Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

Journal Browser

Fault Diagnosis and Detection of Machinery

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: closed (20 December 2024) | Viewed by 18040

Share This Special Issue

Special Issue Editors

Dr. Marco Cocconcelli

E-Mail Website1 Website2
Guest Editor

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

Dr. Matteo Strozzi

E-Mail Website
Guest Editor

Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, 42122 Reggio Emilia, Italy
Interests: nonlinear vibrations; energy exchange in dynamics; applied mechanics; nanotubes; FGM
Special Issues, Collections and Topics in MDPI journals

Dr. Gianluca D’Elia

E-Mail Website
Guest Editor

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

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. Applied Sciences 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 2400 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

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

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (10 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

40 pages, 19053 KiB

Open AccessArticle

MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems

by Abdul Jabbar, Marco Cocconcelli, Gianluca D’Elia, Davide Borghi, Luca Capelli, Jacopo Cavalaglio Camargo Molano, Matteo Strozzi and Riccardo Rubini

Appl. Sci. 2025, 15(7), 3691; https://doi.org/10.3390/app15073691 - 27 Mar 2025

Viewed by 341

Abstract

This paper introduces a comprehensive and publicly accessible data set from an experimental study on an independent cart system powered by linear motors. The primary objective is to advance research in machine health monitoring, predictive maintenance, and stochastic modeling by providing the first data set of its kind. Vibration signals were collected using sensors placed along the track, alongside key system variables such as cart position, following error, speed, and set current. Experiments were conducted under a wide range of operating conditions, including different fault types, fault severities, cart speeds, and fault orientations, for both single-cart and multi-cart configurations. The data set captures the relationship between vibration signatures, system variables, and fault characteristics across diverse speed profiles. The data set includes inner race (IR) and outer race (OR) faults in both the top and bottom bearings, with fault severities of 0.25 mm, 0.5 mm, 1.0 mm, and 1.5 mm in width. Eight different types of experiments were performed, classified based on the number of carts used, the section of the guide rail traversed, and the type of movement exhibited. Each experiment was conducted at two distinct nominal speeds of 1000 mm/s and 2000 mm/s, with acquisition durations ranging from 30 s to 2 min. Many experiments included multiple realizations to ensure statistical reliability. Data were recorded at a sampling frequency of 50 kHz with a resolution of 24 bits. For single-cart experiments, 5 system variables were captured, while for three-cart experiments, 15 system variables were recorded along with nine vibration channels. The total data set is approximately 400 GB, offering an extensive resource for data-driven research. Independent cart systems present unique challenges such as non-synchronous operation, speed reversals, and modularity, with each cart containing multiple bearings. In industrial applications where hundreds of carts may operate simultaneously, monitoring a large number of bearings becomes highly complex, making fault identification and localization particularly difficult. Unlike conventional rotary systems, where bearings are fixed around a rotating shaft, independent cart systems involve bearings that both rotate and translate along the track. This fundamental difference makes existing data sets and methodologies inadequate, emphasizing the need for specialized research. By addressing this gap, this work provides a critical resource for benchmarking and developing novel algorithms for fault diagnosis, signal processing, and machine learning in industrial transport applications. The outcomes of this study lay the foundation for future research in the condition monitoring of linear motor-driven transport systems. Full article

(This article belongs to the Special Issue Fault Diagnosis and Detection of Machinery)

► Show Figures

Figure 1

16 pages, 7598 KiB

Open AccessArticle

Torque/Speed Equilibrium Point Monitoring of an Aircraft Hybrid Electric Propulsion System Through Accelerometric Signal Processing

by Vincenzo Niola, Chiara Cosenza, Enrico Fornaro, Pierangelo Malfi, Francesco Melluso, Armando Nicolella, Sergio Savino and Mario Spirto

Appl. Sci. 2025, 15(4), 2135; https://doi.org/10.3390/app15042135 - 18 Feb 2025

Viewed by 419

Abstract

The present work proposes a new torque/speed equilibrium point monitoring technique for an aircraft Hybrid Electric Propulsion System (HEPS) through an accelerometric-signal-based approach. Sampled signals were processed using statistical indexes, filtering, and a feature reduction and selection algorithm to train a classification Feedforward Neural Network. A supervised Machine Learning model was developed to classify the HEPS operating modes characterized by an Internal Combustion Engine as a single propulsor or by combining the latter with an Electric Machine used as a motor or a generator. The abnormal changes in the torque/speed equilibrium point were detected by the monitoring index built by computing the Root Mean Square on the value identified by the classifier. The procedure was validated through experimental tests that demonstrated its validity. Full article

