Mechanical Equipment Reliability: Diagnosis, Prognostic and Maintenance

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

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 7398

Special Issue Editors

School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
Interests: nonlinear dynamics and vibration control; intelligent modeling; intelligent fault diagnosis; digital twins
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Guest Editor
School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, Jilin, China
Interests: machine tools; industrial robot; reliability design

Special Issue Information

Dear Colleagues,

Mechanical equipment is a vast term that encompasses different areas, such as manufacturing, measurement, and logistics, and it is an essential asset for industry. However, issues around improving accuracy and efficiency, as well as reliability, must be solved to improve mechanical equipment performance. Operational safety, maintenance cost effectiveness, and availability have a direct impact on the competitiveness of machinery, and there is therefore a pressing need to continuously develop and improve reliability technologies for mechanical equipment, which is important for both equipment manufacturers and users.

Mechanical equipment performance deteriorates with use and age; thus, knowing when, where, and how equipment will deteriorate to the point of failure and how to properly but cheaply maintain it to ensure it stays in a good condition are significant problems. Moreover, mechanical equipment generally involves a complex system with interlinked mechanic, electronic, and hydraulic, traditional reliability technologies, and thus, the rapid development growth of mechanical equipment may not be possible due to multiple degradation types, complex failure mechanisms, and long-life properties. With the utilization of increased automation and sensor techniques, industries are attempting to monitor, analyze, and predict system degradation to optimize system maintenance policy, and to further control degradation tendencies, identify and predict failures, mitigate risks, and minimize costs. This is critical for the industry to solve the life-cycle health management issue of mechanical equipment, which is also a challenge in the field of reliability engineering. Therefore, more efficient and effective theoretical and experimental methodologies for mechanical equipment are expected to be developed to provide early detection and isolation of the precursor and/or incipient failure of components or subsystems, to have the means to monitor and predict the progression of failure, and to aid in creating or autonomously triggering a maintenance schedule and health management decisions or actions.

This Special Issue will be devoted to state-of-the-art research on reliability technologies and applications for mechanical equipment and their subsystems and components, such as machine tools, measuring instrument, industrial robots, articulated arms, motorized spindles, turrets, automatic tool changers (ATCs), etc. We seek submissions with an original perspective and advanced thinking on the theme addressed. Research on theories, simulations, experiments, and engineering applications is welcome.

Possible topics include but are not limited to the following:

  • Reliability design
  • Reliability testing
  • Reliability modeling
  • Health management
  • Failure diagnosis
  • Failure analysis
  • Service performance of material
  • Micromechanics of materials
  • Preventive maintenance
  • Surface modification
  • Lightweight design
  • Security design
  • Human reliability
  • Dynamical stability

Dr. Lei Hou
Dr. Chuanhai Chen
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

  • Reliability design 
  • Reliability testing 
  • Reliability modeling 
  • Health management 
  • Failure diagnosis 
  • Failure analysis 
  • Service performance of material 
  • Micromechanics of materials 
  • Preventive maintenance 
  • Surface modification 
  • Lightweight design 
  • Security design 
  • Human reliability 
  • Dynamical stability

Published Papers (3 papers)

