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Applied Sciences
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Fault Diagnosis and Health Monitoring of Mechanical Systems
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Fault Diagnosis and Health Monitoring of Mechanical Systems

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

Deadline for manuscript submissions: closed (20 September 2024) | Viewed by 6532

Special Issue Editors

Dr. Xiaoxi Ding

E-Mail Website
Guest Editor
State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
Interests: signal processing; data mining; edge computing; digital twin; interpretable deep learning; and intelligent monitoring system for fault diagnosis and health monitoring of machines
Dr. Jun Zhu

E-Mail Website
Guest Editor
School of Civil Aviation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: intelligent sensing; condition monitoring; fault diagnosis; dynamics modeling; signal processing; data analysis

Special Issue Information

Dear Colleagues,

In recent years, the fault diagnosis and health monitoring of mechanical systems have played an increasingly important role in automated and intelligent industrial applications. Although many studies related to mechanism modeling and data-driven and artificial intelligence have been proposed, there are still issues that need further study. These issues include the intelligent fault diagnosis of mechanical systems, such as nonlinear system dynamics modeling analysis, early weak fault detection, multi-source information decoupling and separation, trend assessment and prediction under complex operating conditions, interpretable deep learning, and digital twin algorithms.

This Special Issue aims to publish the latest advancements and research findings in the field of mechanical systems’ fault diagnosis, as well as the health monitoring and interpretable intelligent recognition. It aims to explore innovative theories, methodologies, and technologies employed in ensuring the safety and longevity of mechanical systems. Topics covered may include, but are not limited to, dynamics mechanism analysis, signal adaptive filtering, blind source separation, predictive maintenance techniques, information fusion, intelligent systems’ fault diagnosis, predictive techniques, IoT applications, interpretable deep learning algorithms, and digital twin approaches to mechanical systems’ health maintenance. The Special Issue provides a platform for experts, scholars, and research groups in related fields to share their insights, experiences, and solutions contributing to the advancement of the intelligent fault diagnosis and health monitoring of mechanical systems.

Dr. Xiaoxi Ding
Dr. Jun Zhu
Guest Editors

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Keywords

  • dynamics mechanism modeling
  • signal adaptive filtering
  • mode decomposition
  • blind source separation
  • information fusion
  • intelligent fault diagnosis
  • predictive maintenance techniques
  • interpretable deep learning
  • internet of things applications
  • digital twin approaches to mechanical systems health maintenance

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Published Papers (5 papers)

