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Machines
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Machinery Diagnostics and Prognostics
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Machinery Diagnostics and Prognostics

A special issue of Machines (ISSN 2075-1702).

Deadline for manuscript submissions: closed (31 October 2014) | Viewed by 46172

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
Prof. Dr. Andrew D. Ball

E-Mail Website
Guest Editor
Department of Computing and Engineering, The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK
Interests: detection and diagnosis of faults in mechanical; electrical and electro-hydraulic machines; data analysis and signal processing

Special Issue Information

Dear Colleagues,

Considerable advances in sensing hardware, information technologies, reasoning systems, and software algorithms, are leading to significant new developments in the areas of equipment health monitoring, fault diagnosis, and prognosis. These advances are enabling industries to undergo a fundamental shift towards condition based maintenance to improve equipment availability and readiness at reduced operating cost throughout the system life-cycle. Emergence of sensor networks is also bringing the possibility of collective learning algorithms and decision-theoretic approaches to facilitate effective and scalable diagnostics/prognostics technology for widespread deployment of condition-based maintenance. This special issue aims to bring together papers that report recent advances and challenges in designing and developing state-of-the-art fault diagnosis and prognosis systems and algorithms. Of particular interest are original contribution papers that demonstrate successful application of the algorithms and methods to complex equipment in a variety of industries. 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.

Suitable topics for this special issue include but are not limited to:

• Data-driven methods for anomaly detection, diagnosis, and prognosis
• Model-based methods for fault detection, diagnosis, and prognosis
• Health management system design and engineering
• Physics of failure modeling and simulation
• Health monitoring sensors and sensing
• Industrial applications

Prof. Dr. Ratna Babu Chinnam
Prof. Dr. Andrew Ball
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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

