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Sensors and Measurement Techniques for the Diagnostics and Prognostics of Mechanical Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 8175

Special Issue Editors

Prof. Dr. Giulio D'Emilia

E-Mail Website
Guest Editor
Department of Industrial and Information Engineering and of Economics, University of L’Aquila, 67100 L’Aquila, Italy
Interests: measuring systems; MEMS accelerometers; sensor integration; uncertainty assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Piotr Bilski

E-Mail Website
Guest Editor
Faculty of Electronics and information technology, Technical University of Warsaw, 00-661 Warsaw, Poland
Interests: Artificial Intelligence; Technical Diagnostics
Dr. Emanuela Natale

E-Mail Website
Guest Editor
Department of Industrial and Information Engineering and of Economics, University of L’Aquila, 67100 L’Aquila, Italy
Interests: measuring systems; MEMS accelerometers; sensor integration; uncertainty assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the modern industrial context, which is strongly interconnected with a high degree of automation, sensors and sensor networks have an important role. They provide quantitative information about physical processes, which allows people or smart devices to make suitable decisions, for example, about defective product management or maintenance strategies.

A large number of different sensors, or a fusion of them, can be used to identify faulty conditions or to predict failure events, depending on the type of defect to be detected and the symptoms that the system manifests as a consequence of a failure.

In order to develop robust and reliable methods for the diagnosis/prognosis of industrial systems, and to ensure general validity and easy implementation in the field, many aspects have to be considered, with reference to:

  • Sensor type, number and positioning;
  • Uncertainty analysis;
  • Validation of data;
  • Feature extraction and selection;
  • Data processing techniques for the classification of defects or parameter fitting, based on data-driven, knowledge-based, or hybrid approaches.

More topics will also be considered if they are coherent with this theme.

Prof. Dr. Giulio D'Emilia
Dr. Piotr Bilski
Dr. Emanuela Natale
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • sensors
  • measuring systems
  • uncertainty assessment
  • fault detection
  • condition monitoring
  • condition-based maintenance
  • diagnosis
  • prognostics
  • machine learning
  • deep learning
  • sensor fusion

Published Papers (3 papers)

