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Machines
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Advances in Diagnostics and Prognostics in the Era of Industry...
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Advances in Diagnostics and Prognostics in the Era of Industry 4.0

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 December 2022) | Viewed by 7707

Special Issue Editors

Prof. Dr. Ratna Babu Chinnam

E-Mail Website
Guest Editor
Chair, Department of Industrial & Systems Engineering, Wayne State University, 4815 Fourth Street, Detroit, MI 48201, USA
Interests: autonomous diagnostics; prognostics; Industry 4.0; smart engineering systems; supply chain management; sustainability
Special Issues, Collections and Topics in MDPI journals
Dr. Murat Yildirim

E-Mail Website
Guest Editor
Director, Cyber-Physical Systems Laboratory, Industrial & Systems Engineering Department, College of Engineering, Wayne State University, 4815 Fourth Street, Detroit, MI 48201, USA
Interests: monitoring; diagnostics; cyberphysical systems; federated learning; operations research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in sensor technology, industrial information systems, and predictive algorithms have unlocked a new generation of approaches in monitoring, diagnostics, prognostics, and asset management. These advances enable deeper insights into asset failure processes, and innovative methods to connect these insights with condition monitoring and maintenance management for industrial assets. Recent growth in the adoption of Industry 4.0 technologies and associated Internet-of-Things (IoT) systems and sensor networks infrastructure provide scalable platforms for the development of comprehensive solutions for effective and cost-efficient asset management. However, there are several fundamental challenges centered around collaborative learning (e.g., “federated learning” and “transfer learning”) from fleets of assets, edge computing, and data security and privacy. New generation of models also need to address the ability to derive audit trails for predictions/recommendations and also facilitate interpretation of different failure processes and, ultimately, knowledge development and management.

This Special Issue seeks original research papers focusing on advances in all facets of diagnostics and prognostics. We welcome papers that offer new research directions and insights. Of particular interest are original contribution papers that demonstrate successful application of the algorithms and methods to complex equipment. We hope that this Special Issue will be useful and informative to both researchers and practitioners. We also hope to deliver readers promising new ideas and directions for future research.

Research topics that are of interest for this special issue include but not limited to:

  • Fault detection, diagnostics, and prognostics
  • Physics of failure modeling and simulation
  • Integration of physics-based models and data-driven algorithms
  • Health monitoring sensors and sensing
  • Sensor-driven maintenance and inspection models
  • Edge computing solutions for scalable diagnostics and prognostics
  • Managing uncertainty and risk in predictive maintenance
  • Design and management of predictive maintenance platforms
  • Industrial applications

Prof. Dr. Ratna Babu Chinnam
Dr. Murat Yildirim
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

  • monitoring
  • diagnostics
  • prognostics
  • maintenance
  • sensors
  • Internet of things (IoT)
  • edge computing

Published Papers (4 papers)

