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Fault Diagnosis and Vibration Signal Processing in Rotor Systems
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Fault Diagnosis and Vibration Signal Processing in Rotor Systems

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

Deadline for manuscript submissions: 25 September 2024 | Viewed by 8854

Special Issue Editors

Dr. Yongfeng Yang

E-Mail Website
Guest Editor
Institute of Vibration Engineering, Northwestern Polytechnical University, Xi’an 710072, China
Interests: fault diagnosis; vibration signal processing; rotor systems; nonlinear dynamics
Prof. Dr. Jin Zhou

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Interests: magnetic bearings; vibration control; rotor dynamics; mechatronics; rotating machinery
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rafael Morales

E-Mail Website
Guest Editor
Electrénica Automática y Comunicaciones, Universidad de Castilla-La Mancha, Albacete, Spain
Interests: offshore sustainable renewable energy systems; maintenance maneuvers; robotics; control; automation and sensor integration applied to offshore renewable energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Alexandre Presas

E-Mail Website
Guest Editor
Center for Industrial Diagnosis and Fluid Dynamics, Polytechnic University of Catalonia, 08028 Barcelona, Spain
Interests: fatigue failures; erosion and wear; signal processing; rotor systems; modal analysis; resonance problems; vibrations in hydraulic machinery
Special Issues, Collections and Topics in MDPI journals
Dr. Saleh Mobayen

E-Mail Website
Guest Editor
Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 640301, Taiwan
Interests: fault diagnosis; robust control; variable structure control; robotics; wind turbines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rotor systems are kernel components of rotating machinery and are applied in most industrial fields, such as aero-engines, gas turbines, steam turbines, generators, electric motors, and mechanical manufacturing. Meanwhile, some renewable energy systems contain a lot of rotating subsystems, such as synchronous generators, wind power systems, power-driven machines, and bearing systems. With the performance improvements of the rotating machinery, the complications of structural design are ever increasing. Vibration is integral to these rotating systems, especially in high-speed rotating systems, and the almost all the faults may show special characteristic compared with regular work. The aim of this Special Issue is to compile original research and review articles on the topics of fault diagnosis and related vibration signal processing. Submissions about the current state of dynamic analysis, as well as advances in, structural optimization of, and vibration control of nonlinear rotor systems are welcome. Research on theories, simulations, experiments, and engineering applications are all welcome. We hope that this Special Issue will create an academic discussion in the field.

Dr. Yongfeng Yang
Prof. Dr. Jin Zhou
Prof. Dr. Rafael Morales
Dr. Alexandre Presas
Dr. Saleh Mobayen
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

  • vbiration of wind power system with faults 
  • dynamic modeling of rotor systems 
  • improvement of theoretical methods rotor systems 
  • simulation methods for rotor systems 
  • nonlinear vibration response characteristics of rotor systems with faults 
  • vibration and stability control of rotor systems 
  • active, semi-active, and passive control techniques applied in rotor systems 
  • applications of intelligent controls, adaptive controls, nonlinear controls, and linear controls in rotor systems 
  • intelligent sensing and signal analysis for rotor systems 
  • intellignet fault diagnosis for rotor systems 
  • rotor systems with magnetic bearings

Published Papers (7 papers)

