Special Issue "Incipient Fault Detection and Diagnosis, Fault-Tolerant Control"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Electrical Power and Energy System".

Deadline for manuscript submissions: closed (20 July 2020).

Special Issue Editors

Prof. Dr. Demba Diallo
Website
Guest Editor
1. Université Paris Saclay, France, 91400 Orsay, France
2. Shanghai Maritime University, shanghai 200135, China
Interests: electrical drives; incipient fault diagnosis; fault-tolerant control; renewable energy
Special Issues and Collections in MDPI journals
Prof. Dr. Antonio J. Marques Cardoso
Website
Guest Editor
CISE - Electromechatronic Systems Research Centre, Department of Electromechanical Engineering, University of Beira Interior, Calçada Fonte do Lameiro, P - 6201-001 Covilhã, Portugal
Interests: diagnosis and fault tolerance of electrical machines; power electronics and drives
Special Issues and Collections in MDPI journals
Dr. Claude DELPHA
Website
Guest Editor
Laboratoire des Signaux et Systèmes, Université Paris-Saclay, CNRS, CentraleSupélec, 91192 Gif Sur Yvette, France
Interests: data and signal processing; incipient fault diagnosis; data hiding; watermarking; complex systems
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Of all the current issues in industry, safety, reliability and availability are clearly at the forefront. One way to meet the ever-increasing constraints is to implement Condition-Based Maintenance (CBM). As a result, CBM is drawing huge attention from both industry and academia. The implementation of CBM requires efficient health monitoring and fault-tolerance strategies.

Currently, particular attention is paid to incipient fault—meaning the fault level is in the same range as the nuisances. Indeed, incipient fault—if not detected at its earliest stage—may increase gradually and silently and finally leads to catastrophic failures.

Health monitoring includes fault detection, fault isolation and fault estimation. It can be based on modelling (physics-based or data driven), or information processing, to retrieve and analyse the most relevant features, to make the most reliable and accurate decision regarding the health status of the process under study.

Once the fault has been diagnosed and the alarm sent, the fault-tolerant strategy is engaged to mitigate the fault occurrence, in order to avoid any unpredicted and unwanted stoppages. This can be done with the same level of performances, as long as it is sustainable or with degraded performances. The fault-tolerance strategy is mainly based on the application of theoretical tools from control and information processing.

Therefore, this Special Issue has a wide potential audience, including both practitioners and academics working in different areas (energy, transportation, etc…).

We invite them all to contribute to this Special Issue and share their valuable experience.

Prof. Dr. Demba Diallo
Prof. Antonio J. MARQUES CARDOSO
Dr. Claude DELPHA
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 papers will be 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. Energies 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 2000 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

  • incipient fault
  • fault detection and diagnosis
  • fault tolerant control
  • Condition-based Maintenance
  • modelling
  • data, signal and information processing

Published Papers (6 papers)

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Research

Open AccessArticle
Fault-Structure-Based Active Fault Diagnosis: A Geometric Observer Approach
Energies 2020, 13(17), 4475; https://doi.org/10.3390/en13174475 - 31 Aug 2020
Viewed by 333
Abstract
Fault diagnosis techniques can be classified into passive and active types. Passive approaches only utilize the original input and output signals of the system. Because of the small amplitudes, the characteristics of incipient faults are not fully represented in the data of the [...] Read more.
Fault diagnosis techniques can be classified into passive and active types. Passive approaches only utilize the original input and output signals of the system. Because of the small amplitudes, the characteristics of incipient faults are not fully represented in the data of the system, so it is difficult to detect incipient faults by passive fault diagnosis techniques. In contrast, active methods can design auxiliary signals for specific faults and inject them into the system to improve fault diagnosis performance. Therefore, active fault diagnosis techniques are utilized in this article to detect and isolate incipient faults based on the fault structure. A new framework based on observer approach for active fault diagnosis is proposed and the geometric approach based fault diagnosis observer is introduced to active fault diagnosis for the first time. Based on the dynamic equations of residuals, auxiliary signals are designed to enhance the diagnosis performance for incipient faults that have specific structures. In addition, the requirements that auxiliary signals need to meet are discussed. The proposed method can realize the seamless combination of active fault diagnosis and passive fault diagnosis. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed approach, and it is indicated that the proposed method significantly improves the accuracy of the diagnosis for incipient faults. Full article
(This article belongs to the Special Issue Incipient Fault Detection and Diagnosis, Fault-Tolerant Control)
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Open AccessArticle
Fault Diagnosis of a Granulator Operating under Time-Varying Conditions Using Canonical Variate Analysis
Energies 2020, 13(17), 4427; https://doi.org/10.3390/en13174427 - 27 Aug 2020
Cited by 1 | Viewed by 406
Abstract
Granulators play a key role in many pharmaceutical processes because they are involved in the production of tablets and capsule dosage forms. Considering the characteristics of the production processes in which a granulator is involved, proper maintenance of the latter is relevant for [...] Read more.
Granulators play a key role in many pharmaceutical processes because they are involved in the production of tablets and capsule dosage forms. Considering the characteristics of the production processes in which a granulator is involved, proper maintenance of the latter is relevant for plant safety. During the operational phase, there is a high risk of explosion, pollution, and contamination. The nature of this process also requires an in-depth examination of the time-dependence of the process variables. This study proposes the application of canonical variate analysis (CVA) to perform fault detection in a granulation process that operates under time-varying conditions. Beyond this, a different approach to the management of process non-linearities is proposed. The novelty of the study is in the application of CVA in this kind of process, because it is possible to state that the actual literature on the theme shows some limitations of CVA in such processes. The aim was to increase the applicability of CVA in variable contexts, with simple management of non-linearities. The results, considering process data from a pharmaceutical granulator, showed that the proposed approach could detect faults and manage non-linearities, exhibiting future scenarios for more performing and automatic monitoring techniques of time-varying processes. Full article
(This article belongs to the Special Issue Incipient Fault Detection and Diagnosis, Fault-Tolerant Control)
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Open AccessArticle
Estimation of Bearing Fault Severity in Line-Connected and Inverter-Fed Three-Phase Induction Motors
Energies 2020, 13(13), 3481; https://doi.org/10.3390/en13133481 - 06 Jul 2020
Cited by 1 | Viewed by 589
Abstract
This paper addresses a comprehensive evaluation of a bearing fault evolution and its consequent prediction concerning the remaining useful life. The proper prediction of bearing faults in their early stage is a crucial factor for predictive maintenance and mainly for the production management [...] Read more.
This paper addresses a comprehensive evaluation of a bearing fault evolution and its consequent prediction concerning the remaining useful life. The proper prediction of bearing faults in their early stage is a crucial factor for predictive maintenance and mainly for the production management schedule. The detection and estimation of the progressive evolution of a bearing fault are performed by monitoring the amplitude of the current signals at the time domain. Data gathered from line-fed and inverter-fed three-phase induction motors were used to validate the proposed approach. To assess classification accuracy and fault estimation, the models described in this paper are investigated by using Artificial Neural Networks models. The paper also provides process flowcharts and classification tables to present the prognostic models used to estimate the remaining useful life of a defective bearing. Experimental results confirmed the method robustness and provide an accurate diagnosis regardless of the bearing fault stage, motor speed, load level, and type of supply. Full article
(This article belongs to the Special Issue Incipient Fault Detection and Diagnosis, Fault-Tolerant Control)
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Open AccessArticle
A Learning Variable Neighborhood Search Approach for Induction Machines Bearing Failures Detection and Diagnosis
Energies 2020, 13(11), 2953; https://doi.org/10.3390/en13112953 - 09 Jun 2020
Cited by 1 | Viewed by 595
Abstract
This paper proposes a three-phase metaheuristic-based approach for induction machine bearing failure detection and diagnosis. It consists of extracting and processing different failure types features to set up a knowledge base, which contains different failure types. The first phase consists in pre-processing the [...] Read more.
This paper proposes a three-phase metaheuristic-based approach for induction machine bearing failure detection and diagnosis. It consists of extracting and processing different failure types features to set up a knowledge base, which contains different failure types. The first phase consists in pre-processing the measured signals by aggregating them and preparing the data in exploitable formats for the clustering. The second phase ensures the induction machine operating mode diagnosis. A measured signals clustering is performed to build classes where each one represents a health state. A variable neighborhood search (VNS) metaheuristic is designed for data clustering. Moreover, VNS is hybridized with a classical mechanics-inspired optimization (CMO) metaheuristic to balance global exploration and local exploitation during the evolutionary process. The third phase consists of two-step failure detection, setting up a knowledge base containing different failure types, and defining a representative model for each failure type. In the learning step, different class features are extracted and inserted in the knowledge base to be used during the decision step. The proposed metaheuristic-based failure detection diagnosis approach is evaluated using PRONOSTIA and CWR bearing data experimental platforms vibration and temperature measurements. The achieved results clearly demonstrate the failure detection and diagnosis, efficiency, and effectiveness of the proposed metaheuristic approach. Full article
(This article belongs to the Special Issue Incipient Fault Detection and Diagnosis, Fault-Tolerant Control)
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Open AccessArticle
Higher-Order Spectra Analysis-Based Diagnosis Method of Blades Biofouling in a PMSG Driven Tidal Stream Turbine
Energies 2020, 13(11), 2888; https://doi.org/10.3390/en13112888 - 05 Jun 2020
Cited by 1 | Viewed by 501
Abstract
Most electrical machines and drive signals are non-Gaussian and are highly nonlinear in nature. A useful set of techniques to examine such signals relies on higher-order statistics (HOS) spectral representations. They describe statistical dependencies of frequency components that are neglected by traditional spectral [...] Read more.
Most electrical machines and drive signals are non-Gaussian and are highly nonlinear in nature. A useful set of techniques to examine such signals relies on higher-order statistics (HOS) spectral representations. They describe statistical dependencies of frequency components that are neglected by traditional spectral measures, namely the power spectrum (PS). One of the most used HOS is the bispectrum where examining higher-order correlations should provide further details and information about the conditions of electric machines and drives. In this context, the stator currents of electric machines are of particular interest because they are periodic, nonlinear, and cyclostationary. This current is, therefore, well adapted for analysis using bispectrum in the designing of an efficient condition monitoring method for electric machines and drives. This paper is, therefore, proposing a bispectrum-based diagnosis method dealing the with tidal stream turbine (TST) rotor blades biofouling issue, which is a marine environment natural process responsible for turbine rotor unbalance. The proposed bispectrum-based diagnosis method is verified using experimental data provided from a permanent magnet synchronous generator (PMSG)-based TST experiencing biofouling emulated by attachment on the turbine blade. Based on the achieved results, it can be concluded that the proposed diagnosis method has been very successful. Indeed, biofouling imbalance-related frequencies are clearly identified despite marine environmental nuisances (turbulences and waves). Full article
(This article belongs to the Special Issue Incipient Fault Detection and Diagnosis, Fault-Tolerant Control)
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Open AccessArticle
Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor
Energies 2020, 13(6), 1475; https://doi.org/10.3390/en13061475 - 20 Mar 2020
Cited by 5 | Viewed by 943
Abstract
In this paper, the idea of using a convolutional neural network (CNN) for the detection and classification of induction motor stator winding faults is presented. The diagnosis inference of the stator inter-turn short-circuits is based on raw stator current data. It offers the [...] Read more.
In this paper, the idea of using a convolutional neural network (CNN) for the detection and classification of induction motor stator winding faults is presented. The diagnosis inference of the stator inter-turn short-circuits is based on raw stator current data. It offers the possibility of using the diagnostic signal direct processing, which could replace well known analytical methods. Tests were carried out for various levels of stator failures. In order to assess the sensitivity of the applied CNN-based detector to motor operating conditions, the tests were carried out for variable load torques and for different values of supply voltage frequency. Experimental tests were conducted on a specially designed setup with the 3 kW induction motor of special construction, which allowed for the physical modelling of inter-turn short-circuits in each of the three phases of the machine. The on-line tests prove the possibility of using CNN in the real-time diagnostic system with the high accuracy of incipient stator winding fault detection and classification. The impact of the developed CNN structure and training method parameters on the fault diagnosis accuracy has also been tested. Full article
(This article belongs to the Special Issue Incipient Fault Detection and Diagnosis, Fault-Tolerant Control)
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