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Research

24 pages, 10299 KiB  
Article
General Cutting Load Model for Workload Simulation in Spindle Reliability Test
by Lingda Kong, Weizheng Chen, Wei Luo, Chuanhai Chen and Zhaojun Yang
Machines 2022, 10(2), 144; https://doi.org/10.3390/machines10020144 - 16 Feb 2022
Cited by 5 | Viewed by 2318
Abstract
As the key functional component of the machine tool, the reliability test of the spindle is necessary to verify the reliability of the machine tool. In the reliability test, the cutting load model is the guideline of workload simulation and is the prerequisite [...] Read more.
As the key functional component of the machine tool, the reliability test of the spindle is necessary to verify the reliability of the machine tool. In the reliability test, the cutting load model is the guideline of workload simulation and is the prerequisite to ensure the accuracy and effectiveness of the long-term experiment. However, the existing load models usually aim at the specific cutting force at the tool-tip, thereby ignoring the versatility, maneuverability, and accuracy of the load model when applied in the spindle reliability test. In this study, a general cutting load model for the machine tool spindle is established in a form of radial-axial-torque decomposition, and the radial force is simplified as non-rotating status for the maneuverability of conducting a load simulation. The difference between rotating and non-rotating radial force on the reliability calculation is also discussed and corrected using bearing fatigue analysis. A spindle reliability test platform with radial force, axial force, and torque simulation is developed according to the cutting load model, while the loading spectrum is compiled for conducting the spindle reliability test. This research is of great engineering value for the designing of the spindle reliability test. Full article
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18 pages, 6105 KiB  
Article
Prediction of Abrasive Belt Wear Based on BP Neural Network
by Yuanxun Cao, Ji Zhao, Xingtian Qu, Xin Wang and Bowen Liu
Machines 2021, 9(12), 314; https://doi.org/10.3390/machines9120314 - 26 Nov 2021
Cited by 4 | Viewed by 1764
Abstract
Abrasive belt grinding is the key technology in high-end precision manufacturing field, but the working condition of abrasive particles on the surface of the belt will directly affect the quality and efficiency during processing. Aiming at the problem of the inability to monitor [...] Read more.
Abrasive belt grinding is the key technology in high-end precision manufacturing field, but the working condition of abrasive particles on the surface of the belt will directly affect the quality and efficiency during processing. Aiming at the problem of the inability to monitor the wearing status of abrasive belt in real-time during the grinding process, and the challenge of time-consuming control while shutdown for detection, this paper proposes a method for predicating the wear of abrasive belt while the grinding process based on back-propagation (BP) neural network. First, experiments are carried out based on ultra-depth-of-field detection technology, and different parameter combinations are used to measure the degree of abrasive belt wear. Then the effects of different grinding speeds, different contact pressures, and different work piece materials on the abrasive belt wear rate are obtained. It can be concluded that the abrasive belt wear rate gradually increases as the grinding speed of the abrasive belt increases. With the increase of steel grade, the hardness of the steel structure increases, which intensifies the abrasive belt wear. As the contact pressure increases, the pressure on a single abrasive particle increases, which ultimately leads to increased wear. With the increase of contact pressure, the increase of the wear rate of materials with higher hardness is greater. By utilizing the artificial intelligence BP neural network method, 18 sets of experiment data are used for training BP neural network while 9 sets of data are used for verification, and the nonlinear mapping relationship between various process parameter combinations such as grinding speed, contact pressure, workpiece material, and wear rate is established to predict the wear degree of abrasive belt. Finally, the results of verification by examples show that the method proposed in this paper can fulfill the purpose of quickly and accurately predicting the degree of abrasive belt wear, which can be used for guiding the manufacturing processing, and greatly improving the processing efficiency. Full article
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21 pages, 5683 KiB  
Article
Vibration Characteristics of a Dual-Rotor System with Non-Concentricity
by Shengliang Hou, Lei Hou, Shiwei Dun, Yufeng Cai, Yang Yang and Yushu Chen
Machines 2021, 9(11), 251; https://doi.org/10.3390/machines9110251 - 26 Oct 2021
Cited by 6 | Viewed by 1819
Abstract
A finite element model of an aero-engine dual-rotor system with intermediate bearing supported by six bearings is set up. Three modes of non-concentricity caused by the assembly process are defined, namely parallel non-concentricity, front deflection angle non-concentricity and rear deflection angle non-concentricity. The [...] Read more.
A finite element model of an aero-engine dual-rotor system with intermediate bearing supported by six bearings is set up. Three modes of non-concentricity caused by the assembly process are defined, namely parallel non-concentricity, front deflection angle non-concentricity and rear deflection angle non-concentricity. The influence of the non-concentricity on the vibration characteristics of the dual-rotor system is investigated in detail. The results show that the parallel non-concentricity and the front deflection angle non-concentricity have a significant influence on the bending vibration modals of the high-pressure rotor and the low-pressure rotor, but have little influence on the local vibration modals of the rotors. With the increase in the magnitude of the non-concentricity, the natural frequencies of the bending modals decrease continuously, and the mode shapes of bending modals and that of local modals may be interchanged, leading to the emergence of bending modals in advance. Therefore, the key parameters to be controlled in the assembly process are the parallel non-concentricity and the front deflection angle non-concentricity. In order to prevent the bending modal of the dual-rotor system from appearing in advance, it is necessary to control the parallel non-concentricity within 2 mm and the front deflection angle non-concentricity amount within 0.18°. Full article
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