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Advanced Sensing for Mechanical Vibration and Fault Diagnosis
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Advanced Sensing for Mechanical Vibration and Fault Diagnosis

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

Deadline for manuscript submissions: closed (10 March 2024) | Viewed by 16915

Special Issue Editors

Prof. Dr. Jinrui Wang

E-Mail Website
Guest Editor
College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Interests: fault diagnosis; mechanical engineering; deep learning; feature extraction; condition monitoring
Dr. Zongzhen Zhang

E-Mail Website
Guest Editor
College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Interests: fault diagnosis; mechanical engineering; signal processing; eature extraction; condition monitoring
Dr. Xingkai Yang

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
Interests: condition monitoring and fault diagnosis; gearbox dynamics and diagnostics; gear tribology; signal processing
Dr. Ke Zhao

E-Mail Website
Guest Editor
School of Construction Machinery, Chang’an University, Xi’an 710064, China
Interests: intelligent fault diagnosis; remaining useful life prediction; deep learning; transfer learning

Special Issue Information

Dear Colleagues,

With the arrival of modern manufacturing systems, machines are becoming more automatic and efficient, which require them to meet the demands of higher reliability, better quality and increased availability. As a result, machine fault diagnosis systems have drawn extensive attention, and advanced sensing techniques are urgently needed to collect useful monitoring data. Therefore, more advanced and intelligent fault diagnosis methods and measurement techniques still require further research.

This Special Issue aims to provide a platform to present high-quality original research on the latest developments of sensing and measurement techniques in machine vibration and fault diagnosis. This Special Issue encourages submissions that cover, but are not limited to, the following topics:

  • Advanced sensing techniques for machine fault diagnosis.
  • Machine learning technologies in sensing systems.
  • Mechanical fault diagnosis-based vibration signal processing.
  • Advanced sensing and monitoring techniques under variable working conditions.
  • Transfer learning-based mechanical fault diagnosis and prognosis.
  • Sensor fusion techniques with multi-modal data.

Prof. Dr. Jinrui Wang
Dr. Zongzhen Zhang
Dr. Xingkai Yang
Dr. Ke Zhao
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.

Published Papers (15 papers)