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Research

14 pages, 3713 KiB  
Article
An Intelligent Ball Bearing Fault Diagnosis System Using Enhanced Rotational Characteristics on Spectrogram
by Gyujin Seong and Dongwan Kim
Sensors 2024, 24(3), 776; https://0-doi-org.brum.beds.ac.uk/10.3390/s24030776 - 25 Jan 2024
Viewed by 838
Abstract
Faults in the ball bearing are a major cause of failure in rotating machinery where ball bearings are used. Therefore, there is a growing demand for ball bearing fault diagnosis to prevent failures in rotating machinery. Although studies on the fault diagnosis of [...] Read more.
Faults in the ball bearing are a major cause of failure in rotating machinery where ball bearings are used. Therefore, there is a growing demand for ball bearing fault diagnosis to prevent failures in rotating machinery. Although studies on the fault diagnosis of bearing have been conducted using temperature measurements and sound monitoring, these methods have limitations, because they are affected by external noise. Therefore, many researchers have studied vibration monitoring for bearing fault diagnosis. Among these, mel-frequency cepstral coefficients (MFCCs) and 2D convolutional neural networks (CNNs) have attracted significant attention in vibration monitoring schemes. However, the MFCC in existing studies requires a high sampling rate and an expansive frequency band utilization. In addition, 2D CNNs are highly complex. In this study, a rotational characteristic emphasis (RCE) spectrogram process and an optimized CNN were proposed to solve these problems. The RCE spectrogram process analyzes a narrow frequency band and produces low-resolution images. The optimized CNN was designed with a shallow network structure. The experimental results showed an accuracy of 0.9974 for the proposed system. The optimized CNN model has parameters of 5.81 KB and FLOPs of 1.53×106. We demonstrate that the proposed ball bearing fault diagnosis system can achieve high accuracy with low complexity. Thus, we propose a ball bearing fault diagnosis scheme that is applicable to a low sampling rate and changing rotation frequency. Full article
(This article belongs to the Special Issue Fault Diagnosis and Vibration Signal Processing in Rotor Systems)
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40 pages, 29073 KiB  
Article
Scale-Fractal Detrended Fluctuation Analysis for Fault Diagnosis of a Centrifugal Pump and a Reciprocating Compressor
by Ruben Medina, René-Vinicio Sánchez, Diego Cabrera, Mariela Cerrada, Edgar Estupiñan, Wengang Ao and Rafael E. Vásquez
Sensors 2024, 24(2), 461; https://0-doi-org.brum.beds.ac.uk/10.3390/s24020461 - 11 Jan 2024
Viewed by 889
Abstract
Reciprocating compressors and centrifugal pumps are rotating machines used in industry, where fault detection is crucial for avoiding unnecessary and costly downtime. A novel method for fault classification in reciprocating compressors and multi-stage centrifugal pumps is proposed. In the feature extraction stage, raw [...] Read more.
Reciprocating compressors and centrifugal pumps are rotating machines used in industry, where fault detection is crucial for avoiding unnecessary and costly downtime. A novel method for fault classification in reciprocating compressors and multi-stage centrifugal pumps is proposed. In the feature extraction stage, raw vibration signals are processed using multi-fractal detrended fluctuation analysis (MFDFA) to extract features indicative of different types of faults. Such MFDFA features enable the training of machine learning models for classifying faults. Several classical machine learning models and a deep learning model corresponding to the convolutional neural network (CNN) are compared with respect to their classification accuracy. The cross-validation results show that all models are highly accurate for classifying the 13 types of faults in the centrifugal pump, the 17 valve faults, and the 13 multi-faults in the reciprocating compressor. The random forest subspace discriminant (RFSD) and the CNN model achieved the best results using MFDFA features calculated with quadratic approximations. The proposed method is a promising approach for fault classification in reciprocating compressors and multi-stage centrifugal pumps. Full article
(This article belongs to the Special Issue Fault Diagnosis and Vibration Signal Processing in Rotor Systems)
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15 pages, 3710 KiB  
Article
Physics-Informed Neural Networks for the Condition Monitoring of Rotating Shafts
by Marc Parziale, Luca Lomazzi, Marco Giglio and Francesco Cadini
Sensors 2024, 24(1), 207; https://0-doi-org.brum.beds.ac.uk/10.3390/s24010207 - 29 Dec 2023
Viewed by 1300
Abstract
Condition monitoring of rotating shafts is essential for ensuring the reliability and optimal performance of machinery in diverse industries. In this context, as industrial systems become increasingly complex, the need for efficient data processing techniques is paramount. Deep learning has emerged as a [...] Read more.
