Privacy‐preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis

Xia, Liqiao; Zheng, Pai; Li, Jinjie; Tang, Wangchujun; Zhang, Xiangying

doi:10.1049/cim2.12057

Cited by 11 publications

(2 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A major contributions contains: (1) a generic hypergraph method for representing the interactions of difficult tools; and (2) a temporal enabled forecast method for learning the difficult data correlation and high order representation dependent upon the hypergraph. In [20], a vertical federated learning (FL) method, privacy-preserving boosting tree was established to collaborative fault analysis of industrial practitioners but maintained anonymity.…”

Section: Related Workmentioning

confidence: 99%

Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT

et al. 2023

View full text Add to dashboard Cite

Internet of Things (IoT) devices are becoming increasingly ubiquitous in daily life. They are utilized in various sectors like healthcare, manufacturing, and transportation. The main challenges related to IoT devices are the potential for faults to occur and their reliability. In classical IoT fault detection, the client device must upload raw information to the central server for the training model, which can reveal sensitive business information. Blockchain (BC) technology and a fault detection algorithm are applied to overcome these challenges. Generally, the fusion of BC technology and fault detection algorithms can give a secure and more reliable IoT ecosystem. Therefore, this study develops a new Blockchain Assisted Data Edge Verification with Consensus Algorithm for Machine Learning (BDEV-CAML) technique for IoT Fault Detection purposes. The presented BDEV-CAML technique integrates the benefits of blockchain, IoT, and ML models to enhance the IoT network's trustworthiness, efficacy, and security. In BC technology, IoT devices that possess a significant level of decentralized decision-making capability can attain a consensus on the efficiency of intrablock transactions. For fault detection in the IoT network, the deep directional gated recurrent unit (DBiGRU) model is used. Finally, the African vulture optimization algorithm (AVOA) technique is utilized for the optimal hyperparameter tuning of the DBiGRU model, which helps in improving the fault detection rate. A detailed set of experiments were carried out to highlight the enhanced performance of the BDEV-CAML algorithm. The comprehensive experimental results stated the improved performance of the BDEV-CAML technique over other existing models with maximum accuracy of 99.6%.INDEX TERMS Blockchain, Internet of Things, consensus algorithm, fault detection, deep learning, hyperparameter tuning.

show abstract

Section: Related Workmentioning

confidence: 99%

Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Early fault detection can identify the incipient fault symptoms of rolling bearings, which enables condition-based maintenance to be taken before severe failure occurring. Therefore, conducting EFD for rolling bearings can help prevent mechanical systems break down and reduce the economic loss caused by the failure of bearings [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Early fault detection for rolling bearings: A meta‐learning approach

Song,

Wu,

Shen

et al. 2024

IET Collab Intel Manufact

View full text Add to dashboard Cite

Early fault detection (EFD) of rolling bearings aims at detecting the early symptoms of faults by monitoring small deviations of health states. Accurate EFD enables predictive maintenance and contributes to the stability of mechanical systems. In recent years, machine learning based methods have shown impressive performance on EFD. Most of the current machine learning‐based methods assume the availability for a large amount of data. However, in practice, the authors may only have a very limited amount of training data, which makes it hard to learn a reliable machine learning model. To address this concern, in this work, the authors propose to tackle EFD via meta learning. Specifically, the authors first formulate EFD as a few‐shot learning problem and then propose to tackle this problem with a metric‐based meta learning method. Furthermore, ensemble learning is further leveraged to improve the detection robustness. For the proposed method, the distribution difference from the working conditions and the bearings are considered. The experimental results on two bearing datasets show that the proposed method can achieve better EFD performance, that is, detecting incipient faults earlier while bringing in lower false alarms, compared with several frequently used EFD methods.

show abstract

Enhanced Bearing Fault Diagnosis Through Trees Ensemble Method and Feature Importance Analysis

Alhams,

Abdelhadi,

Badri

et al. 2024

J. Vib. Eng. Technol.

View full text Add to dashboard Cite

Purpose This research introduces a groundbreaking method for bearing defect detection. It leverages ensemble machine learning (ML) models and conducts comprehensive feature importance analysis. The key innovation is the training and benchmarking of three tree ensemble models—Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—on an extensive experimental dataset (QU-DMBF) collected from bearing tests with seeded defects of varying sizes on the inner and outer raceways under different operating conditions. Method The dataset was meticulously prepared with categorical variable encoding and Min–Max data normalization to ensure consistent class distribution and model accuracy. Implementing the ML models involved a grid search method for hyperparameter tuning, focusing on reporting the models’ accuracy. The study also explores applying ensemble methods and using supervised and unsupervised learning algorithms for bearing fault detection. It underscores the value of feature importance analysis in understanding the contributions of specific inputs to the model’s performance. The research compares the ML models to traditional methods and discusses their potential for advanced fault diagnosis in bearing systems. Results and Conclusions The XGBoost model, trained on data from actual bearing tests, outperformed the others, achieving 92% accuracy in detecting bearing health and fault location. However, a deeper analysis of feature importance reveals that the models weigh certain experimental conditions differently—such as sensor location and motor speed. This research’s primary novelties and contributions are comparative evaluation, experimental validation, accuracy benchmarking, and interpretable feature importance analysis. This comprehensive methodology advances the bearing health monitoring field and has significant practical implications for condition-based maintenance, potentially leading to substantial cost savings and improved operational efficiency.

show abstract

Privacy‐preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis

Cited by 11 publications

References 44 publications

Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT

Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT

Early fault detection for rolling bearings: A meta‐learning approach

Enhanced Bearing Fault Diagnosis Through Trees Ensemble Method and Feature Importance Analysis

Contact Info

Product

Resources

About