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Research

16 pages, 4115 KiB  
Article
Lightweight Knowledge Distillation-Based Transfer Learning Framework for Rolling Bearing Fault Diagnosis
by Ruijia Lu, Shuzhi Liu, Zisu Gong, Chengcheng Xu, Zonghe Ma, Yiqi Zhong and Baojian Li
Sensors 2024, 24(6), 1758; https://doi.org/10.3390/s24061758 - 08 Mar 2024
Viewed by 496
Abstract
Compared to fault diagnosis across operating conditions, the differences in data distribution between devices are more pronounced and better aligned with practical application needs. However, current research on transfer learning inadequately addresses fault diagnosis issues across devices. To better balance the relationship between [...] Read more.
Compared to fault diagnosis across operating conditions, the differences in data distribution between devices are more pronounced and better aligned with practical application needs. However, current research on transfer learning inadequately addresses fault diagnosis issues across devices. To better balance the relationship between computational resources and diagnostic accuracy, a knowledge distillation-based lightweight transfer learning framework for rolling bearing diagnosis is proposed in this study. Specifically, a deep teacher–student model based on variable-scale residual networks is constructed to learn domain-invariant features relevant to fault classification within both the source and target domain data. Subsequently, a knowledge distillation framework incorporating a temperature factor is established to transfer fault features learned by the large teacher model in the source domain to the smaller student model, thereby reducing computational and parameter overhead. Finally, a multi-kernel domain adaptation method is employed to capture the feature probability distribution distance of fault characteristics between the source and target domains in Reproducing Kernel Hilbert Space (RKHS), and domain-invariant features are learned by minimizing the distribution distance between them. The effectiveness and applicability of the proposed method in situations of incomplete data across device types were validated through two engineering cases, spanning device models and transitioning from laboratory equipment to real-world operational devices. Full article
(This article belongs to the Special Issue Advanced Sensing for Mechanical Vibration and Fault Diagnosis)
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20 pages, 13275 KiB  
Article
The Strength of Rail Vehicles Transported by a Ferry Considering the Influence of Sea Waves on Its Hull
by Alyona Lovska, Juraj Gerlici, Ján Dižo and Vadym Ishchuk
Sensors 2024, 24(1), 183; https://doi.org/10.3390/s24010183 - 28 Dec 2023
Viewed by 740
Abstract
The article presents the results of a determination of the load attributed to rail vehicles transported by a ferry, considering the influence of sea waves on its hull. A mathematic model describing the displacements of a train ferry, which transported rail vehicles on [...] Read more.
The article presents the results of a determination of the load attributed to rail vehicles transported by a ferry, considering the influence of sea waves on its hull. A mathematic model describing the displacements of a train ferry, which transported rail vehicles on its decks during rolling oscillations, was created. Calculated accelerations were used to identify the load of components from a dynamics point of view and they were subsequently applied as an input to the analysis of the strength of the open wagon main-bearing structure in a standard scheme of interaction with a train ferry deck. The calculated maximal equivalent stresses in the structure of the fastening units exceeded the valid permissible values. To confirm the theoretical results, experimental studies focused on the strength analysis of the open wagon placed on the railway ferry deck, which was performed in real operational conditions. Electrical voltage sensors were used to determine stress distribution in the areas where the body was attached to the deck. In this case, sensors of the strain gauges, i.e., tensiometers, were used. The base of 25 mm is a dimensional parameter and the resistance, 124 Ohms, is the tensiometer parameter. Verification has been performed and, based on the obtained experimental results, it has been established that the hypothesis’ adequacy is not rejected. The authors developed some measures for adaption of the lashing devices for rail cars on train ferries, which can ensure their safe transportation by sea. The strength calculation demonstrated that, in the new scheme of securing the transported railway vehicles on the railway train ferry, the stresses in its structure do not exceed the permissible values. The article also includes information about the results of the strength calculation of a container placed on a roll trailer transported by a train ferry. This research will contribute to the development of measures regarding the safety of railway vehicle transportation by sea ferry and better efficiency of train ferry transportation. Full article
(This article belongs to the Special Issue Advanced Sensing for Mechanical Vibration and Fault Diagnosis)
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15 pages, 7292 KiB  
Article
Adaptive Convolution Sparse Filtering Method for the Fault Diagnosis of an Engine Timing Gearbox
by Shigong Fan, Yixi Cai, Zongzhen Zhang, Jinrui Wang, Yunxi Shi and Xiaohua Li
Sensors 2024, 24(1), 169; https://doi.org/10.3390/s24010169 - 28 Dec 2023
Cited by 1 | Viewed by 598
Abstract
