications. Its time-history reconstruction and localization are crucial for structural health monitoring and reliabili...

A feature learning-based method for impact load reconstruction and localization of the plate-rib assembled structure:

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FedCAE: A New Federated Learning Framework for Edge-cloud Collaboration Based Machine Fault Diagnosis

A feature learning-based method for impact load reconstruction and localization of the plate-rib assembled structure: