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Processes
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Predictive Maintenance for Manufacturing System
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Predictive Maintenance for Manufacturing System

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 5001

Special Issue Editors

Dr. Piotr Gierlak

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Guest Editor
Department of Applied Mechanics and Robotics, Rzeszów University of Technology, 35-959 Rzeszów, Poland
Interests: mechanical systems modelling; non-linear robot control; adaptive and robust control; hybrid position/force control; fuzzy logic; artificial neural networks; underactuated systems; stability of control systems; vibration measurement; vibration analysis; vibrodiagnostics
Special Issues, Collections and Topics in MDPI journals
Dr. Satish Kumar

E-Mail Website
Guest Editor
Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, Maharashtra, India
Interests: smart manufacturing; cryotreatment; machining; Industry 4.0; digital twin; predictive maintenance; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Arunkumar Bongale

E-Mail Website
Guest Editor
Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India
Interests: smart manufacturing; mechanical properties; mechanical behavior of materials; mechanical testing; materials processing; advanced materials; statastical analysis

Special Issue Information

Dear Colleagues,

Industry 4.0 is an emerging area in smart manufacturing, which aims to generate more sustainable and efficient industries. Many sectors, such as aerospace, health, and automobiles, require proper monitoring during the manufacturing process to produce high-quality and reliable products. Industry 4.0 adds an intelligent perspective to traditional manufacturing.

The smart manufacturing approach provides valuable insights into manufacturing processes and equipment. In manufacturing industries, unplanned downtime occurs due to the degradation of the system, causing component or equipment failure. Predicting the useful life of components is essential to avoid the unplanned downtime of machines.

Accurately predicting the Remaining Useful Life (RUL) of equipment is challenging due to the varying operating conditions across different industries. Therefore, predictive maintenance has gained the attention of many researchers who aim to improve the prediction accuracy of models estimating the RUL of equipment.

This Special Issue will focus on advancements in manufacturing related to monitoring and prediction in order to enhance the useful life of industrial machines and equipment, improving the quality of the end product. 

The scope of this Special Issue includes, but is not limited to, the following areas:

  • Digital manufacturing;
  • Advanced sensing, sensor fusion, and analysis;
  • Multi-sensor data fusion;
  • Data-driven, model-based, or hybrid methods for industrial maintenance;
  • Machine vision;
  • Remaining Useful Life estimation;
  • Inferring quality and fault localization;
  • Predictive and risk-based maintenance practices;
  • Vibration Damping during machining and other operations;
  • Additive manufacturing;
  • Machine Learning and Deep Learning in Manufacturing;
  •  Digital twin

Prof. Dr. Piotr Gierlak
Dr. Satish Kumar
Prof. Dr. Arunkumar Bongale
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. Processes 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

  • digital manufacturing
  • industrial maintenance
  • vibration damping
  • machine learning
  • deep learning
  • digital twin

Published Papers (2 papers)

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Research

22 pages, 3742 KiB  
Article
Developing an Anomaly Detection System for Automatic Defective Products’ Inspection
by Yu-Hsin Hung
Processes 2022, 10(8), 1476; https://doi.org/10.3390/pr10081476 - 27 Jul 2022
Cited by 1 | Viewed by 2129
Abstract
Since unqualified products cause enterprise revenue losses, product inspection is essential for maintaining manufacturing quality. An automated optical inspection (AOI) system is an efficient tool for product inspection, providing a convenient interface for users to view their products of interest. Specifically, in the [...] Read more.
Since unqualified products cause enterprise revenue losses, product inspection is essential for maintaining manufacturing quality. An automated optical inspection (AOI) system is an efficient tool for product inspection, providing a convenient interface for users to view their products of interest. Specifically, in the screw manufacturing industry, the conventional methods are the human visual inspection of the product and for the inspector to view the product image displayed on the dashboard of the AOI system. However, despite the inspector and the approach used, inspection results strongly depend on the inspector’s experience. Moreover, machine learning algorithms could improve the efficiency of human visual inspection, thus addressing the above problem. Based on these facts, we improved anomaly detection efficiency during product inspection, using product image data from the AOI system to obtain valuable information. This study notably used the visual geometry group network, Inception V3, and Xception algorithms to detect qualified and unqualified products during product image analytics. Therefore, we considered that the analyzed results could be integrated into a proposed cloud system for human–machine interaction. Thus, administrators can receive reminders concerning the anomaly-inspected notification through the proposed cloud system, comprising a message queuing telemetry transport protocol, an application programming interface, and a cloud dashboard. From the experimental results, the above-mentioned algorithms had more than 93% accuracy, especially Xception, which had a better performance during the defective type classification. From our study, the proposed system can successfully apply the obtained data in data communication, anomaly dashboards, and anomaly notifications. Full article
(This article belongs to the Special Issue Predictive Maintenance for Manufacturing System)
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20 pages, 7804 KiB  
Article
A Bearing Fault Diagnosis Method Based on Spectrum Map Information Fusion and Convolutional Neural Network
by Baiyang Wang, Guifang Feng, Dongyue Huo and Yuyun Kang
Processes 2022, 10(7), 1426; https://doi.org/10.3390/pr10071426 - 21 Jul 2022
Cited by 10 | Viewed by 1869
Abstract
With the development of information technology, it has become increasingly important to use intelligent algorithms to diagnose mechanical equipment faults based on vibration signals of rolling bearings. However, with the application of high-performance sensors in the Internet of Things, the complexity of real-time [...] Read more.
With the development of information technology, it has become increasingly important to use intelligent algorithms to diagnose mechanical equipment faults based on vibration signals of rolling bearings. However, with the application of high-performance sensors in the Internet of Things, the complexity of real-time classification of multichannel, multidimensional sensor signals is increasing. In view of the need for intelligent methods for fault diagnosis methods of mechanical equipment, the generalization ability of fault diagnosis models also needs to be further strengthened. In this context, in order to make fault diagnosis intelligent and efficient, a bearing fault diagnosis method based on spectrum map information fusion and convolutional neural network (CNN) is proposed. First, short-time Fourier transform (STFT) is used to analyze the multichannel vibration signal of the rolling bearing and obtain the frequency domain information of the signal over a period of time. Second, the information fusion is converted into two-dimensional (2D) images, which are input into CNN for training, and the bearing fault identification model is obtained. Next, the frequency domain information of each signal is converted into a 2D spectrum map, which is used as a CNN training dataset to train a bearing fault identification model. Finally, the diagnostic model is validated using the existing datasets. The results show that the accuracy of fault diagnosis using the proposed bearing is greater than 99.4% and can even reach 100%. The proposed method considerably reduces the workload of the diagnosis process, with strong robustness and generalization ability. Full article
(This article belongs to the Special Issue Predictive Maintenance for Manufacturing System)
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