Due to the superior robustness of outlier signals and the unique advantage of not relying on a priori knowledge, Convolution Sparse Filtering (CSF) is drawing more and more attention. However, the excellent properties of CSF is limited by its inappropriate selection of the [...] Read more.
Due to the superior robustness of outlier signals and the unique advantage of not relying on a priori knowledge, Convolution Sparse Filtering (CSF) is drawing more and more attention. However, the excellent properties of CSF is limited by its inappropriate selection of the number and length of its filters. Therefore, the Adaptive Convolution Sparse Filtering (ACSF) method is proposed in this paper to implement an end-to-end health monitoring and fault diagnostic model. Firstly, a novel metric entropy–time function (He−T) is proposed to measure the accuracy and efficiency of signals filtered by the CSF. Then, the filtered signal with the minimum He−T is detected with particle swarm optimization. Finally, the failure mode is diagnosed according to the envelope spectrum of the signal with minimum He−T. The effectiveness and efficiency of the ACSF is demonstrated through the experiment. The results indicate the ACSF can extract the failure characteristic of the gearbox. Full article
(This article belongs to the Special Issue Advanced Sensing for Mechanical Vibration and Fault Diagnosis)
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21 pages, 15407 KiB  
Article
Iterative-Based Impact Force Identification on a Bridge Concrete Deck
by Maria Rashidi, Shabnam Tashakori, Hamed Kalhori, Mohammad Bahmanpour and Bing Li
Sensors 2023, 23(22), 9257; https://doi.org/10.3390/s23229257 - 18 Nov 2023
Viewed by 846
Abstract
Steel-reinforced concrete decks are prominently utilized in various civil structures such as bridges and railways, where they are susceptible to unforeseen impact forces during their operational lifespan. The precise identification of the impact events holds a pivotal role in the robust health monitoring [...] Read more.
Steel-reinforced concrete decks are prominently utilized in various civil structures such as bridges and railways, where they are susceptible to unforeseen impact forces during their operational lifespan. The precise identification of the impact events holds a pivotal role in the robust health monitoring of these structures. However, direct measurement is not usually possible due to structural limitations that restrict arbitrary sensor placement. To address this challenge, inverse identification emerges as a plausible solution, albeit afflicted by the issue of ill-posedness. In tackling such ill-conditioned challenges, the iterative regularization technique known as the Landweber method proves valuable. This technique leads to a more reliable and accurate solution compared with traditional direct regularization methods and it is, additionally, more suitable for large-scale problems due to the alleviated computation burden. This paper employs the Landweber method to perform a comprehensive impact force identification encompassing impact localization and impact time–history reconstruction. The incorporation of a low-pass filter within the Landweber-based identification procedure is proposed to augment the reconstruction process. Moreover, a standardized reconstruction error metric is presented, offering a more effective means of accuracy assessment. A detailed discussion on sensor placement and the optimal number of regularization iterations is presented. To automatedly localize the impact force, a Gaussian profile is proposed, against which reconstructed impact forces are compared. The efficacy of the proposed techniques is illustrated by utilizing the experimental data acquired from a bridge concrete deck reinforced with a steel beam. Full article
(This article belongs to the Special Issue Advanced Sensing for Mechanical Vibration and Fault Diagnosis)
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27 pages, 33386 KiB  
Article
A Novel Hybrid Technique Combining Improved Cepstrum Pre-Whitening and High-Pass Filtering for Effective Bearing Fault Diagnosis Using Vibration Data
by Amirmasoud Kiakojouri, Zudi Lu, Patrick Mirring, Honor Powrie and Ling Wang
Sensors 2023, 23(22), 9048; https://doi.org/10.3390/s23229048 - 08 Nov 2023
Cited by 2 | Viewed by 1286
Abstract
Rolling element bearings (REBs) are an essential part of rotating machinery. A localised defect in a REB typically results in periodic impulses in vibration signals at bearing characteristic frequencies (BCFs), and these are widely used for bearing fault detection and diagnosis. One of [...] Read more.
Rolling element bearings (REBs) are an essential part of rotating machinery. A localised defect in a REB typically results in periodic impulses in vibration signals at bearing characteristic frequencies (BCFs), and these are widely used for bearing fault detection and diagnosis. One of the most powerful methods for BCF detection in noisy signals is envelope analysis. However, the selection of an effective band-pass filtering region presents significant challenges in moving towards automated bearing fault diagnosis due to the variable nature of the resonant frequencies present in bearing systems and rotating machinery. Cepstrum Pre-Whitening (CPW) is a technique that can effectively eliminate discrete frequency components in the signal whilst detecting the impulsive features related to the bearing defect(s). Nevertheless, CPW is ineffective for detecting incipient bearing defects with weak signatures. In this study, a novel hybrid method based on an improved CPW (ICPW) and high-pass filtering (ICPW-HPF) is developed that shows improved detection of BCFs under a wide range of conditions when compared with existing BCF detection methods, such as Fast Kurtogram (FK). Combined with machine learning techniques, this novel hybrid method provides the capability for automated bearing defect detection and diagnosis without the need for manual selection of the resonant frequencies. The results from this novel hybrid method are compared with a number of established BCF detection methods, including Fast Kurtogram (FK), on vibration signals collected from the project I2BS (An EU Clean Sky 2 project ‘Integrated Intelligent Bearing Systems’ collaboration between Schaeffler Technologies and the University of Southampton. Safran Aero Engines was the topic manager for this project) and those from three databases available in the public domain—Case Western Reserve University (CWRU), Intelligent Maintenance Systems (IMS) datasets, and Safran jet engine data—all of which have been widely used in studies of this kind. By calculating the Signal-to-Noise Ratio (SNR) of each case, the new method is shown to be effective for a much lower SNR (with an average of 30.21) compared with that achieved using the FK method (average of 14.4) and thus is much more effective in detecting incipient bearing faults. The results also show that it is effective in detecting a combination of several bearing faults that occur simultaneously under a wide range of bearing configurations and test conditions and without the requirement of further human intervention such as extra screening or manual selection of filters. Full article
(This article belongs to the Special Issue Advanced Sensing for Mechanical Vibration and Fault Diagnosis)
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16 pages, 22457 KiB  
Article
Fault Diagnosis of Medium Voltage Circuit Breakers Based on Vibration Signal Envelope Analysis
by Yongbin Wu, Jianzhong Zhang, Zhengxi Yuan and Hao Chen
Sensors 2023, 23(19), 8331; https://doi.org/10.3390/s23198331 - 09 Oct 2023
Viewed by 860
Abstract
In modern power systems or new energy power stations, the medium voltage circuit breakers (MVCBs) are becoming more crucial and the operation reliability of the MVCBs could be greatly improved by online monitoring technology. The purpose of this research is to put forward [...] Read more.
In modern power systems or new energy power stations, the medium voltage circuit breakers (MVCBs) are becoming more crucial and the operation reliability of the MVCBs could be greatly improved by online monitoring technology. The purpose of this research is to put forward a fault diagnosis approach based on vibration signal envelope analysis, including offline fault feature training and online fault diagnosis. During offline fault feature training, the envelope of the vibration signal is extracted from the historic operation data of the MVCB, and then the typical fault feature vector M is built by using the wavelet packet-energy spectrum. In the online fault diagnosis process, the fault feature vector T is built based on the extracted envelope of the real-time vibration signal, and the MVCB states are assessed by using the distance between the feature vectors T and M. The proposed method only needs to handle the envelope of the vibration signal, which dramatically reduces the signal bandwidth, and then the cost of the processing hardware and software could be cut down. Full article
(This article belongs to the Special Issue Advanced Sensing for Mechanical Vibration and Fault Diagnosis)
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17 pages, 4605 KiB  
Article
Federated Transfer Learning Strategy: A Novel Cross-Device Fault Diagnosis Method Based on Repaired Data
by Zhenhao Yan, Jiachen Sun, Yixiang Zhang, Lilan Liu, Zenggui Gao and Yuxing Chang
Sensors 2023, 23(16), 7302; https://doi.org/10.3390/s23167302 - 21 Aug 2023
Cited by 1 | Viewed by 1046
Abstract
Federated learning has attracted much attention in fault diagnosis since it can effectively protect data privacy. However, efficient fault diagnosis performance relies on the uninterrupted training of model parameters with massive amounts of perfect data. To solve the problems of model training difficulty [...] Read more.
Federated learning has attracted much attention in fault diagnosis since it can effectively protect data privacy. However, efficient fault diagnosis performance relies on the uninterrupted training of model parameters with massive amounts of perfect data. To solve the problems of model training difficulty and parameter negative transfer caused by data corruption, a novel cross-device fault diagnosis method based on repaired data is proposed. Specifically, the local model training link in each source client performs random forest regression fitting on the fault samples with missing fragments, and then the repaired data is used for network training. To avoid inpainting fragments to produce the wrong characteristics of faulty samples, joint domain discrepancy loss is introduced to correct the phenomenon of parameter bias during local model training. Considering the randomness of the overall performance change brought about by the local model update, an adaptive update is proposed for each round of global model download and local model update. Finally, the experimental verification was carried out in various industrial scenarios established by three sets of bearing data sets, and the effectiveness of the proposed method in terms of fault diagnosis performance and data privacy protection was verified by comparison with various currently popular federated transfer learning methods. Full article
