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Fault Diagnosis and Fault-Tolerant Control

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (20 July 2019) | Viewed by 23641

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Special Issue Editors

Prof. Dr. Demba Diallo

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Guest Editor

CNRS, CentraleSupélec, Group of Electrical Engineering of Paris, Université Paris Saclay, 91400 Orsay, France
Interests: electrical drives; incipient fault diagnosis; fault tolerant control; renewable energy
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. David Mba

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Guest Editor

Executive Office, University of the Arts London, 272 High Holborn, London WC1V 7EY, UK
Interests: condition monitoring; machine fault diagnosis; model based prognostics; machine performance prediction
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Prof. Dr. Claude Delpha

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Guest Editor

CNRS, CentraleSupélec, Laboratoire des Signaux et Systèmes, Université Paris Saclay, 91400 Orsay, France
Interests: data and signal processing; incipient fault diagnosis; detection and estimation; data hiding; watermarking; complex systems; statistical learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Among the current issues in industry, safety, reliability, and availability are clearly at the forefront. One way to meet ever-increasing constraints is to implement condition-based maintenance (CBM); accordingly, CBM is attracting significant attention from both industry and academia. The implementation of CBM requires efficient health-monitoring and a fault-tolerance strategy.

Health monitoring includes fault detection, fault isolation, and fault estimation. It is based on modelling (physics-based or data driven) information processing to retrieve and analyse the most relevant features to make the most reliable and accurate decision on 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 fault occurrence in order to avoid any unpredicted and unwanted delays. This can be done with the same level of performances if 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 attracted a wide potential audience from both practitioners and academics working in different areas (energy, transportation, etc…).

We invite all of them to contribute to this Special Issue and share their valuable experience on this topical issue.

Prof. Dr. Demba Diallo
Prof. Dr. David Mba
Dr. Claude Delpha
Guest Editors

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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.

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Keywords

fault diagnosis
fault-tolerant control
condition-based maintenance
modelling
information processing

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Published Papers (6 papers)

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Research

17 pages, 3794 KiB

Open AccessFeature PaperArticle

Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning

by Xiaochuan Li, Faris Elasha, Suliman Shanbr and David Mba

Energies 2019, 12(14), 2705; https://doi.org/10.3390/en12142705 - 15 Jul 2019

Cited by 27 | Viewed by 4905

Abstract

Components of rotating machines, such as shafts, bearings and gears are subject to performance degradation, which if left unattended could lead to failure or breakdown of the entire system. Analyzing condition monitoring data, implementing diagnostic techniques and using machinery prognostic algorithms will bring about accurate estimation of the remaining life and possible failures that may occur. This paper proposes a combination of two supervised machine learning techniques; namely, the regression model and multilayer artificial neural network model, to predict the remaining useful life of rolling element bearings. Root mean square and Kurtosis were analyzed to define the bearing failure stages. The proposed methodology was validated through two case studies involving vibration measurements of an operational wind turbine gearbox and a split cylindrical roller bearing in a test rig. Full article

(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control)

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10 pages, 3643 KiB

Open AccessArticle

Enhanced Threshold Point Calculation Algorithm for Switch Fault Diagnosis in Grid Connected 3-Phase AC–DC PWM Converters

by Geun Wan Koo, DongMyoung Joo and Byoung Kuk Lee

Energies 2019, 12(10), 1979; https://doi.org/10.3390/en12101979 - 23 May 2019

Cited by 3 | Viewed by 3466

Abstract

The resilience of systems with alternating current (AC)–direct current (DC) converters has been investigated with the aim of improving switch fault diagnosis. To satisfy this aim, this paper proposes a switch fault diagnosis algorithm for three-phase AC–DC converters. The proposed algorithm operates using the phase current instead of the average current to reduce the calculation time required for fault switch detection. Moreover, a threshold point calculation method is derived using a theoretical analysis, which was lacking in previous research. Using the calculated threshold point, a switch fault diagnosis algorithm is obtained to detect faults independent of the load condition. Using the proposed algorithm, switch faults can be detected within 4 ms, which is the recommended value for uninterruptible power supply (UPS). The theoretical analysis, the operating principle, and the experimental results on a 3-kW grid-tied AC–DC converter test-bed are presented herein, which verify the performance of the proposed algorithm. Full article

(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control)

