Influence of Varnish on Bearing Performance and Vibration of Rotating Machinery

Zhang, Chu; Yang, Jiangang; Shi, Liwei; Gao, Qingshui; Yang, Yi

doi:10.1155/2017/9131275

Cited by 4 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gears are the most susceptible component to failures in gear reducers, with approximately 60% of failures being caused by gear pitting. Pitting failure not only leads to metal spalling on the gear surface but also causes tooth breakage, impact, noise, and reduces transmission efficiency [3]. Therefore, the detection of pitting failure in gears is of great significance for the development of gear fault diagnosis technology.…”

Section: Introductionmentioning

confidence: 99%

Development of a Vibration-based Experimental Platform for Diagnosing Gear Pitting Failure in Reducers

Li,

Wang,

Jia

et al. 2023

TETR

View full text Add to dashboard Cite

Gear pitting failure in the reducer has a significant impact on the normal operation and transmission efficiency of the equipment. In this paper, a vibration-based experimental platform for diagnosing gear pitting failure in reducers is developed. The platform aims to simulate gear pitting failure conditions and analyze vibration signal characteristics. Through the validation of experimental results, this paper demonstrates that analyzing vibration parameters such as clearance, impulse, peak value, and vibration intensity, combined with the multiple relationship between fault frequency in vibration spectrum and gear meshing frequency, can accurately determine the type and location of gear failure in the reducer. Furthermore, the experimental platform can simulate gear vibration under different speeds and loads, providing a foundation for accurate diagnosis and identification of gear failures. The developed experimental platform has significant practical value and can provide technical support for the prevention and maintenance of gear failures in reducers.

show abstract

Section: Introductionmentioning

confidence: 99%

Development of a Vibration-based Experimental Platform for Diagnosing Gear Pitting Failure in Reducers

Li,

Wang,

Jia

et al. 2023

TETR

View full text Add to dashboard Cite

show abstract

Condition-based maintenance using machine learning and role of interpretability: a review

Sharma

Mittal

Soni

2022

Int J Syst Assur Eng Manag

View full text Add to dashboard Cite

This article aims to review the literature on condition-based maintenance (CBM) by analyzing various terms, applications, and challenges. CBM is a maintenance technique that monitors the existing condition of an industrial asset to determine what maintenance needs to be performed. This article enlightens the readers with research in condition-based maintenance using machine learning and artificial intelligence techniques and related literature. A bibliometric analysis is performed on the data collected from the Scopus database. The foundation of a CBM is accurate anomaly detection and diagnosis. Several machine-learning approaches have produced excellent results for anomaly detection and diagnosis. However, due to the black-box nature of the machine learning models, the level of their interpretability is limited. This article provides insight into the existing maintenance strategies, anomaly detection techniques, interpretable models, and model-agnostic methods that are being applied. It has been found that significant work has been done towards condition based-maintenance using machine learning, but explainable artificial intelligence approaches got less attention in CBM. Based on the review, we contend that explainable artificial intelligence can provide unique insights and opportunities for addressing critical difficulties in maintenance leading to more informed decision-making. The analysis put forward encouraging research directions in this area.

show abstract

Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization

Carcillo

Borgne

Caelen

et al. 2018

Int J Data Sci Anal

View full text Add to dashboard Cite

Credit card fraud detection is a very challenging problem because of the specific nature of transaction data and the labeling process. The transaction data is peculiar because they are obtained in a streaming fashion, they are strongly imbalanced and prone to non-stationarity. The labeling is the outcome of an active learning process, as every day human investigators contact only a small number of cardholders (associated to the riskiest transactions) and obtain the class (fraud or genuine) of the related transactions. An adequate selection of the set of cardholders is therefore crucial for an efficient fraud detection process. In this paper, we present a number of active learning strategies and we investigate their fraud detection accuracies. We compare different criteria (supervised, semi-supervised and unsupervised) to query unlabeled transactions. Finally, we highlight the existence of an exploitation/exploration trade-off for active learning in the context of fraud detection, which has so far been overlooked in the literature. 1

show abstract

Influence of Varnish on Bearing Performance and Vibration of Rotating Machinery

Cited by 4 publications

References 19 publications

Development of a Vibration-based Experimental Platform for Diagnosing Gear Pitting Failure in Reducers

Development of a Vibration-based Experimental Platform for Diagnosing Gear Pitting Failure in Reducers

Condition-based maintenance using machine learning and role of interpretability: a review

Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization

Contact Info

Product

Resources

About