Fault Diagnosis and Detection of Machinery

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 20 April 2024 | Viewed by 6118

Special Issue Editors

Department of Sciences and Methods of Engineering, University of Modena and Reggio Emilia, 42122 Modena, Italy
Interests: fault detection of machinery; vibration-based condition monitoring; mechanical systems modeling; bearing analysis
Special Issues, Collections and Topics in MDPI journals
Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, 42122 Reggio Emilia, Italy
Interests: nonlinear dynamics; shells and plates; carbon nanotubes; functionally graded materials; vibration-based condition monitoring; mechanical systems modeling; stability analysis; damping
Special Issues, Collections and Topics in MDPI journals
Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, 42122 Reggio Emilia, Italy
Interests: fault detection of machinery; vibration-based condition monitoring; mechanical systems modeling; gear analysis

Special Issue Information

Dear Colleagues,

This Special Issue focuses on sharing advances, results and perspectives in the field of condition monitoring of mechanical systems. Although most of the critical components have been widely analyzed, new applications are proposed in the industrial field and always pose new challenges to diagnostics in terms of complexity, harsh environment, and non-stationary working conditions, among others. An example is the diagnostics of a fleet of machines in a closed environment. Strong non-stationarity of the motion profile or of the dynamic loads, vibration interference from close devices, or inability to properly sensor the moving elements make the condition monitoring challenging.

The target of the Special Issue is to collect novel contributions for all the steps of the fault diagnosis and detection process. An indicative list may include the development of specific sensors, hardware setup, data analytics, physical modelling, data processing and data fusion. Papers on machine learning approaches to diagnostics are accepted but the physical parameters that determine the success of the methodology proposed should be evident. Although advances have been made in other fields—such as MCSA—this Special Issue is mainly focused on the vibration-based condition monitoring of mechanical/mechatronics systems. Other types of signals/sensors are allowed as long as they are necessary for the vibrational analysis.

The experimental dataset is not accessible to all researchers but several free collections are available online. We suggest, for example, the Polito Bearing Dataset (Politecnico di Torino, Italy), available through the following link:

ftp://ftp.polito.it/people/DIRG_BearingData/

It comprises both tests at different fault levels and a complete lifetime of a bearing set.

Dr. Marco Cocconcelli
Dr. Matteo Strozzi
Dr. Gianluca D’Elia
Guest Editors

Manuscript Submission Information

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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. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • damage identification
  • damage prediction
  • gear/bearing diagnostics
  • remaining useful life
  • digital twins for diagnostics/prognostics
  • physics-enhanced machine learning
  • variable speed conditions
  • non-stationary signal processing
  • cyclostationarity
  • diagnostic algorithms
  • mechatronic systems
  • rotor dynamics
  • stability analysis

Published Papers (6 papers)