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Research

21 pages, 2865 KiB  
Article
Fault Detection, Diagnostics, and Treatment in Automated Manufacturing Systems Using Internet of Things and Colored Petri Nets
by Husam Kaid, Abdulrahman Al-Ahmari and Khaled N. Alqahtani
Machines 2023, 11(2), 173; https://doi.org/10.3390/machines11020173 - 27 Jan 2023
Cited by 3 | Viewed by 1991
Abstract
Internet of things (IoT) applications, which include environmental sensors and control of automated manufacturing systems (AMS), are growing at a rapid rate. In terms of hardware and software designs, communication protocols, and/or manufacturers, IoT devices can be extremely heterogeneous. Therefore, when these devices [...] Read more.
Internet of things (IoT) applications, which include environmental sensors and control of automated manufacturing systems (AMS), are growing at a rapid rate. In terms of hardware and software designs, communication protocols, and/or manufacturers, IoT devices can be extremely heterogeneous. Therefore, when these devices are interconnected to create a complicated system, it can be very difficult to detect and fix any failures. This paper proposes a new reliability design methodology using “colored resource-oriented Petri nets” (CROPNs) and IoT to identify significant reliability metrics in AMS, which can assist in accurate diagnosis, prognosis, and resulting automated repair to enhance the adaptability of IoT devices within complicated cyber-physical systems (CPSs). First, a CROPN is constructed to state “sufficient and necessary conditions” for the liveness of the CROPN under resource failures and deadlocks. Then, a “fault diagnosis and treatment” technique is presented, which combines the resulting network with IoT to guarantee the reliability of the CROPN. In addition, a GPenSIM tool is used to verify, validate, and analyze the reliability of the IoT-based CROPN. Comparing the results to those found in the literature shows that they are structurally simpler and more effective in solving the deadlock issue and modeling AMS reliability. Full article
(This article belongs to the Special Issue Advances in Diagnostics and Prognostics in the Era of Industry 4.0)
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34 pages, 5544 KiB  
Article
Online System Prognostics with Ensemble Models and Evolving Clustering
by Fling Tseng, Dimitar Filev, Murat Yildirim and Ratna Babu Chinnam
Machines 2023, 11(1), 40; https://doi.org/10.3390/machines11010040 - 29 Dec 2022
Cited by 1 | Viewed by 1251
Abstract
An online evolving clustering (OEC) method equivalent to ensemble modeling is proposed to tackle prognostics problems of learning and the prediction of remaining useful life (RUL). During the learning phase, OEC extracts predominant operating modes as multiple evolving clusters (EC). Each EC is [...] Read more.
An online evolving clustering (OEC) method equivalent to ensemble modeling is proposed to tackle prognostics problems of learning and the prediction of remaining useful life (RUL). During the learning phase, OEC extracts predominant operating modes as multiple evolving clusters (EC). Each EC is associated with its own Weibull distribution-inspired degradation (survivability) model that will receive incremental online modifications as degradation signals become available. Example case studies from machining (drilling) and automotive brake-pad wear prognostics are used to validate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Advances in Diagnostics and Prognostics in the Era of Industry 4.0)
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16 pages, 4656 KiB  
Article
Machining Quality Prediction of Marine Diesel Engine Block Based on Error Transmission Network
by Li Sun, Xiaodie Ren, Honggen Zhou, Guochao Li, Weibin Yang, Junjie Zhao and Yinfei Liu
Machines 2022, 10(11), 1081; https://doi.org/10.3390/machines10111081 - 16 Nov 2022
Cited by 3 | Viewed by 1202
Abstract
In view of the high precision requirement of the marine diesel engine body and the difficulty of quality control, a quality prediction method of the body, based on a process error transfer network, is proposed. First, according to the processing information of the [...] Read more.
In view of the high precision requirement of the marine diesel engine body and the difficulty of quality control, a quality prediction method of the body, based on a process error transfer network, is proposed. First, according to the processing information of the body, the network nodes and edges are abstracted to establish the process error transfer network of the body. Then, the key quality control points and key quality features of the diesel engine body are determined by the PageRank and node degree. The key quality features obtained from the network analysis are taken as the output, and the corresponding process errors and process parameters are taken as the input. Finally, the quality prediction model of the body is established based on SVR algorithm, and the C, g parameters of SVR algorithm are optimized by the K-fold cross-validation method and grid search method to improve the prediction accuracy of the body processing quality. Full article
(This article belongs to the Special Issue Advances in Diagnostics and Prognostics in the Era of Industry 4.0)
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23 pages, 4787 KiB  
Article
A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions
by Amelie Bender
Machines 2021, 9(10), 210; https://doi.org/10.3390/machines9100210 - 24 Sep 2021
Cited by 4 | Viewed by 2129
Abstract
While increasing digitalization enables multiple advantages for a reliable operation of technical systems, a remaining challenge in the context of condition monitoring is seen in suitable consideration of uncertainties affecting the monitored system. Therefore, a suitable prognostic approach to predict the remaining useful [...] Read more.
While increasing digitalization enables multiple advantages for a reliable operation of technical systems, a remaining challenge in the context of condition monitoring is seen in suitable consideration of uncertainties affecting the monitored system. Therefore, a suitable prognostic approach to predict the remaining useful lifetime of complex technical systems is required. To handle different kinds of uncertainties, a novel Multi-Model-Particle Filtering-based prognostic approach is developed and evaluated by the use case of rubber-metal-elements. These elements are maintained preventively due to the strong influence of uncertainties on their behavior. In this paper, two measurement quantities are compared concerning their ability to establish a prediction of the remaining useful lifetime of the monitored elements and the influence of present uncertainties. Based on three performance indices, the results are evaluated. A comparison with predictions of a classical Particle Filter underlines the superiority of the developed Multi-Model-Particle Filter. Finally, the value of the developed method for enabling condition monitoring of technical systems related to uncertainties is given exemplary by a comparison between the preventive and the predictive maintenance strategy for the use case. Full article
(This article belongs to the Special Issue Advances in Diagnostics and Prognostics in the Era of Industry 4.0)
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