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The Application of Sensors in Fault Diagnosis and Prognosis
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The Application of Sensors in Fault Diagnosis and Prognosis

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

Deadline for manuscript submissions: closed (15 August 2021) | Viewed by 18164

Special Issue Editors

Prof. Enrique López Droguett

E-Mail Website
Guest Editor
University of Maryland, College Park, USA;
Mechanical Engineering Department, University of Chile, Santiago, Chile
Interests: reliability of complex systems; deep learning methods in fault diagnosis and prognosis; big data analytics in predictive maintenance; uncertainty analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Márcio das Chagas Moura

E-Mail Website
Guest Editor
Industrial Engineering Department, Federal University of Pernambuco, Recife-PE, Brazil
Interests: reliability engineering and risk analysis; including fault diagnosis and prognosis of failure; deep learning; accelerated life testing; stochastic processes applied to risk and reliability; dynamic complex systems; soft computing; modeling and simulation techniques

Special Issue Information

Dear Colleagues,

With the advent of Industry 4.0, Industrial Internet of Things (IoT), the proliferation of inexpensive sensing technology, and advances in prognostics and health management, customers are not only requiring reliable physical asset investment, but also that their assets diagnose and prognose faults and alert maintenance staff when components need to be replaced. These assets often have substantial sensor systems capable of generating millions of data points a minute. The availability and access to these massive and multidimensional sensor data open the door to a wide gamut of opportunities for powerful approaches for smart, autonomous, and online early fault detection and prognostics of remaining useful life. This Special Issue invites contributions concerning the Application of Sensors to Fault Diagnosis and Prognosis. The Guest Editors encourage authors to submit research articles with original perspectives and advanced thinking on this topic and related issues. Potential topics should include, but not be limited to, the following:

  • Physics-based model and hybrid methods for multi-sensor fusion in fault diagnosis and prognosis
  • Data-driven approaches, including deep learning-based models, for multi-sensor fusion in fault diagnosis and prognosis
  • Structural health monitoring
  • Prognostics and health management (PHM)
  • Predictive maintenance
  • Decision making in reliability and maintenance

Prof. Enrique López Droguett
Guest Editor

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

  • sensor fusion
  • fault diagnosis
  • fault prognosis
  • reliability
  • predictive maintenance

Published Papers (4 papers)

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Research

15 pages, 2505 KiB  
Article
Risk Analysis by a Probabilistic Model of the Measurement Process
by Wojciech Toczek and Janusz Smulko
Sensors 2021, 21(6), 2053; https://doi.org/10.3390/s21062053 - 15 Mar 2021
Cited by 4 | Viewed by 1498
Abstract
The aim of the article is presentation of the testing methodology and results of examination the probabilistic model of the measurement process. The case study concerns the determination of the risk of an incorrect decision in the assessment of the compliance of products [...] Read more.
The aim of the article is presentation of the testing methodology and results of examination the probabilistic model of the measurement process. The case study concerns the determination of the risk of an incorrect decision in the assessment of the compliance of products by measurement. Measurand is characterized by the generalized Rayleigh distribution. The model of the measurement process was tested in parallel mode by six risk metrics. An undesirable effect in the reconstruction building block of the model was detected, consisting in the distortion of probability distribution at the edges of the measuring range. The paper gives guidelines on how to use the model, to obtain the analytical risk assessment consistent with the results of the Monte Carlo method. The study can be useful in product quality control, test design, and fault diagnosis. Full article
(This article belongs to the Special Issue The Application of Sensors in Fault Diagnosis and Prognosis)
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14 pages, 11799 KiB  
Article
Ultrasonic Sensors Enabling Early Detection of Emergency Trends and Analysis of Structure Inclination and Stability by Means of Highly Accurate Level Measurements
by Leszek Ornoch, Paweł Popielski, Andrzej Olszewski and Adam Kasprzak
Sensors 2021, 21(5), 1789; https://doi.org/10.3390/s21051789 - 04 Mar 2021
Cited by 5 | Viewed by 2396
Abstract
Building inclinations can be measured through the use of ultrasonic hydrostatic levelers. These are used to measure long-term relative displacements of vertical parts of structures and utilize the principle of communicating vessels (similar to the classic water scales). The presented ultrasonic displacement measurement [...] Read more.
Building inclinations can be measured through the use of ultrasonic hydrostatic levelers. These are used to measure long-term relative displacements of vertical parts of structures and utilize the principle of communicating vessels (similar to the classic water scales). The presented ultrasonic displacement measurement technique was developed by Ultrasystem in the 1990s and was applied to several objects in Poland. Long-term measurements enabled the development of a model of object behavior under the influence of various factors. Among these are the annual cycle of temperature changes, fluctuating water levels, turbine chamber emptying, etc. Such a model can facilitate the prediction of failure based on the appearance of changes deviating from typical behavior (e.g., a much stronger dependence of the inclination as a function of the water level). The results obtained with the help of ultrasonic sensors enable the observation of subtle deformations of the object, which is valuable when developing and calibrating new models of the object (e.g., by means of the finite element method). Full article
(This article belongs to the Special Issue The Application of Sensors in Fault Diagnosis and Prognosis)
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14 pages, 2449 KiB  
Article
Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach
by Yassine Bouabdallaoui, Zoubeir Lafhaj, Pascal Yim, Laure Ducoulombier and Belkacem Bennadji
Sensors 2021, 21(4), 1044; https://doi.org/10.3390/s21041044 - 03 Feb 2021
Cited by 58 | Viewed by 9613
Abstract
The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In [...] Read more.
The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings. Full article
(This article belongs to the Special Issue The Application of Sensors in Fault Diagnosis and Prognosis)
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26 pages, 3879 KiB  
Article
Condition-Based Maintenance with Reinforcement Learning for Dry Gas Pipeline Subject to Internal Corrosion
by Zahra Mahmoodzadeh, Keo-Yuan Wu, Enrique Lopez Droguett and Ali Mosleh
Sensors 2020, 20(19), 5708; https://doi.org/10.3390/s20195708 - 07 Oct 2020
Cited by 19 | Viewed by 3754
Abstract
Gas pipeline systems are one of the largest energy infrastructures in the world and are known to be very efficient and reliable. However, this does not mean they are prone to no risk. Corrosion is a significant problem in gas pipelines that imposes [...] Read more.
Gas pipeline systems are one of the largest energy infrastructures in the world and are known to be very efficient and reliable. However, this does not mean they are prone to no risk. Corrosion is a significant problem in gas pipelines that imposes large risks such as ruptures and leakage to the environment and the pipeline system. Therefore, various maintenance actions are performed routinely to ensure the integrity of the pipelines. The costs of the corrosion-related maintenance actions are a significant portion of the pipeline’s operation and maintenance costs, and minimizing this large cost is a highly compelling subject that has been addressed by many studies. In this paper, we investigate the benefits of applying reinforcement learning (RL) techniques to the corrosion-related maintenance management of dry gas pipelines. We first address the rising need for a simulated testbed by proposing a test bench that models corrosion degradation while interacting with the maintenance decision-maker within the RL environment. Second, we propose a condition-based maintenance management approach that leverages a data-driven RL decision-making methodology. An RL maintenance scheduler is applied to the proposed test bench, and the results show that applying the proposed condition-based maintenance management technique can reduce up to 58% of the maintenance costs compared to a periodic maintenance policy while securing pipeline reliability. Full article
(This article belongs to the Special Issue The Application of Sensors in Fault Diagnosis and Prognosis)
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