Condition monitoring of rotating shafts is essential for ensuring the reliability and optimal performance of machinery in diverse industries. In this context, as industrial systems become increasingly complex, the need for efficient data processing techniques is paramount. Deep learning has emerged as a dominant approach due to its capacity to capture intricate data patterns and relationships. However, a prevalent challenge lies in the black-box nature of many deep learning algorithms, which often operate without adhering to the underlying physical characteristics intrinsic to the studied phenomena. To address this limitation and enhance the fusion of data-driven methodologies with the fundamental physics of the system under study, this paper leverages physics-informed neural networks (PINNs). Specifically, a simple but realistic numerical case study of an extended Jeffcott rotor model, encompassing damping effects and anisotropic supports for a more comprehensive modelling, is considered. PINNs are used for the estimation of five parameters that characterize the health state of the system. These parameters encompass the radial and angular position of the static unbalance due to the disk installed on the shaft, the stiffness along the principal axes of elasticity, and the non-rotating damping coefficient. The estimation is conducted solely by exploiting the displacement signals from the centre of the disk and, to showcase the efficacy and precision provided by this novel methodology, various scenarios involving different constant rotational speeds are examined. Additionally, the impact of noisy input data is also taken into account within the analysis and the performance is compared to that of traditional optimization algorithms used for parameters estimation. Full article
(This article belongs to the Special Issue Fault Diagnosis and Vibration Signal Processing in Rotor Systems)
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15 pages, 9786 KiB  
Article
Research on Circuit Breaker Operating Mechanism Fault Diagnosis Method Combining Global-Local Feature Extraction and KELM
by Qinzhe Liu, Xiaolong Wang, Zhaojing Guo, Jian Li, Wei Xu, Xiaowen Dai, Chenlei Liu and Tong Zhao
Sensors 2024, 24(1), 124; https://0-doi-org.brum.beds.ac.uk/10.3390/s24010124 - 26 Dec 2023
Viewed by 749
Abstract
In response to the lack of generality in feature extraction using modal decomposition methods and the susceptibility of diagnostic performance to parameter selection in traditional mechanical fault diagnosis of high-voltage circuit breaker operating mechanisms, this paper proposes a Global-Local feature extraction method based [...] Read more.
In response to the lack of generality in feature extraction using modal decomposition methods and the susceptibility of diagnostic performance to parameter selection in traditional mechanical fault diagnosis of high-voltage circuit breaker operating mechanisms, this paper proposes a Global-Local feature extraction method based on Generalized S-Transform (S-Translate) combined with Gray Level Co-Occurrence Matrix (GLCM) and complemented by Maximum Relevance and Minimum Redundancy (mRMR) feature selection. The GL (Global-Local)-mRMR-KELM fault diagnosis model is proposed, which employs the Kernel Extreme Learning Machine (KELM). In this model, the original time-frequency domain features and the time-frequency features of the Generalized S-Transform matrix of vibration signals under different states of the circuit breaker are first extracted as global features. Then, the GLCM is obtained to extract texture features as local features. Finally, the mRMR and KELM are comprehensively applied to perform feature selection and classification on the dataset, thereby accomplishing the fault diagnosis of the circuit breaker’s operating mechanism. In this study, the 72.5 kV SF6 circuit breaker operating mechanism is taken as the research object, and three types of mechanical faults are simulated to obtain a vibration signal. Experimental results verify the effectiveness of the proposed GL-mRMR-KELM model, achieving a diagnostic accuracy of 96%. This research provides a feasible approach for the fault diagnosis of circuit breaker operating mechanisms. Full article
(This article belongs to the Special Issue Fault Diagnosis and Vibration Signal Processing in Rotor Systems)
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15 pages, 10213 KiB  
Article
Digital Twin of a Gear Root Crack Prognosis
by Omri Matania, Eric Bechhoefer and Jacob Bortman
Sensors 2023, 23(24), 9883; https://0-doi-org.brum.beds.ac.uk/10.3390/s23249883 - 17 Dec 2023
Viewed by 883
Abstract
Digital twins play a significant role in Industry 4.0, offering the potential to revolutionize machinery maintenance. In this paper, we introduce a new digital twin designed to address the open problem of predicting gear root crack propagation. This digital twin uses signal processing [...] Read more.
Digital twins play a significant role in Industry 4.0, offering the potential to revolutionize machinery maintenance. In this paper, we introduce a new digital twin designed to address the open problem of predicting gear root crack propagation. This digital twin uses signal processing and model fitting to continuously monitor the condition of the root crack and successfully estimate the remaining time until immediate maintenance is required for the physical asset. The functionality of this new digital twin is demonstrated through the experimental data obtained from a planetary gear, where comparisons are made between the actual and estimated severity of the fault, as well as the remaining time until maintenance. It is shown that the digital twin addresses the open problem of predicting gear root crack propagation. Full article
(This article belongs to the Special Issue Fault Diagnosis and Vibration Signal Processing in Rotor Systems)
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16 pages, 4445 KiB  
Article
Rolling Bearing Composite Fault Diagnosis Method Based on Enhanced Harmonic Vector Analysis
by Jiantao Lu, Qitao Yin and Shunming Li
Sensors 2023, 23(11), 5115; https://0-doi-org.brum.beds.ac.uk/10.3390/s23115115 - 27 May 2023
Cited by 1 | Viewed by 888
Abstract