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17 pages, 2067 KiB

Open AccessArticle

A Network Method for Identifying the Root Cause of High-Speed Rail Faults Based on Text Data

by Liu Yang, Keping Li, Dan Zhao, Shuang Gu and Dongyang Yan

Energies 2019, 12(10), 1908; https://doi.org/10.3390/en12101908 - 18 May 2019

Cited by 11 | Viewed by 3179

Abstract

Root cause identification is an important task in providing prompt assistance for diagnosis, security monitoring and guidance for specific routine maintenance measures in the field of railway transportation. However, most of the methods addressing rail faults are based on state detection, which involves structured data. Manual cause identification from railway equipment maintenance and management text records is undoubtedly a time-consuming and laborious task. To quickly obtain the root cause text from unstructured data, this paper proposes an approach for root cause factor identification by using a root cause identification-new word sentence (RCI-NWS) keyword extraction method. The experimental results demonstrate that the extraction of railway fault text data can be performed using the keyword extraction method and the highest values are obtained using RCI-NWS. Full article

(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control)

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20 pages, 11582 KiB

Open AccessArticle

Three-Level NPC Inverter Incipient Fault Detection and Classification using Output Current Statistical Analysis

by Mehdi Baghli, Claude Delpha, Demba Diallo, Abdelhamid Hallouche, David Mba and Tianzhen Wang

Energies 2019, 12(7), 1372; https://doi.org/10.3390/en12071372 - 9 Apr 2019

Cited by 16 | Viewed by 3748

Abstract

This paper deals with open switch Fault Detection and Diagnosis (FDD) in three-level Neutral Point Clamped (NPC) inverter for electrical drives. The approach is based on the already available phase current time series measurements for different operating conditions (motor speed, load, and environment noise). Both fault detection and classification are studied and the efficiency performances of the proposed selected features are shown. For the fault detection, we focus on the first four statistical moments and the extracted features and then the Cumulative Sum (CUSUM) algorithm as the feature analysis technique to improve the performances. For the classification study, we propose to couple the knowledge on the faulty system brought by the statistical moments and the Kullback-Leibler divergence particularly suitable for the detection of incipient changes. The Principal Component Analysis (PCA) is then used to perform the classification. A 2D framework is obtained, which allows the faults to be classified efficiently within the considered operating conditions for all the selected fault durations. Full article

(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control)

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14 pages, 4967 KiB

Open AccessFeature PaperArticle

Fault Detection of a Spherical Tank Using a Genetic Algorithm-Based Hybrid Feature Pool and k-Nearest Neighbor Algorithm

by Md Junayed Hasan and Jong-Myon Kim

Energies 2019, 12(6), 991; https://doi.org/10.3390/en12060991 - 14 Mar 2019

Cited by 35 | Viewed by 3896

Abstract

Fault detection in metallic structures requires a detailed and discriminative feature pool creation mechanism to develop an effective condition monitoring system. Traditional fault detection methods incorporate handcrafted features either from the time, frequency or time-frequency domains. To explore the salient information provided by the acoustic emission (AE) signals, a hybrid of feature pool creation and an optimal features subset selection mechanism is proposed for crack detection in a spherical tank. The optimal hybrid feature pool creation process is composed of two major parts: (1) extraction of statistical features from time and frequency domains, as well as extraction of traditional features associated with the AE signals; and (2) genetic algorithm (GA)-based optimal features subset selection. The optimal features subset is then provided to the k-nearest neighbor (k-NN) classifier to distinguish between normal (NC) and crack conditions (CC). Experimental results show that the proposed approach yields an average 99.8% accuracy for heath state classification. To validate the effectiveness of the proposed approach, it is compared to conventional non-linear dimensionality reduction techniques, as well as those without feature selection schemes. Experimental results show that the proposed approach outperforms conventional non-linear dimensionality reduction techniques, achieving at least 2.55% higher classification accuracy. Full article

(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control)

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16 pages, 9670 KiB

Open AccessArticle

Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults

by Xiaochuan Li, David Mba, Demba Diallo and Claude Delpha

Energies 2019, 12(4), 726; https://doi.org/10.3390/en12040726 - 22 Feb 2019

Cited by 9 | Viewed by 3166

Abstract

This study puts forward a novel diagnostic approach based on canonical variate residuals (CVR) to implement incipient fault diagnosis for dynamic process monitoring. The conventional canonical variate analysis (CVA) fault detection approach is extended to form a new monitoring index based on Hotelling’s [...] Read more.

T^{2}

Q

and a CVR-based monitoring index,

T_{d}

. A CVR-based contribution plot approach is also proposed based on

Q

and

T_{d}

statistics. Two performance metrics: (1) false alarm rate and (2) missed detection rate are used to assess the effectiveness of the proposed approach. The CVR diagnostic approach was validated on incipient faults in a continuous stirred tank reactor (CSTR) system and an operational centrifugal compressor. Full article

(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control)

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