(This article belongs to the Special Issue Advanced Sensing for Mechanical Vibration and Fault Diagnosis)
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16 pages, 3541 KiB  
Article
A Bearing Fault Diagnosis Method Based on a Residual Network and a Gated Recurrent Unit under Time-Varying Working Conditions
by Zheng Wang, Xiaoyang Xu, Yu Zhang, Zhongyao Wang, Yuting Li, Zhidong Liu and Yuxi Zhang
Sensors 2023, 23(15), 6730; https://doi.org/10.3390/s23156730 - 27 Jul 2023
Cited by 1 | Viewed by 748
Abstract
The diagnosis of bearing faults is an important guarantee for the healthy operation of mechanical equipment. Due to the time-varying working conditions of mechanical equipment, it is necessary to achieve bearing fault diagnosis under time-varying working conditions. However, the superposition of the two-dimensional [...] Read more.
The diagnosis of bearing faults is an important guarantee for the healthy operation of mechanical equipment. Due to the time-varying working conditions of mechanical equipment, it is necessary to achieve bearing fault diagnosis under time-varying working conditions. However, the superposition of the two-dimensional working conditions of speed and acceleration brings great difficulties to diagnosis via data-driven models. The long short-term memory (LSTM) model based on the infinitesimal method is an effective method to solve this problem, but its performance still has certain limitations. On this basis, this article proposes a model for fault diagnosis under time-varying operating conditions that combines a residual network model (ResNet) and a gate recurrent unit (model) (GRU). Firstly, the samples were segmented, and feature extraction was performed using ResNet. We then used GRU to process the information. Finally, the classification results were output through the output network. This model could ignore the influence of acceleration and achieve high fault diagnosis accuracy under time-varying working conditions. In addition, we used t-SNE to reduce the dimensionality of the features and analyzed the role of each layer in the model. Experiments showed that this method had a better performance compared with existing bearing fault diagnosis methods. Full article
(This article belongs to the Special Issue Advanced Sensing for Mechanical Vibration and Fault Diagnosis)
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17 pages, 4379 KiB  
Article
Machine-Learning Algorithms for Process Condition Data-Based Inclusion Prediction in Continuous-Casting Process: A Case Study
by Yixiang Zhang, Zenggui Gao, Jiachen Sun and Lilan Liu
Sensors 2023, 23(15), 6719; https://doi.org/10.3390/s23156719 - 27 Jul 2023
Cited by 1 | Viewed by 1394
Abstract
Quality-related prediction in the continuous-casting process is important for the quality and process control of casting slabs. As intelligent manufacturing technologies continue to evolve, numerous data-driven techniques have been available for industrial applications. This case study was aimed at developing a machine-learning algorithm, [...] Read more.
Quality-related prediction in the continuous-casting process is important for the quality and process control of casting slabs. As intelligent manufacturing technologies continue to evolve, numerous data-driven techniques have been available for industrial applications. This case study was aimed at developing a machine-learning algorithm, capable of predicting slag inclusion defects in continuous-casting slabs, based on process condition sensor data. A large dataset consisting of sensor data from nearly 7300 casting samples has been analyzed, with the empirical mode decomposition (EMD) algorithm utilized to process the multi-modal time series. The following machine-learning algorithms have been examined: K-Nearest neighbors, support vector classifier (linear and nonlinear kernels), decision trees, random forests, AdaBoost, and Artificial Neural Networks. Four over-sampling or under-sampling algorithms have been adopted to solve imbalanced data distribution. In the experiment, the optimized random forest outperformed other machine-learning algorithms in terms of recall and ROC AUC, which could provide valuable insights for quality control. Full article
(This article belongs to the Special Issue Advanced Sensing for Mechanical Vibration and Fault Diagnosis)
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16 pages, 10211 KiB  
Article
Characterization Method of Damage Information Based on Heterogeneous Network
by Tong Huang, Qinhe Gao, Zhihao Liu, Dong Wang, Dong Ma and Lei Gao
Sensors 2023, 23(13), 6035; https://doi.org/10.3390/s23136035 - 29 Jun 2023
Viewed by 706
Abstract
Damage is the main form of conflict, and the characterization of damage information is an important component of conflict evaluation. In the existing research, damage mainly refers to the damage effect of a damage load on the target structure. However, in the actual [...] Read more.
Damage is the main form of conflict, and the characterization of damage information is an important component of conflict evaluation. In the existing research, damage mainly refers to the damage effect of a damage load on the target structure. However, in the actual conflict environment, damage is a complex process that includes the entire process from the initial introduction of the damage load to the target function. Therefore, in this paper, the transfer logic of the damage process is analyzed, and the damage process is sequentially divided into being discovered, being attacked, being hit, and being destroyed in succession. Specifically, first considering the multiple types of each process, the transmission of damage is likened to the flow of damage, a network model to characterize damage information based on heterogeneous network meta-path and network flow theory (HF-MCDI) is established. Then, the characteristics of damage information are analyzed based on the capacity of the damage network, the correlation of the damage path, and the importance of the damage node. In addition, HF-MCDI can not only represent the complete damage information and the transmission characteristics of the damage load but also the structural characteristics of the target. Finally, the feasibility and effectiveness of the established HF-MCDI method are fully demonstrated by the example analysis of the launch platform. Full article