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Research

26 pages, 10163 KiB  
Article
Fault Diagnosis of Vehicle Gearboxes Based on Adaptive Wavelet Threshold and LT-PCA-NGO-SVM
by Qingyong Zhang, Changhuan Song and Yiqing Yuan
Appl. Sci. 2024, 14(3), 1212; https://doi.org/10.3390/app14031212 - 31 Jan 2024
Viewed by 464
Abstract
Vehicle gearboxes are subject to strong noise interference during operation, and the noise in the signal affects the accuracy of fault identification. Signal denoising and fault diagnosis processes are often conducted independently, overlooking their synergistic potential in practical applications. This article proposes a [...] Read more.
Vehicle gearboxes are subject to strong noise interference during operation, and the noise in the signal affects the accuracy of fault identification. Signal denoising and fault diagnosis processes are often conducted independently, overlooking their synergistic potential in practical applications. This article proposes a gearbox fault identification method that integrates improved adaptive modified wavelet function noise reduction, logarithmic transformation on principal component analysis (LT-PCA), and support vector machines (SVMs) to mitigate the influence of noise and feature outliers on fault signal recognition. Initially, to address the issue of interfering signals within the original signal, an innovative adaptive wavelet function optimized by the simulated annealing (SA) algorithm is employed for noise reduction of the main intrinsic mode function (IMF) components decomposed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Subsequently, due to the persistence of high-dimension feature vectors containing numerous outliers that interfere with recognition, the LT-PCA compression and dimensionality reduction method is proposed. Experimental analyses on vehicle gearboxes demonstrate an average fault recognition rate of 96.65% using the newly proposed wavelet noise reduction function and the integrated method. This allows for quick and efficient identification of fault types and provides crucial technical support for related industrial applications. Full article
(This article belongs to the Special Issue Fault Diagnosis and Detection of Machinery)
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17 pages, 6647 KiB  
Article
Analysis of Vibration Characteristics of Planetary Gearbox with Broken Sun Gear Based on Phenomenological Model
by Mengting Zou, Jun Ma, Xin Xiong and Rong Li
Appl. Sci. 2023, 13(16), 9413; https://doi.org/10.3390/app13169413 - 19 Aug 2023
Viewed by 950
Abstract
To investigate the vibration properties in healthy and fault conditions of planetary gearboxes, a phenomenological model is constructed to present the vibration spectrum structure. First, the effects of the base deflection of the gear fillet and the flexibility between the root circle and [...] Read more.
To investigate the vibration properties in healthy and fault conditions of planetary gearboxes, a phenomenological model is constructed to present the vibration spectrum structure. First, the effects of the base deflection of the gear fillet and the flexibility between the root circle and the base circle on the time-varying meshing stiffness are considered in order to construct an equivalent model of time-varying mesh stiffness and broken tooth faults, exploring the law of variation for meshing stiffness when differently sized faults occur on the sun gear. Then, considering both the effect of the vibration transfer path and the meshing impacts, we establish phenomenological models of planetary gears under healthy and fault conditions. By comparing and analyzing the phenomenological model based on the cosine function to verify the effectiveness of the proposed model. The experimental results show that the error of the proposed model is 1.38% lower than that of the traditional phenomenological model, and the proposed model can accurately analyze the frequency, amplitude, and sideband characteristics of the vibration signals of sun gear with different degrees of broken tooth, which can be used for the local fault diagnosis of planetary gearboxes. Full article
(This article belongs to the Special Issue Fault Diagnosis and Detection of Machinery)
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17 pages, 27726 KiB  
Article
Identification of Subsurface Mesoscale Crack in Full Ceramic Ball Bearings Based on Strain Energy Theory
by Xiaotian Bai, Zhaonan Zhang, Huaitao Shi, Zhong Luo and Tao Li
Appl. Sci. 2023, 13(13), 7783; https://doi.org/10.3390/app13137783 - 30 Jun 2023
Cited by 23 | Viewed by 909
Abstract
Subsurface mesoscale cracks exist widely in the outer ring of full ceramic ball bearings (FCBBs), which is a potential threat for the stable operation of related devices such as aero engines, food processing machinery, and artificial replacement hip joints. This paper establishes a [...] Read more.
Subsurface mesoscale cracks exist widely in the outer ring of full ceramic ball bearings (FCBBs), which is a potential threat for the stable operation of related devices such as aero engines, food processing machinery, and artificial replacement hip joints. This paper establishes a dynamic model of subsurface mesoscale cracks in the outer ring of FCBBs based on strain energy theory, and the influence of different crack lengths on the running state is analyzed. The existence of mesoscale cracks is regarded as weakening on the stiffness coefficient, and the deterioration degree of outer ring stiffness of subsurface cracks is thereby quantified. It is found that a small wave peak appears in the vibration time-domain signal when there is a mesoscale crack on the outer ring subsurface, and the crack evolution is evaluated by the amplitude of the corresponding feature frequency. Finally, the accuracy of the model is verified by experiments. The model realizes the identification and degree evaluation of subsurface mesoscale cracks in FCBBs, and provides theoretical references for the diagnosis and status monitoring for FCBB rotor systems. Full article
(This article belongs to the Special Issue Fault Diagnosis and Detection of Machinery)
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18 pages, 5746 KiB  
Article
Research on Remaining Useful Life Prediction of Bearings Based on MBCNN-BiLSTM
by Jian Li, Faguo Huang, Haihua Qin and Jiafang Pan
Appl. Sci. 2023, 13(13), 7706; https://doi.org/10.3390/app13137706 - 29 Jun 2023
Cited by 3 | Viewed by 1081
Abstract
For safe maintenance and to reduce the risk of mechanical faults, the remaining useful life (RUL) estimate of bearings is significant. The typical methods of bearings’ RUL prediction suffer from low prediction accuracy because of the difficulty in extracting features. With the aim [...] Read more.
For safe maintenance and to reduce the risk of mechanical faults, the remaining useful life (RUL) estimate of bearings is significant. The typical methods of bearings’ RUL prediction suffer from low prediction accuracy because of the difficulty in extracting features. With the aim of improving the accuracy of RUL prediction, an approach based on multi-branch improved convolutional network (MBCNN) with global attention mechanism combined with bi-directional long- and short-term memory (BiLSTM) network is proposed for bearings’ RUL prediction. Firstly, the original vibration signal is fast Fourier transformed to obtain the frequency domain signal and then normalized. Secondly, the original signal and the frequency domain signal are input into the designed MBCNN network as two branches to extract the spatial features, and then input into the BiLSTM network to further extract the timing features, and the RUL of bearings is mapped by the fully connected network to achieve the purpose of prediction. Finally, an example validation was performed on a publicly available bearing degradation dataset. Compared with some existing prediction methods, the mean absolute and root mean square errors of the predictions were reduced by “22.2%” to “50.0%” and “26.1%” to “52.8%”, respectively, which proved the effectiveness and feasibility of the proposed method. Full article
(This article belongs to the Special Issue Fault Diagnosis and Detection of Machinery)
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23 pages, 7828 KiB  
Article
Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function
by Haihua Qin, Jiafang Pan, Jian Li and Faguo Huang
Appl. Sci. 2023, 13(13), 7593; https://doi.org/10.3390/app13137593 - 27 Jun 2023
Cited by 1 | Viewed by 924
Abstract
In order to cope with the influences of noise interference and variable load on rolling bearing fault diagnosis in real industrial environments, a rolling bearing fault diagnosis method based on CBAM_ResNet and ACON activation function is proposed. Firstly, the collected bearing working vibration [...] Read more.
In order to cope with the influences of noise interference and variable load on rolling bearing fault diagnosis in real industrial environments, a rolling bearing fault diagnosis method based on CBAM_ResNet and ACON activation function is proposed. Firstly, the collected bearing working vibration signals are made into input samples to retain the original features to the maximum extent. Secondly, the CBAM_ResNet fault diagnosis model is constructed. By taking advantage of the convolutional neural network (CNN) in classification tasks and key feature extraction, the convolutional block attention module network (CBAM) is embedded in the residual blocks, to avoid model degradation and enhance the interaction of information in channel and spatial, raise the key feature extraction capability of the model. Finally, the Activate or Not (ACON) activation function, is introduced to adaptively activate shallow features for the purpose of improving the model’s feature representation and generalization capability. The bearing dataset of Case Western Reserve University (CWRU) is used for experiments, and the average accuracy of the proposed method is 97.68% and 93.93% under strong noise interference and variable load, respectively. Compared with the other three published bearing fault diagnosis methods, the results indicate that this proposed method has better noise immunity and generalization ability, and has good application value. Full article
(This article belongs to the Special Issue Fault Diagnosis and Detection of Machinery)
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15 pages, 5125 KiB  
Article
Fault Diagnosis for Body-in-White Welding Robot Based on Multi-Layer Belief Rule Base
by Bang-Cheng Zhang, Ji-Dong Wang, Zhong Zheng, Dian-Xin Chen and Xiao-Jing Yin
Appl. Sci. 2023, 13(8), 4773; https://doi.org/10.3390/app13084773 - 10 Apr 2023
Viewed by 1046
Abstract
Fault diagnosis for body-in-white (BIW) welding robots is important for ensuring the efficient production of the welding assembly line. As a result of the complex mechanism of the body-in-white welding robot, its strong correlation of components, and the many types of faults, it [...] Read more.
Fault diagnosis for body-in-white (BIW) welding robots is important for ensuring the efficient production of the welding assembly line. As a result of the complex mechanism of the body-in-white welding robot, its strong correlation of components, and the many types of faults, it is difficult to establish a complete fault diagnosis model. Therefore, a fault diagnosis model for a BIW-welding robot based on a multi-layer belief rule base (BRB) was proposed. This model can effectively integrate monitoring data and expert knowledge to achieve an accurate fault diagnosis and facilitate traceability. First, according to the established fault tree, a fault mechanism was determined. Second, based on the multi-layer relationship of a fault tree, we established a multi-layer BRB model. Meanwhile, in order to improve the accuracy of the model parameters, the projection covariance matrix adaptive evolutionary strategy (P-CMA-ES) algorithm was used to optimize and update the parameters of the fault diagnosis model. Finally, the validity of the proposed model was verified by a simulation experiment for the BIW-welding robot. Full article
(This article belongs to the Special Issue Fault Diagnosis and Detection of Machinery)
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