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Sensors and Intelligent Data Processing for Condition Monitoring and Fault Diagnosis

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Published Papers

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 14800

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Special Issue Editors

Dr. Alberto Cardoso

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Guest Editor

Department of Informatics Engineering, University of Coimbra, Pólo II da Universidade de Coimbra, 3030-290 Coimbra, Portugal
Interests: cyber-physical systems; data analysis and processing; intelligent systems; wireless sensor networks; sensor data fusion; remote and virtual laboratories; geographic information systems; soft computing; supervision and fault diagnosis; predictive maintenance
Special Issues, Collections and Topics in MDPI journals

Dr. Jorge Henriques

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Guest Editor

Centre for Informatics and Systems of the University of Coimbra (CISUC), Department of Informatics Engineering, University of Coimbra, 3030-790 Coimbra, Portugal
Interests: data science; computational intelligence; intelligent systems; fault diagnosis; prediction and decision
Special Issues, Collections and Topics in MDPI journals

Dr. Paulo Gil

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Guest Editor

Centre for Informatics and Systems of the University of Coimbra (CISUC), Department of Informatics Engineering, University of Coimbra, 3030-790 Coimbra, Portugal
Interests: data science; distributed systems; intelligent systems; cyber-physical systems; hybrid control systems
Special Issues, Collections and Topics in MDPI journals

Dr. Yacine Rebahi

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Guest Editor

Fraunhofer Fokus, Kaiserin Augusta Allee 31, 10589 Berlin, Germany
Interests: Cybersecurity; Machine learning; industry 4.0; NGN/NGMN

Special Issue Information

Dear Colleagues,

Condition monitoring and fault diagnosis are very important for early fault detection and diagnosis as well as the real-time health condition monitoring of dynamic systems in different application areas, particularly for industrial systems. In the context of Industry 4.0 and circular manufacturing, the development of innovative methodologies and intelligent systems is crucial to ensure the adequate operation of machines without the unplanned interruption of equipment operation, optimizing production lines and minimizing both resources and energy consumption.

Artificial intelligence algorithms, such as machine learning, deep learning and transfer learning, are important tools for analyzing and processing sensor data to enable health condition monitoring as well as the fault diagnosis and predictive maintenance of industrial systems, contributing to extending their life cycle, improving manufacturing efficiency and optimizing maintenance task planning.

Considering the relevance of advances and achievements in these topics as well as the continuous extension of the state-of-the-art, this Special Issue offers an opportunity to present theoretical and practical approaches, solutions, and results for the interdisciplinary challenges that these topics involve.

Innovative contributions fitting within the scope of Sensors and covering the topics of condition monitoring and fault diagnosis, as well as data-driven approaches to predictive maintenance, among other related topics, are most welcome.

Dr. Alberto Cardoso
Dr. Jorge Henriques
Dr. Paulo Gil
Dr. Yacine Rebahi
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

condition monitoring
fault diagnosis
predictive maintenance
machine-learning algorithms
sensor data processing
sensor fusion for diagnostics and prognostics
data-driven approaches
intelligent systems
Industry 4.0
circular manufacturing

Published Papers (5 papers)

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Research

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26 pages, 2365 KiB

Open AccessArticle

An Integrated Framework for Data Quality Fusion in Embedded Sensor Systems

by Christoph Scholl, Maximilian Spiegler, Klaus Ludwig, Bjoern M. Eskofier, Andreas Tobola and Dario Zanca

Sensors 2023, 23(8), 3798; https://0-doi-org.brum.beds.ac.uk/10.3390/s23083798 - 07 Apr 2023