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Research

19 pages, 810 KiB  
Article
A Data-Driven Framework for Small Hydroelectric Plant Prognosis Using Tsfresh and Machine Learning Survival Models
by Rodrigo Barbosa de Santis, Tiago Silveira Gontijo and Marcelo Azevedo Costa
Sensors 2023, 23(1), 12; https://doi.org/10.3390/s23010012 - 20 Dec 2022
Cited by 1 | Viewed by 1987
Abstract
Maintenance in small hydroelectric plants (SHPs) is essential for securing the expansion of clean energy sources and supplying the energy estimated to be required for the coming years. Identifying failures in SHPs before they happen is crucial for allowing better management of asset [...] Read more.
Maintenance in small hydroelectric plants (SHPs) is essential for securing the expansion of clean energy sources and supplying the energy estimated to be required for the coming years. Identifying failures in SHPs before they happen is crucial for allowing better management of asset maintenance, lowering operating costs, and enabling the expansion of renewable energy sources. Most fault prognosis models proposed thus far for hydroelectric generating units are based on signal decomposition and regression models. In the specific case of SHPs, there is a high occurrence of data being censored, since the operation is not consistently steady and can be repeatedly interrupted due to transmission problems or scarcity of water resources. To overcome this, we propose a two-step, data-driven framework for SHP prognosis based on time series feature engineering and survival modeling. We compared two different strategies for feature engineering: one using higher-order statistics and the other using the Tsfresh algorithm. We adjusted three machine learning survival models—CoxNet, survival random forests, and gradient boosting survival analysis—for estimating the concordance index of these approaches. The best model presented a significant concordance index of 77.44%. We further investigated and discussed the importance of the monitored sensors and the feature extraction aggregations. The kurtosis and variance were the most relevant aggregations in the higher-order statistics domain, while the fast Fourier transform and continuous wavelet transform were the most frequent transformations when using Tsfresh. The most important sensors were related to the temperature at several points, such as the bearing generator, oil hydraulic unit, and turbine radial bushing. Full article
(This article belongs to the Special Issue Sensors and Measurement Techniques for the Diagnostics and Prognostics of Mechanical Systems)
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16 pages, 2328 KiB  
Article
Multi-Sensor Fault Diagnosis Based on Time Series in an Intelligent Mechanical System
by Zhuoran Xu, Qianmu Li, Linfang Qian and Manyi Wang
Sensors 2022, 22(24), 9973; https://doi.org/10.3390/s22249973 - 17 Dec 2022
Cited by 4 | Viewed by 2202
Abstract
Intelligent mechanical systems are a focused area nowadays. One of the requirements of intelligent mechanical systems is to achieve intelligent fault diagnosis through the real-time acquisition and analysis of data from various sensors installed on mechanical components. In this paper, a new fault [...] Read more.
Intelligent mechanical systems are a focused area nowadays. One of the requirements of intelligent mechanical systems is to achieve intelligent fault diagnosis through the real-time acquisition and analysis of data from various sensors installed on mechanical components. In this paper, a new fault diagnosis method is proposed to solve the problems of difficulty in integrating the fault diagnosis algorithm and locating fault parts due to the complexity of modern mechanical systems. The complexity of modern industrial intelligent systems is due to the fact that the systems are composed of multiple components and there are various connections between them. Common fault diagnosis is to design specialized fault identification algorithms for the physical characteristics of each component, and the integration of different algorithms is a major challenge for system performance. Therefore, this paper investigates a general algorithm for the fault diagnosis of complex systems using the timing characteristics of sensors and transfer entropy. The fault diagnosis algorithm is based on the prediction of multi-dimensional long time series using Autoformer, and fault identification is performed based on the deviation of the predicted value from the actual value. After fault identification, a root cause analysis method of faults based on transfer entropy is proposed. The method can locate the component where the fault occurs more accurately based on the analysis of the cause–effect relationship of each component and help maintenance personnel to troubleshoot the fault. Full article
(This article belongs to the Special Issue Sensors and Measurement Techniques for the Diagnostics and Prognostics of Mechanical Systems)
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24 pages, 5069 KiB  
Article
Fuzzy Risk-Based Maintenance Strategy with Safety Considerations for the Mining Industry
by Agnieszka Tubis, Sylwia Werbińska-Wojciechowska, Pawel Sliwinski and Radoslaw Zimroz
Sensors 2022, 22(2), 441; https://doi.org/10.3390/s22020441 - 07 Jan 2022
Cited by 10 | Viewed by 3015
Abstract
Enterprises today are increasingly seeking maintenance management strategies to ensure that their machines run faultlessly. This problem is particularly relevant in the mining sector, due to the demanding working conditions of underground mines and machines and equipment-operating regimes. Therefore, in this article, the [...] Read more.
Enterprises today are increasingly seeking maintenance management strategies to ensure that their machines run faultlessly. This problem is particularly relevant in the mining sector, due to the demanding working conditions of underground mines and machines and equipment-operating regimes. Therefore, in this article, the authors proposed a new approach to mining machinery maintenance management, based on the concept of risk-based maintenance (RBM) and taking into account safety issues. The proposed method includes five levels of analysis, of which the first level focuses on hazard analysis, while the next three are connected with a risk evaluation. The final level relates to determining the RBM recommendations. The recommendations are defined in relation to the three main improvement areas: maintenance, safety, and resource availability/allocation. The proposed approach is based on the use of fuzzy logic. To present the possibilities of implementing our method, a case study covering the operation of selected mining machinery in a selected Polish underground mine is presented. In the case of mining machinery, fourteen adverse-event scenarios were identified and investigated; general recommendations were also given. The authors have also indicated further directions of research work to optimize system maintenance strategies, based on the concept of risk-based maintenance. Additionally, the discussion about the implementation possibilities of the approach developed herein is provided. Full article
(This article belongs to the Special Issue Sensors and Measurement Techniques for the Diagnostics and Prognostics of Mechanical Systems)
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