(This article belongs to the Special Issue Advanced Sensing for Mechanical Vibration and Fault Diagnosis)
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15 pages, 8852 KiB  
Article
The Remaining Useful Life Prediction Method of a Hydraulic Pump under Unknown Degradation Model with Limited Data
by Fenghe Wu, Jun Tang, Zhanpeng Jiang, Yingbing Sun, Zhen Chen and Baosu Guo
Sensors 2023, 23(13), 5931; https://doi.org/10.3390/s23135931 - 26 Jun 2023
Cited by 2 | Viewed by 1327
Abstract
This study proposes a remaining useful life (RUL) prediction method using limited degradation data with an unknown degradation model for hydraulic pumps with long service lives and no failure data in turbine control systems. The volumetric efficiency is calculated based on real-time monitoring [...] Read more.
This study proposes a remaining useful life (RUL) prediction method using limited degradation data with an unknown degradation model for hydraulic pumps with long service lives and no failure data in turbine control systems. The volumetric efficiency is calculated based on real-time monitoring signal data, and it is used as the degradation indicator. The optimal degradation curve is established using the degradation trajectory model, and the optimal probability distribution model is selected via the K-S test. The above process was repeated to optimize the degradation model and update parameters in different performance degradation stages of the hydraulic pump, providing quantification of the prediction uncertainty and enabling accurate online prediction of the hydraulic pump’s RUL. Finally, an RUL test bench for hydraulic pumps is built for verification. The results show that the proposed method is convenient, efficient, and has low model complexity. The method enables online accurate prediction of the RUL of hydraulic pumps using only limited degradation data, with a prediction accuracy of over 85%, which meets practical application requirements. Full article
(This article belongs to the Special Issue Advanced Sensing for Mechanical Vibration and Fault Diagnosis)
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17 pages, 6308 KiB  
Article
MVDR-LSTM Distance Estimation Model Based on Diagonal Double Rectangular Array
by Xiong Zhang, Wenbo Wu, Jialu Li, Fan Dong and Shuting Wan
Sensors 2023, 23(11), 5094; https://doi.org/10.3390/s23115094 - 26 May 2023
Viewed by 1080
Abstract
Deep learning algorithms have the advantages of a powerful time series prediction ability and the real-time processing of massive samples of big data. Herein, a new roller fault distance estimation method is proposed to address the problems of the simple structure and long [...] Read more.
Deep learning algorithms have the advantages of a powerful time series prediction ability and the real-time processing of massive samples of big data. Herein, a new roller fault distance estimation method is proposed to address the problems of the simple structure and long conveying distance of belt conveyors. In this method, a diagonal double rectangular microphone array is used as the acquisition device, minimum variance distortionless response (MVDR) and long short-term memory network (LSTM) are used as the processing models, and the roller fault distance data are classified to complete the estimation of the idler fault distance. The experimental results showed that this method could achieve high-accuracy fault distance identification in a noisy environment and had better accuracy than the conventional beamforming algorithm (CBF)-LSTM and functional beamforming algorithm (FBF)-LSTM. In addition, this method could also be applied to other industrial testing fields and has a wide range of application prospects. Full article
(This article belongs to the Special Issue Advanced Sensing for Mechanical Vibration and Fault Diagnosis)
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22 pages, 16773 KiB  
Article
Vibration and Noise Analysis and Experimental Study of Rail Conveyor
by Nini Hao, Xinming Sun, Mengchao Zhang, Yuan Zhang, Xingyu Wang and Xiaoting Yi
Sensors 2023, 23(10), 4867; https://doi.org/10.3390/s23104867 - 18 May 2023
Viewed by 1403
Abstract
The rail conveyor is a new type of energy-saving system for the long-distance transportation of bulk materials. Operating noise is an urgent problem that the current model faces. It will cause noise pollution and affect the health of workers. In this paper, the [...] Read more.