Viewed by 1367

Abstract

The advancement of embedded sensor systems allowed the monitoring of complex processes based on connected devices. As more and more data are produced by these sensor systems, and as the data are used in increasingly vital areas of applications, it is of growing importance to also track the data quality of these systems. We propose a framework to fuse sensor data streams and associated data quality attributes into a single meaningful and interpretable value that represents the current underlying data quality. Based on the definition of data quality attributes and metrics to determine real-valued figures representing the quality of the attributes, the fusion algorithms are engineered. Methods based on maximum likelihood estimation (MLE) and fuzzy logic are used to perform data quality fusion by utilizing domain knowledge and sensor measurements. Two data sets are used to verify the proposed fusion framework. First, the methods are applied to a proprietary data set targeting sample rate inaccuracies of a micro-electro-mechanical system (MEMS) accelerometer and second, to the publicly available Intel Lab Data set. The algorithms are verified against their expected behavior based on data exploration and correlation analysis. We prove that both fusion approaches are capable of detecting data quality issues and providing an interpretable data quality indicator. Full article

(This article belongs to the Special Issue Sensors and Intelligent Data Processing for Condition Monitoring and Fault Diagnosis)

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19 pages, 10645 KiB

Open AccessArticle

Pipeline Leakage Detection Using Acoustic Emission and Machine Learning Algorithms

by Niamat Ullah, Zahoor Ahmed and Jong-Myon Kim

Sensors 2023, 23(6), 3226; https://0-doi-org.brum.beds.ac.uk/10.3390/s23063226 - 17 Mar 2023

Cited by 13 | Viewed by 5015

Abstract

Pipelines play a significant role in liquid and gas resource distribution. Pipeline leaks, however, result in severe consequences, such as wasted resources, risks to community health, distribution downtime, and economic loss. An efficient autonomous leakage detection system is clearly required. The recent leak diagnosis capability of acoustic emission (AE) technology has been well demonstrated. This article proposes a machine learning-based platform for leakage detection for various pinhole-sized leaks using the AE sensor channel information. Statistical measures, such as kurtosis, skewness, mean value, mean square, root mean square (RMS), peak value, standard deviation, entropy, and frequency spectrum features, were extracted from the AE signal as features to train the machine learning models. An adaptive threshold-based sliding window approach was used to retain the properties of both bursts and continuous-type emissions. First, we collected three AE sensor datasets and extracted 11 time domain and 14 frequency domain features for a one-second window for each AE sensor data category. The measurements and their associated statistics were transformed into feature vectors. Subsequently, these feature data were utilized for training and evaluating supervised machine learning models to detect leaks and pinhole-sized leaks. Several widely known classifiers, such as neural networks, decision trees, random forests, and k-nearest neighbors, were evaluated using the four datasets regarding water and gas leakages at different pressures and pinhole leak sizes. We achieved an exceptional overall classification accuracy of 99%, providing reliable and effective results that are suitable for the implementation of the proposed platform. Full article

(This article belongs to the Special Issue Sensors and Intelligent Data Processing for Condition Monitoring and Fault Diagnosis)

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22 pages, 10897 KiB

Open AccessArticle

Online Monitoring of Sensor Calibration Status to Support Condition-Based Maintenance

by Alexandre Martins, Inácio Fonseca, José Torres Farinha, João Reis and António J. Marques Cardoso

Sensors 2023, 23(5), 2402; https://0-doi-org.brum.beds.ac.uk/10.3390/s23052402 - 21 Feb 2023

Cited by 7 | Viewed by 2266

Abstract

Condition-Based Maintenance (CBM), based on sensors, can only be reliable if the data used to extract information are also reliable. Industrial metrology plays a major role in ensuring the quality of the data collected by the sensors. To guarantee that the values collected by the sensors are reliable, it is necessary to have metrological traceability made by successive calibrations from higher standards to the sensors used in the factories. To ensure the reliability of the data, a calibration strategy must be put in place. Usually, sensors are only calibrated on a periodic basis; so, they often go for calibration without it being necessary or collect data inaccurately. In addition, the sensors are checked often, increasing the need for manpower, and sensor errors are frequently overlooked when the redundant sensor has a drift in the same direction. It is necessary to acquire a calibration strategy based on the sensor condition. Through online monitoring of sensor calibration status (OLM), it is possible to perform calibrations only when it is really necessary. To reach this end, this paper aims to provide a strategy to classify the health status of the production equipment and of the reading equipment that uses the same dataset. A measurement signal from four sensors was simulated, for which Artificial Intelligence and Machine Learning with unsupervised algorithms were used. This paper demonstrates how, through the same dataset, it is possible to obtain distinct information. Because of this, we have a very important feature creation process, followed by Principal Component Analysis (PCA), K-means clustering, and classification based on Hidden Markov Models (HMM). Through three hidden states of the HMM, which represent the health states of the production equipment, we will first detect, through correlations, the features of its status. After that, an HMM filter is used to eliminate those errors from the original signal. Next, an equal methodology is conducted for each sensor individually and using statistical features in the time domain where we can obtain, through HMM, the failures of each sensor. Full article

(This article belongs to the Special Issue Sensors and Intelligent Data Processing for Condition Monitoring and Fault Diagnosis)

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19 pages, 11606 KiB

Open AccessArticle

Damage Detection for Rotating Blades Using Digital Image Correlation with an AC-SURF Matching Algorithm

by Jiawei Gu, Gang Liu and Mengzhu Li

Sensors 2022, 22(21), 8110; https://0-doi-org.brum.beds.ac.uk/10.3390/s22218110 - 23 Oct 2022

Cited by 1 | Viewed by 1698

Abstract

The motion information of blades is a key reflection of the operation state of an entire wind turbine unit. However, the special structure and operation characteristics of rotating blades have become critical obstacles for existing contact vibration monitoring technologies. Digital image correlation performs powerfully in non-contact, full-field measurements, and has increasingly become a popular method for solving the problem of rotating blade monitoring. Aiming at the problem of large-scale rotation matching for blades, this paper proposes a modified speeded-up robust features (SURF)-enhanced digital image correlation algorithm to extract the full-field deformation of blades. Combining an angle compensation (AC) strategy, the AC-SURF algorithm is developed to estimate the rotation angle. Then, an iterative process is presented to calculate the accurate rotation displacement. Subsequently, with reference to the initial state of rotation, the relative strain distribution caused by flaws is determined. Finally, the sensitivity of the strain is validated by comparing the three damage indicators including unbalanced rotational displacement, frequency change, and surface strain field. The performance of the proposed algorithm is verified by laboratory tests of blade damage detection and wind turbine model deformation monitoring. The study demonstrated that the proposed method provides an effective and robust solution for the operation status monitoring and damage detection of wind turbine blades. Furthermore, the strain-based damage detection algorithm is more advantageous in identifying cracks on rotating blades than one based on fluctuated displacement or frequency change. Full article

(This article belongs to the Special Issue Sensors and Intelligent Data Processing for Condition Monitoring and Fault Diagnosis)

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Review

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37 pages, 3241 KiB

Open AccessReview

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

by Iva Matetić, Ivan Štajduhar, Igor Wolf and Sandi Ljubic

Sensors 2023, 23(1), 1; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010001 - 20 Dec 2022

Cited by 11 | Viewed by 3727

Abstract

Heating, ventilation, and air conditioning (HVAC) systems are a popular research topic because buildings’ energy is mostly used for heating and/or cooling. These systems heavily rely on sensory measurements and typically make an integral part of the smart building concept. As such, they require the implementation of fault detection and diagnosis (FDD) methodologies, which should assist users in maintaining comfort while consuming minimal energy. Despite the fact that FDD approaches are a well-researched subject, not just for improving the operation of HVAC systems but also for a wider range of systems in industrial processes, there is a lack of application in commercial buildings due to their complexity and low transferability. The aim of this review paper is to present and systematize cutting-edge FDD methodologies, encompassing approaches and special techniques that can be applied in HVAC systems, as well as to provide best-practice heuristics for researchers and solution developers in this domain. While the literature analysis targets the FDD perspective, the main focus is put on the data-driven approach, which covers commonly used models and data pre-processing techniques in the field. Data-driven techniques and FDD solutions based on them, which are most commonly used in recent HVAC research, form the backbone of our study, while alternative FDD approaches are also presented and classified to properly contextualize and round out the review. Full article

(This article belongs to the Special Issue Sensors and Intelligent Data Processing for Condition Monitoring and Fault Diagnosis)

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