(This article belongs to the Special Issue Fault Diagnosis and Detection of Machinery)

► Show Figures

Figure 1

12 pages, 3393 KiB

Open AccessArticle

Research on Fault Detection and Automatic Diagnosis Technology of Water Hammer in Centrifugal Pump

by Lei Chen, Zhenao Li, Wenxuan Shi and Wenlong Li

Appl. Sci. 2024, 14(13), 5606; https://doi.org/10.3390/app14135606 - 27 Jun 2024

Cited by 1 | Viewed by 1250

Abstract

Centrifugal pumps are important equipment in industrial production, and their safe and reliable operation is of great significance to water supply and industrial safety. During the use of centrifugal pumps, faults such as bearing damage, blade wear, shaft imbalance, shaft misalignment and water hammer often occur. Among them, although water hammer faults occur at a low frequency, they are difficult to monitor and pose significant risks to valve and pipeline interfaces. This article analyzes the causes, mechanisms and phenomena of water hammer faults in centrifugal pumps, designs a monitoring method to effectively monitor the vibration signal of the centrifugal pumps, extracts vibration characteristics to determine and record water hammer events, designs monitoring and diagnostic models for the edge layer and server side, and establishes an experimental verification testing system. The test results show that, under the conditions of simulating water hammer faults, after high-pass filtering of the collected vibration data, the kurtosis index, pulse index and margin index all exceed twice the threshold, and both sensors emit water hammer alarms. The designed data acquisition method can capture water hammer signals in a timely manner, and the analysis model can automatically identify water hammer faults based on existing fault knowledge and rules. This fully demonstrates the scientific and effective nature of the proposed centrifugal pump fault monitoring method and system, which is of great significance for ensuring the safe operation and improving the design of centrifugal pumps. Full article

(This article belongs to the Special Issue Fault Diagnosis and Detection of Machinery)

► Show Figures

Figure 1

17 pages, 7230 KiB

Open AccessArticle

Practical Test on the Operation of the Three-Phase Induction Motor under Single-Phasing Fault

by Ali Abdo, Jamal Siam, Ahmed Abdou, Hakam Shehadeh and Rashad Mustafa

Appl. Sci. 2024, 14(11), 4690; https://doi.org/10.3390/app14114690 - 29 May 2024

Cited by 4 | Viewed by 2238

Abstract

Single-phasing is a common problem in the three-phase electrical grid. Despite the fact that the fault occurs in one phasing, the three-phase load is affected, and therefore the load is typically turned off. The three-phase induction motor is the most commonly used in the industry; therefore, this research investigates the behavior of the three-phase induction motor under a single-phasing fault. The main aim of this paper is to answer the question, should the three-phase induction motor be turned off under a single-phasing fault? The problem is investigated theoretically and compared with practical tests to explore the parameters of the induction motor (current, stator temperature, and vibration) that are affected under healthy and single-phasing fault conditions. A practical test machine is built to test the motor behavior under single-phasing faults, where the practical experiment results are compared to those of the simulations. Despite the common recommendation under single-phasing fault is to turn off the induction motors, the preliminary results of this study show that turning off an induction motor under single-phasing can be avoided under certain operating conditions with a simple protection scheme, which is useful in some practical situations. Full article

(This article belongs to the Special Issue Fault Diagnosis and Detection of Machinery)

► Show Figures

Figure 1

26 pages, 10163 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1293

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 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)

► Show Figures

Figure 1

17 pages, 6647 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 2605

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 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)

► Show Figures

Figure 1

17 pages, 27726 KiB

Open AccessArticle

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 30 | Viewed by 2377

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 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)

► Show Figures

Figure 1

18 pages, 5746 KiB

Open AccessArticle

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 13 | Viewed by 2177

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 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)

► Show Figures

Figure 1

23 pages, 7828 KiB

Open AccessArticle

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 6 | Viewed by 1832

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 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)

► Show Figures

Figure 1

15 pages, 5125 KiB

Open AccessArticle

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

Cited by 3 | Viewed by 1925

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 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)

► Show Figures

Journal Menu

Journal Browser

Fault Diagnosis and Detection of Machinery

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (10 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI