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Machines
Special Issues
Advances in Machinery Condition Monitoring, Diagnosis and Prognosis
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Advances in Machinery Condition Monitoring, Diagnosis and Prognosis

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2577

Special Issue Editors

Dr. Kai Zhou

E-Mail Website
Guest Editor
Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, Houghton, MI 49931,USA
Interests: structural health monitoring; machinery fault diagnosis and prognosis; smart structures, materials and sensing; advanced manufacturing
Dr. Zequn Wang

E-Mail Website
Guest Editor
Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, Houghton, MI 49931-1295, USA
Interests: reliability-based analysis and design; failure prognostics and diagnostics; uncertainty quantification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machinery plays a vital role in many industries. The main machinery components include the shaft, bearing, gears, blades, motors and engines, and so on.  Since these components are subject to harsh operation conditions, they are prone to failure, which causes the machinery breakdown and, consequently, tremendous economic loss. Therefore, conducting timely and accurate prognosis and diagnosis of machinery is critically important to maintaining the machinery integrity and operational reliability.

This Special Issue of Machines solicitates the novel research studies that can make significant contributions to advancing state-of-the-art machinery monitoring, diagnosis and prognosis techniques, leading to enhanced diagnosis and prognosis accuracy, robustness and reliability in the practical applications. The research topics of interest include, but are not limited to:

  • Enhanced signal processing and feature extraction for retrieving the pivot fault-related signals.
  • Enhanced multi-physics system-level modeling and simulation of machinery to elucidate the consequence of component fault occurrence.
  • Novel machine-learning-enabled intelligent fault diagnosis and prognosis with enhanced performance even under the limited quality and size of available dataset.
  • Novel integrated/unified numerical framework/platform that leverages the synergic advancement of physical modeling, signal processing, machine learning and optimization methods.
  • The demonstrations of practical and challenging fault diagnosis and prognosis applications.
  • Reliability-based analysis and design of machinery considering the multifarious uncertainties.

Dr. Kai Zhou
Dr. Zequn Wang
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

  • machinery fault diagnosis and prognosis
  • signal processing and feature extraction
  • machine learning
  • deep learning
  • system-level machinery modeling
  • numerical optimization
  • reliability-based analysis and design

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Published Papers (2 papers)

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Research

13 pages, 722 KiB  
Article
Efficiency Analysis of Die Attach Machines Using Overall Equipment Effectiveness Metrics and Failure Mode and Effects Analysis with an Ishikawa Diagram
by Rex Revian A. Guste, Klint Allen A. Mariñas and Ardvin Kester S. Ong
Machines 2024, 12(7), 467; https://doi.org/10.3390/machines12070467 - 11 Jul 2024
Viewed by 666
Abstract
The semiconductor manufacturing sector has contributed to the advancement of technical development in the sphere of industrial applications, but one crucial factor that cannot be overlooked is the evaluation of a machine’s state. Despite the presence of advanced equipment, data on their performances [...] Read more.
The semiconductor manufacturing sector has contributed to the advancement of technical development in the sphere of industrial applications, but one crucial factor that cannot be overlooked is the evaluation of a machine’s state. Despite the presence of advanced equipment, data on their performances are not properly reviewed, resulting in a variety of concerns such as high rejection rates, lower production output, manufacturing overhead cost issues, and customer complaints. This study’s goal is to evaluate the performance of die attach machines made by a prominent subcontractor semiconductor manufacturing business in the Philippines; our findings will provide other organizations with important insights into the appropriate diagnosis of productivity difficulties via productivity metrics analyses. The study focuses on a specific type of die attach machine, with machine 10 showing to be the most troublesome, with an overall equipment effectiveness (OEE) rating of 43.57%. The Failure Mode and Effects Analysis (FMEA) identified that the primary reasons for the issue were idling, small stoppages, and breakdown loss resulting from loosened screws in the work holder. The risk priority number (RPN) was calculated to be 392, with a severity level of 7, an occurrence level of 7, and a detection level of 8. The findings provide new insight into the methods that should be included in the production process to boost efficiency and better suit the expectations of customers in a highly competitive market. Full article
(This article belongs to the Special Issue Advances in Machinery Condition Monitoring, Diagnosis and Prognosis)
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20 pages, 5590 KiB  
Article
A Multi-Task-Based Deep Multi-Scale Information Fusion Method for Intelligent Diagnosis of Bearing Faults
by Ruihao Xin, Xin Feng, Tiantian Wang, Fengbo Miao and Cuinan Yu
Machines 2023, 11(2), 198; https://doi.org/10.3390/machines11020198 - 1 Feb 2023
Cited by 3 | Viewed by 1484
Abstract
The use of deep learning for fault diagnosis is already a common approach. However, integrating discriminative information of fault types and scales into deep learning models for rich multitask fault feature diagnosis still deserves attention. In this study, a deep multitask-based multiscale feature [...] Read more.
The use of deep learning for fault diagnosis is already a common approach. However, integrating discriminative information of fault types and scales into deep learning models for rich multitask fault feature diagnosis still deserves attention. In this study, a deep multitask-based multiscale feature fusion network model (MEAT) is proposed to address the limitations and poor adaptability of traditional convolutional neural network models for complex jobs. The model performed multidimensional feature extraction through convolution at different scales to obtain different levels of fault information, used a hierarchical attention mechanism to weight the fusion of features to achieve an accuracy of 99.95% for the total task of fault six classification, and considered two subtasks in fault classification to discriminate fault size and fault type through multi-task mapping decomposition. Of these, the highest accuracy of fault size classification reached 100%. In addition, Precision, ReCall, and Sacore F1 all reached the index of 1, which achieved the accurate diagnosis of bearing faults. Full article
(This article belongs to the Special Issue Advances in Machinery Condition Monitoring, Diagnosis and Prognosis)
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