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Special Issue "Sensors for Fault Diagnosis and Prognostics"

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

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editors

Prof. Dr. Andrew G Starr
Website
Guest Editor
School of Aerospace, Transport and Manufacturing, Building 50, Cranfield University, College Road, Cranfield, MK43 0AL, UK
Interests: maintenance of machine systems; data fusion; fault diagnostics and prognostics
Dr. Muhammad Khan
Website
Guest Editor
School of Aerospace, Transport and Manufacturing, Building 50, Cranfield University, College Road, Cranfield, MK43 0AL, UK
Interests: machine and structural health monitoring; fault diagnostics and prognostics

Special Issue Information

The recent advancements in automation, artificial intelligence, digitization, and data communication have widely influenced the sensing fundamentals and methods for fault diagnostics and prognostics. Noncontact-based sensing methods have made fault diagnostics and prognostics possible even for those assets which are operated around very harsh and hazardous surroundings. The challenges with regard to size and resolution of sensing elements have been addressed comprehensively in the last decade due the evolution of smart materials and mechanisms. Challenges with raw data conversion into useful information have been addressed by state-of-the-art algorithms based on statistical tools, signal processing techniques and data fusion. All these developments starting from sensing elements to reliable prediction of fault in a machine have made things possible which were not a few years ago.

This Special Issue welcomes contributions dealing with sensing methods and approaches for fault diagnosis and prognostics, including sensor materials, sensor data generation and acquisition and techniques to use sensor data for diagnosis and prognosis. Special consideration will be given to papers discussing sensor material modelling and measurement, algorithms for converting raw sensor data and experimental studies on fault monitoring.

Prof. Dr. Andrew G Star
Dr. Muhammad Khan
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. 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 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

  • Diagnosis
  • Prognostics
  • Sensor material
  • Sensor measurement
  • Fault monitoring
  • Intelligent algorithms
  • Raw data analysis
  • Noncontact sensing

Published Papers (3 papers)

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Research

Open AccessArticle
A Novel Approach for Acoustic Signal Processing of a Drum Shearer Based on Improved Variational Mode Decomposition and Cluster Analysis
Sensors 2020, 20(10), 2949; https://doi.org/10.3390/s20102949 - 22 May 2020
Abstract
During operation, the acoustic signal of the drum shearer contains a wealth of information. The monitoring or diagnosis system based on acoustic signal has obvious advantages. However, the signal is challenging to extract and recognize. Therefore, this paper proposes an approach for acoustic [...] Read more.
During operation, the acoustic signal of the drum shearer contains a wealth of information. The monitoring or diagnosis system based on acoustic signal has obvious advantages. However, the signal is challenging to extract and recognize. Therefore, this paper proposes an approach for acoustic signal processing of a shearer based on the parameter optimized variational mode decomposition (VMD) method and a clustering algorithm. First, the particle swarm optimization (PSO) algorithm searched for the best parameter combination of the VMD. According to the results, the approach determined the number of modes and penalty parameters for VMD. Then the improved VMD algorithm decomposed the acoustic signal. It selected the ideal component through the minimum envelope entropy. The PSO was designed to optimize the clustering analysis, and the minimum envelope entropy of the acoustic signal was regarded as the feature for classification. We then use a shearer simulation platform to collect the acoustic signal and use the approach proposed in this paper to process and classify the signal. The experimental results show that the approach proposed can effectively extract the features of the acoustic signal of the shearer. The recognition accuracy of the acoustic signal was high, which has practical application value. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis and Prognostics)
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Open AccessArticle
A Novel Fault Diagnosis Scheme for Rolling Bearing Based on Convex Optimization in Synchroextracting Chirplet Transform
Sensors 2020, 20(10), 2813; https://doi.org/10.3390/s20102813 - 15 May 2020
Abstract
Synchroextracting transform (SET) developed from synchrosqueezing transform (SST) is a novel time-frequency (TF) analysis method. Its concentrated TF spectrum is obtained by applying a synchroextracting operator into TF transformation co-efficients on the TF plane. For this class of post-processing TF analysis methods, the [...] Read more.
Synchroextracting transform (SET) developed from synchrosqueezing transform (SST) is a novel time-frequency (TF) analysis method. Its concentrated TF spectrum is obtained by applying a synchroextracting operator into TF transformation co-efficients on the TF plane. For this class of post-processing TF analysis methods, the main research focuses on the accurate estimation of instantaneous frequency (IF). However, the performance of TF analysis is greatly affected by the strong frequency modulation (FM) signal. In particular, the actual measured mechanical vibration signals always contain strong background noise, which decreases the resolution of TF representation, resulting in an inaccurate ridge extraction. To solve this problem, an improved penalty function based on the convex optimization scheme is firstly introduced for signal denoising. Based on the superiority of the linear chirplet transform (LCT) in dealing with modulated signals, the synchroextracting chirplet transform (SECT) is employed to sharpen the TF representation after the convex optimization denoising operation. To verify the effectiveness of the proposed method, the numerical simulation signals and the measured fault signals of rolling bearing are carried out, respectively. The results demonstrate that the proposed method leads to a better solution in rolling bearing fault feature extraction. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis and Prognostics)
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Open AccessArticle
Fault Diagnosis of Planetary Gearbox Based on Adaptive Order Bispectrum Slice and Fault Characteristics Energy Ratio Analysis
Sensors 2020, 20(8), 2433; https://doi.org/10.3390/s20082433 - 24 Apr 2020
Abstract
The vibration of a planetary gearbox (PG) is complex and mutually modulated, which makes the weak features of incipient fault difficult to detect. To target this problem, a novel method, based on an adaptive order bispectrum slice (AOBS) and the fault characteristics energy [...] Read more.
The vibration of a planetary gearbox (PG) is complex and mutually modulated, which makes the weak features of incipient fault difficult to detect. To target this problem, a novel method, based on an adaptive order bispectrum slice (AOBS) and the fault characteristics energy ratio (FCER), is proposed. The order bispectrum (OB) method has shown its effectiveness in the feature extraction of bearings and fixed-shaft gearboxes. However, the effectiveness of the PG still needs to be explored. The FCER is developed to sum up the fault information, which is scattered by mutual modulation. In this method, the raw vibration signal is firstly converted to that in the angle domain. Secondly, the characteristic slice of AOBS is extracted. Different from the conventional OB method, the AOBS is extracted by searching for a characteristic carrier frequency adaptively in the sensitive range of signal coupling. Finally, the FCER is summed up and calculated from the fault features that were dispersed in the characteristic slice. Experimental data was processed, using both the AOBS-FCER method, and the method that combines order spectrum analysis with sideband energy ratio (OSA-SER), respectively. Results indicated that the new method is effective in incipient fault feature extraction, compared with the methods of OB and OSA-SER. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis and Prognostics)
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