The rail conveyor is a new type of energy-saving system for the long-distance transportation of bulk materials. Operating noise is an urgent problem that the current model faces. It will cause noise pollution and affect the health of workers. In this paper, the factors causing vibration and noise are analyzed by modeling the wheel-rail system and the supporting truss structure. Based on the built test platform, the system vibration of the vertical steering wheel, the track support truss, and the track connection were measured, and the vibration characteristics at different positions were analyzed. Based on the established noise and vibration model, the distribution and occurrence rules of system noise under different operating speeds and fastener stiffness conditions were obtained. The experimental results show that the vibration amplitude of the frame near the head of the conveyor is the largest. The amplitude under the condition of 2 m/s running speed at the same position is 4 times that under the condition of 1 m/s. At different welds of the track, the width and depth of the rail gap have a great influence on the vibration impact, which is mainly due to the impact of the uneven impedance at the track gap, and the greater the running speed, the more obvious the vibration impact. The simulation results show the trend of noise generation, the speed of the trolley, and the stiffness of the track fasteners have a positive effect on the generation of noise in the low-frequency region. The research results of this paper will play an important role in the noise and vibration analysis of rail conveyors and help to optimize the structure design of the track transmission system. Full article
(This article belongs to the Special Issue Advanced Sensing for Mechanical Vibration and Fault Diagnosis)
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19 pages, 4645 KiB  
Article
Intelligent Health Assessment of Aviation Bearing Based on Deep Transfer Graph Convolutional Networks under Large Speed Fluctuations
by Xiaoli Zhao, Xingjun Zhu, Jianyong Yao, Wenxiang Deng, Yudong Cao, Peng Ding, Minping Jia and Haidong Shao
Sensors 2023, 23(9), 4379; https://doi.org/10.3390/s23094379 - 28 Apr 2023
Cited by 1 | Viewed by 1387
Abstract
As a critical support and fixed component of aero engines, electro-hydrostatic actuators, and other equipment, the operation of aviation bearings is often subject to high speed, high-temperature rise, large load, and other continuous complex fluctuation conditions, which makes their health assessment tasks more [...] Read more.
As a critical support and fixed component of aero engines, electro-hydrostatic actuators, and other equipment, the operation of aviation bearings is often subject to high speed, high-temperature rise, large load, and other continuous complex fluctuation conditions, which makes their health assessment tasks more difficult. To solve this problem, an intelligent health assessment method based on a new Deep Transfer Graph Convolutional Network (DTGCN) is proposed for aviation bearings under large speed fluctuation conditions. First, a new DTGCN algorithm is designed, which mainly uses the domain adaptation mechanism to enhance the performance of Graph Convolutional Network (GCN) and the generalization performance of transfer properties. Specifically, order spectrum analysis is employed to resample the vibration signals of aviation bearings and transform them into order spectral signals. Then, the trained 1dGCN is used as the feature extractor, and the designed Dynamic Multiple Kernel Maximum Mean Discrepancy (DMKMMD) is calculated to match the difference in edge distribution. Finally, the aligned features are fed into the softmax classifier for intelligent health assessment. The effectiveness of the proposed diagnostic algorithm and method are validated by using aviation bearing fault data set under large speed fluctuation conditions. Full article
(This article belongs to the Special Issue Advanced Sensing for Mechanical Vibration and Fault Diagnosis)
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15 pages, 4184 KiB  
Article
Fault Voiceprint Signal Diagnosis Method of Power Transformer Based on Mixup Data Enhancement
by Shuting Wan, Fan Dong, Xiong Zhang, Wenbo Wu and Jialu Li
Sensors 2023, 23(6), 3341; https://doi.org/10.3390/s23063341 - 22 Mar 2023
Cited by 5 | Viewed by 1489
Abstract
A voiceprint signal as a non-contact test medium has a broad application prospect in power-transformer operation condition monitoring. Due to the high imbalance in the number of fault samples, when training the classification model, the classifier is prone to bias to the fault [...] Read more.
A voiceprint signal as a non-contact test medium has a broad application prospect in power-transformer operation condition monitoring. Due to the high imbalance in the number of fault samples, when training the classification model, the classifier is prone to bias to the fault category with a large number of samples, resulting in poor prediction performance of other fault samples, and affecting the generalization performance of the classification system. To solve this problem, a method of power-transformer fault voiceprint signal diagnosis based on Mixup data enhancement and a convolution neural network (CNN) is proposed. First, the parallel Mel filter is used to reduce the dimension of the fault voiceprint signal to obtain the Mel time spectrum. Then, the Mixup data enhancement algorithm is used to reorganize the generated small number of samples, effectively expanding the number of samples. Finally, CNN is used to classify and identify the transformer fault types. The diagnosis accuracy of this method for a typical unbalanced fault of a power transformer can reach 99%, which is superior to other similar algorithms. The results show that this method can effectively improve the generalization ability of the model and has good classification performance. Full article
(This article belongs to the Special Issue Advanced Sensing for Mechanical Vibration and Fault Diagnosis)
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