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2682 KiB  
Article
Number Determination of Successfully Packaged Dies Per Wafer Based on Machine Vision
by Hsuan-Ting Chang, Ren-Jie Pan and Hsiao-Wei Peng
Machines 2015, 3(2), 72-92; https://doi.org/10.3390/machines3020072 - 9 Apr 2015
Cited by 2 | Viewed by 7652
Abstract
Packaging the integrated circuit (IC) chip is a necessary step in the manufacturing process of IC products. In general, wafers with the same size and process should have a fixed number of packaged dies. However, many factors decrease the number of the actually [...] Read more.
Packaging the integrated circuit (IC) chip is a necessary step in the manufacturing process of IC products. In general, wafers with the same size and process should have a fixed number of packaged dies. However, many factors decrease the number of the actually packaged dies, such as die scratching, die contamination, and die breakage, which are not considered in the existing die-counting methods. Here we propose a robust method that can automatically determine the number of actual packaged dies by using machine vision techniques. During the inspection, the image is taken from the top of the wafer, in which most dies have been removed and packaged. There are five steps in the proposed method: wafer region detection, wafer position calibration, dies region detection, detection of die sawing lines, and die number counting. The abnormal cases of fractional dies in the wafer boundary and dropped dies during the packaging are considered in the proposed method as well. The experimental results show that the precision and recall rates reach 99.83% and 99.84%, respectively, when determining the numbers of actual packaged dies in the 41 test cases. Full article
(This article belongs to the Special Issue Machinery Diagnostics and Prognostics)
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1138 KiB  
Article
Initial Work on the Characterization of Additive Manufacturing (3D Printing) Using Software Image Analysis
by Jeremy Straub
Machines 2015, 3(2), 55-71; https://doi.org/10.3390/machines3020055 - 2 Apr 2015
Cited by 126 | Viewed by 18228
Abstract
A current challenge in additive manufacturing (commonly known as 3D printing) is the detection of defects. Detection of defects (or the lack thereof) in bespoke industrial manufacturing may be safety critical and reduce or eliminate the need for testing of printed objects. In [...] Read more.
A current challenge in additive manufacturing (commonly known as 3D printing) is the detection of defects. Detection of defects (or the lack thereof) in bespoke industrial manufacturing may be safety critical and reduce or eliminate the need for testing of printed objects. In consumer and prototype printing, early defect detection may facilitate the printer taking corrective measures (or pausing printing and alerting a user), preventing the need to re-print objects after the compounding of a small error occurs. This paper considers one approach to defect detection. It characterizes the efficacy of using a multi-camera system and image processing software to assess printing progress (thus detecting completion failure defects) and quality. The potential applications and extrapolations of this type of a system are also discussed. Full article
(This article belongs to the Special Issue Machinery Diagnostics and Prognostics)
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519 KiB  
Article
Residual Generator Fuzzy Identification for Wind TurbineBenchmark Fault Diagnosis
by Silvio Simani, Saverio Farsoni and Paolo Castaldi
Machines 2014, 2(4), 275-298; https://doi.org/10.3390/machines2040275 - 27 Nov 2014
Cited by 11 | Viewed by 5346
Abstract
In order to improve the availability of wind turbines, thus improving theirefficiency, it is important to detect and isolate faults in their earlier occurrence. The mainproblem of model-based fault diagnosis applied to wind turbines is represented by thesystem complexity, as well as the [...] Read more.
In order to improve the availability of wind turbines, thus improving theirefficiency, it is important to detect and isolate faults in their earlier occurrence. The mainproblem of model-based fault diagnosis applied to wind turbines is represented by thesystem complexity, as well as the reliability of the available measurements. In this work, adata-driven strategy relying on fuzzy models is presented, in order to build a fault diagnosissystem. Fuzzy theory jointly with the Frisch identification scheme for errors-in-variablemodels is exploited here, since it allows one to approximate unknown models and manageuncertain data. Moreover, the use of fuzzy models, which are directly identified from thewind turbine measurements, allows the design of the fault detection and isolation module.It is worth noting that, sometimes, the nonlinearity of a wind turbine system could lead toquite complex analytic solutions. However, IF-THEN fuzzy rules provide a simpler solution,important when on-line implementations have to be considered. The wind turbine benchmarkis used to validate the achieved performances of the suggested fault detection and isolationscheme. Finally, comparisons of the proposed methodology with respect to different faultdiagnosis methods serve to highlight the features of the suggested solution. Full article
(This article belongs to the Special Issue Machinery Diagnostics and Prognostics)
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1464 KiB  
Article
Data-Driven Methods for the Detection of Causal Structures in Process Technology
by Christian Kühnert and Jürgen Beyerer
Machines 2014, 2(4), 255-274; https://doi.org/10.3390/machines2040255 - 4 Nov 2014
Cited by 15 | Viewed by 4946
Abstract
In modern industrial plants, process units are strongly cross-linked with eachother, and disturbances occurring in one unit potentially become plant-wide. This can leadto a flood of alarms at the supervisory control and data acquisition system, hiding the originalfault causing the disturbance. Hence, one [...] Read more.
In modern industrial plants, process units are strongly cross-linked with eachother, and disturbances occurring in one unit potentially become plant-wide. This can leadto a flood of alarms at the supervisory control and data acquisition system, hiding the originalfault causing the disturbance. Hence, one major aim in fault diagnosis is to backtrackthe disturbance propagation path of the disturbance and to localize the root cause of thefault. Since detecting correlation in the data is not sufficient to describe the direction of thepropagation path, cause-effect dependencies among process variables need to be detected.Process variables that show a strong causal impact on other variables in the process comeinto consideration as being the root cause. In this paper, different data-driven methods areproposed, compared and combined that can detect causal relationships in data while solelyrelying on process data. The information of causal dependencies is used for localization ofthe root cause of a fault. All proposed methods consist of a statistical part, which determineswhether the disturbance traveling from one process variable to a second is significant, and aquantitative part, which calculates the causal information the first process variable has aboutthe second. The methods are tested on simulated data from a chemical stirred-tank reactorand on a laboratory plant. Full article
(This article belongs to the Special Issue Machinery Diagnostics and Prognostics)
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Review

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256 KiB  
Review
Problems in Assessment of Novel Biopotential Front-End with Dry Electrode: A Brief Review
by Gaetano D. Gargiulo, Paolo Bifulco, Mario Cesarelli, Antonio Fratini and Maria Romano
Machines 2014, 2(1), 87-98; https://doi.org/10.3390/machines2010087 - 25 Feb 2014
Cited by 31 | Viewed by 9034
Abstract
Developers of novel or improved front-end circuits for biopotential recordings using dry electrodes face the challenge of validating their design. Dry electrodes allow more user-friendly and pervasive patient-monitoring, but proof is required that new devices can perform biopotential recording with a quality at [...] Read more.
Developers of novel or improved front-end circuits for biopotential recordings using dry electrodes face the challenge of validating their design. Dry electrodes allow more user-friendly and pervasive patient-monitoring, but proof is required that new devices can perform biopotential recording with a quality at least comparable to existing medical devices. Aside from electrical safety requirement recommended by standards and concise circuit requirement, there is not yet a complete validation procedure able to demonstrate improved or even equivalent performance of the new devices. This short review discusses the validation procedures presented in recent, landmark literature and offers interesting issues and hints for a more complete assessment of novel biopotential front-end. Full article
(This article belongs to the Special Issue Machinery Diagnostics and Prognostics)
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