Composite fault diagnosis of rolling bearings is very challenging work, especially when the characteristic frequency ranges of different fault types overlap. To solve this problem, an enhanced harmonic vector analysis (EHVA) method was proposed. Firstly, the wavelet threshold (WT) denoising method is used [...] Read more.
Composite fault diagnosis of rolling bearings is very challenging work, especially when the characteristic frequency ranges of different fault types overlap. To solve this problem, an enhanced harmonic vector analysis (EHVA) method was proposed. Firstly, the wavelet threshold (WT) denoising method is used to denoise the collected vibration signals to reduce the influence of noise. Next, harmonic vector analysis (HVA) is used to remove the convolution effect of the signal transmission path, and blind separation of fault signals is carried out. The cepstrum threshold is used in HVA to enhance the harmonic structure of the signal, and a Wiener-like mask will be constructed to make the separated signals more independent in each iteration. Then, the backward projection technique is used to align the frequency scale of the separated signals, and each fault signal can be obtained from composite fault diagnosis signals. Finally, to make the fault characteristics more prominent, a kurtogram was used to find the resonant frequency band of the separated signals by calculating its spectral kurtosis. Semi-physical simulation experiments are conducted using the rolling bearing fault experiment data to verify the effectiveness of the proposed method. The results show that the proposed method, EHVA, can effectively extract the composite faults of rolling bearings. Compared to fast independent component analysis (FICA) and traditional HVA, EHVA improves separation accuracy, enhances fault characteristics, and has higher accuracy and efficiency compared to fast multichannel blind deconvolution (FMBD). Full article
(This article belongs to the Special Issue Fault Diagnosis and Vibration Signal Processing in Rotor Systems)
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21 pages, 1129 KiB  
Article
Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model
by Andressa Borré, Laio Oriel Seman, Eduardo Camponogara, Stefano Frizzo Stefenon, Viviana Cocco Mariani and Leandro dos Santos Coelho
Sensors 2023, 23(9), 4512; https://0-doi-org.brum.beds.ac.uk/10.3390/s23094512 - 5 May 2023
Cited by 22 | Viewed by 2537
Abstract
The predictive maintenance of electrical machines is a critical issue for companies, as it can greatly reduce maintenance costs, increase efficiency, and minimize downtime. In this paper, the issue of predicting electrical machine failures by predicting possible anomalies in the data is addressed [...] Read more.
The predictive maintenance of electrical machines is a critical issue for companies, as it can greatly reduce maintenance costs, increase efficiency, and minimize downtime. In this paper, the issue of predicting electrical machine failures by predicting possible anomalies in the data is addressed through time series analysis. The time series data are from a sensor attached to an electrical machine (motor) measuring vibration variations in three axes: X (axial), Y (radial), and Z (radial X). The dataset is used to train a hybrid convolutional neural network with long short-term memory (CNN-LSTM) architecture. By employing quantile regression at the network output, the proposed approach aims to manage the uncertainties present in the data. The application of the hybrid CNN-LSTM attention-based model, combined with the use of quantile regression to capture uncertainties, yielded superior results compared to traditional reference models. These results can benefit companies by optimizing their maintenance schedules and improving the overall performance of their electric machines. Full article
(This article belongs to the Special Issue Fault Diagnosis and Vibration Signal Processing in Rotor Systems)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Off-design Operation and Cavitation Detection in Centrifugal Pumps Using Vibration and Motor Stator Current Analyses
Authors: Yuejiang Han, Jiamin Zou, Alexandre Presas, Yin Luo, Jianping Yuan
Affiliation: (1): Yuejiang Han (Research Center of Fluid Machinery Engineering and Technology, Jiangsu University) (2): Jiamin Zou ( Centre for Industrial Diagnostics and Fluid Dynamics, UPC ) (3): Alexandre Presas (Centre for Industrial Diagnostics and Fluid Dynamics, UPC)* (corresponding) (4): Yin Luo (Research Center of Fluid Machinery Engineering and Technology, Jiangsu University) (5): Jianping Yuan (Research Center of Fluid Machinery Engineering and Technology, Jiangsu University)
Abstract: Diagnosis of centrifugal pumps is crucial to ensure their reliable operation. This paper investigates two commonly encountered problems in centrifugal pumps: off-design operation and cavitation. A centrifugal pump was tested under off-design conditions and various levels of cavitation. Vibration and stator current signals were sampled simultaneously under each state, and both are evaluated for their effectiveness in operation diagnosis. Signal processing methods, including wavelet threshold function, variational mode decomposition (VMD), Park vector modulus transformation, and marginal spectrum are introduced for feature extraction. Seven families of machine learning-based classification algorithms are evaluated for their performance when used for off-design and cavitation identification. The obtained results, using both types of signals, prove the effectiveness of both approaches and the advantages of combining them in achieving the most reliable operation diagnosis results for centrifugal pumps.

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