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Machines
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Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction (2nd Edition)
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Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction (2nd Edition)

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electrical Machines and Drives".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2174

Special Issue Editors

Prof. Dr. Hongrui Cao

E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: dynamnic modeling and fault diagnosis of machinery
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jianping Xuan

E-Mail Website
Guest Editor
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: condition monitoring and fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yongqiang Liu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
Interests: condition monitoring and fault diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the previous Special Issue “Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction” (https://www.mdpi.com/journal/machines/special_issues/Prediction_machines), we are pleased to announce the next in the series, entitled “Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction (2nd Edition)”.

A variety of industrial and household appliances are equipped with rotating systems. These are used in electric motors, pumps, rotary engines and compressors, turbines, automobiles, railways, the steel industry, power plants, material handling devices, jet engines and many more. Bearings constitute one of the most critical components in rotating machinery. In today’s competitive environment, due to an increase in demand for running accuracy and nonlinearity involved in such systems, condition-based and predictive maintenance of bearings is gaining more popularity.

The objective of this Special Issue is to discover the most recent and significant developments in bearing modeling, fault diagnosis, and remaining useful life (RUL) prediction. This Special Issue encourages and welcomes original research articles that provide a significant contribution in the form of numerical, theoretical or experimental analysis. Review articles related to these application areas are also invited.

Potential topics include, but are not limited to, the following:

  • Modelling and simulation;
  • Failure mechanisms analysis;
  • Intelligent sensor and flexible sensor;
  • Wireless sensors and sensor networks;
  • Signal processing theory and methods;
  • Data acquisition and measurement methods;
  • Bearing condition monitoring;
  • Machine learning and intelligent fault diagnosis;
  • Bearing RUL prediction;
  • Big data analytics in bearings;
  • Intelligent bearings.

Prof. Dr. Hongrui Cao
Prof. Dr. Jianping Xuan
Prof. Dr. Yongqiang Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bearing modeling
  • failure analysis
  • sensing
  • signal processing
  • condition monitoring
  • fault diagnosis
  • RUL prediction
  • big data analytics

Related Special Issue

Published Papers (2 papers)

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Research

16 pages, 6283 KiB  
Article
Study on Condition Monitoring of Pitch Bearings Based on Stress Measurement
by Zian Wu, Wenxian Yang, Xiaoping Song and Kexiang Wei
Machines 2024, 12(3), 154; https://doi.org/10.3390/machines12030154 - 23 Feb 2024
Viewed by 1005
Abstract
Pitch bearings in wind turbines are crucial components that enable safe blade pitching, optimize electrical power output, and ensure turbine protection. Traditional vibration analysis-based methods used for high-speed bearings are not applicable to monitoring pitch bearings, due to its slow non-integer cycle rotation. [...] Read more.
Pitch bearings in wind turbines are crucial components that enable safe blade pitching, optimize electrical power output, and ensure turbine protection. Traditional vibration analysis-based methods used for high-speed bearings are not applicable to monitoring pitch bearings, due to its slow non-integer cycle rotation. To address this issue, a stress-based pitch bearing monitoring method is proposed in this paper. First, finite element analysis is conducted to establish the relationship between the maximum surface stress on the outer race of the pitch bearing and the presence of cracks. This relationship allows the identification of cracks on the outer race and an assessment of their severity based on the value of the maximum surface stress. Second, the outer race of the pitch bearing is divided into several segments, and a singularity detection technique is employed to locate the position of cracks on the outer race based on the stresses measured from the segments. To verify the proposed method, a wind turbine pitch bearing test rig was developed in a laboratory. Experimental results have shown that the proposed method can effectively and accurately identify and locate cracks on the outer race of the bearing, thereby demonstrating its great potential as a reliable approach for monitoring the condition of wind turbine pitch bearings. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction (2nd Edition))
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17 pages, 5192 KiB  
Article
Predictive Analytics-Based Methodology Supported by Wireless Monitoring for the Prognosis of Roller-Bearing Failure
by Ernesto Primera, Daniel Fernández, Andrés Cacereño and Alvaro Rodríguez-Prieto
Machines 2024, 12(1), 69; https://doi.org/10.3390/machines12010069 - 17 Jan 2024
Viewed by 908
Abstract
Roller mills are commonly used in the production of mining derivatives, since one of their purposes is to reduce raw materials to very small sizes and to combine them. This research evaluates the mechanical condition of a mill containing four rollers, focusing on [...] Read more.
Roller mills are commonly used in the production of mining derivatives, since one of their purposes is to reduce raw materials to very small sizes and to combine them. This research evaluates the mechanical condition of a mill containing four rollers, focusing on the largest cylindrical roller bearings as the main component that causes equipment failure. The objective of this work is to make a prognosis of when the overall vibrations would reach the maximum level allowed (2.5 IPS pk), thus enabling planned replacements, and achieving the maximum possible useful life in operation, without incurring unscheduled corrective maintenance and unexpected plant shutdown. Wireless sensors were used to capture vibration data and the ARIMA (Auto-Regressive Integrated Moving Average) and Holt–Winters methods were applied to forecast vibration behavior in the short term. Finally, the results demonstrate that the Holt–Winters model outperforms the ARIMA model in precision, allowing a 3-month prognosis without exceeding the established vibration limit. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction (2nd Edition))
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