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Research

27 pages, 22321 KiB  
Article
Model Design of Inter-Turn Short Circuits in Internal Permanent Magnet Synchronous Motors and Application of Wavelet Transform for Fault Diagnosis
by Chin-Sheng Chen, Chia-Jen Lin, Fu-Jen Yang and Feng-Chieh Lin
Appl. Sci. 2024, 14(20), 9570; https://doi.org/10.3390/app14209570 - 20 Oct 2024
Cited by 1 | Viewed by 1190
Abstract
The challenge in developing an AI deep learning model for motor health diagnosis is hampered by the lack of sufficient and representative datasets, leading to considerable time and resource consumption in research. Therefore, this paper focuses on the analysis of the second harmonic [...] Read more.
The challenge in developing an AI deep learning model for motor health diagnosis is hampered by the lack of sufficient and representative datasets, leading to considerable time and resource consumption in research. Therefore, this paper focuses on the analysis of the second harmonic component fault characteristic induced by inter-turn short circuits (ITSCs) in phase voltages. First, it establishes a coil inter-turn short-circuit fault (ITSCF) model of the motor to identify the twice-frequency q-axis voltage error characteristics. Subsequently, it develops simulation programs by integrating control and fault models in MATLAB/Simulink/Simscape to observe and analyze the q-axis voltage and circulating current errors caused by the short circuit. Finally, a discrete wavelet transform method is established to analyze the q-axis synchronous reference frame voltage. By applying the energy-based method to extract the twice-frequency voltage error characteristics, the approach successfully detects the error features and confirms ITSCF in the motor. The contributions of this paper include not only the development of an ITSCF characteristic model for the motor but also the successful application of wavelet transform to effectively analyze the time-frequency characteristics of its signals. This approach can serve as a valuable reference for the design of deep learning models in future AI applications. Full article
(This article belongs to the Special Issue Fault Diagnosis and Health Monitoring of Mechanical Systems)
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18 pages, 5732 KiB  
Article
A Collaborative Domain Adversarial Network for Unlabeled Bearing Fault Diagnosis
by Zhigang Zhang, Chunrong Xue, Xiaobo Li, Yinjun Wang and Liming Wang
Appl. Sci. 2024, 14(19), 9116; https://doi.org/10.3390/app14199116 - 9 Oct 2024
Viewed by 1006
Abstract
At present, data-driven fault diagnosis has made significant achievements. However, in actual industrial environments, labeled fault data are difficult to obtain, making the industrial application of intelligent fault diagnosis models very challenging. This limitation even prevents intelligent fault diagnosis algorithms from being applicable [...] Read more.
At present, data-driven fault diagnosis has made significant achievements. However, in actual industrial environments, labeled fault data are difficult to obtain, making the industrial application of intelligent fault diagnosis models very challenging. This limitation even prevents intelligent fault diagnosis algorithms from being applicable in real-world industrial settings. In light of this, this paper proposes a Collaborative Domain Adversarial Network (CDAN) method for the fault diagnosis of rolling bearings using unlabeled data. First, two types of feature extractors are employed to extract features from both the source and target domain samples, reducing signal redundancy and avoiding the loss of critical signal features. Second, the multi-kernel clustering algorithm is used to compute the differences in input feature values, create pseudo-labels for the target domain samples, and update the CDAN network parameters through backpropagation, enabling the network to extract domain-invariant features. Finally, to ensure that unlabeled target domain data can participate in network training, a pseudo-label strategy using the maximum probability label as the true label is employed, addressing the issue of unlabeled target domain data not being trainable and enhancing the model’s ability to acquire reliable diagnostic knowledge. This paper validates the CDAN using two publicly available datasets, CWRU and PU. Compared with four other advanced methods, the CDAN method improved the average recognition accuracy by 7.85% and 5.22%, respectively. This indirectly proves the effectiveness and superiority of the CDAN in identifying unlabeled bearing faults. Full article
(This article belongs to the Special Issue Fault Diagnosis and Health Monitoring of Mechanical Systems)
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19 pages, 10511 KiB  
Article
Strain Gauge Location Optimization for Operational Load Monitoring of an Aircraft Wing Using an Improved Correlation Measure
by Hang Peng, Bintuan Wang, Yu Ning, Shancheng Cao and Mabao Liu
Appl. Sci. 2024, 14(19), 9078; https://doi.org/10.3390/app14199078 - 8 Oct 2024
Viewed by 1400
Abstract
Operational loads of an aircraft are the prerequisite for assessing its safety or fatigue life. Traditionally, numerous strain gauge sensors are installed to monitor the operational loads, which inevitably increase the weight and system complexity of the aircraft. Therefore, in order to decrease [...] Read more.
Operational loads of an aircraft are the prerequisite for assessing its safety or fatigue life. Traditionally, numerous strain gauge sensors are installed to monitor the operational loads, which inevitably increase the weight and system complexity of the aircraft. Therefore, in order to decrease the maintenance costs and data redundancy, the number and location of strain sensors should be optimized for accurate and reliable operational load monitoring. In this paper, a novel two-stage strain gauge location optimization method is proposed to reduce the number of strain gauges while maintaining the operational load monitoring accuracy, which is validated by a numerical case study of an aircraft wing. In the first stage, the traditional Pearson correlation measure is harnessed to initially eliminate numerous correlated strain gauge monitoring points, reducing 996 original strain gauge measurement points to 13 for the aircraft wing box. In the second stage, an improved correlation measure method is proposed to further reduce the 13 strain gauge points to 2, which can evaluate the correlation degree of several variables and simultaneously determine the optimal strain monitoring locations for the two load actuators in this study. The relative errors between the predicted loads and the actual loads for both load actuators are less than 4% when only two optimized monitoring points are adopted. In addition, a comparison study with LASSO regression and principal component regression methods is conducted. The results demonstrate that the proposed method has the characteristics of less monitoring points and higher load prediction precision. Full article
(This article belongs to the Special Issue Fault Diagnosis and Health Monitoring of Mechanical Systems)
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20 pages, 7863 KiB  
Article
Comparison of Crack Detection Performance of Low and High Contact Ratio Spur Gears
by Oguz Dogan
Appl. Sci. 2024, 14(19), 8896; https://doi.org/10.3390/app14198896 - 2 Oct 2024
Viewed by 1185
Abstract
This paper discusses the possible early crack detectability performances of low contact ratio (LCR) and high contact ratio (HCR) spur gears. CAD models of the LCR and HCR gears are created using the rack cutter type tool. Static bending stress analyses are performed [...] Read more.
This paper discusses the possible early crack detectability performances of low contact ratio (LCR) and high contact ratio (HCR) spur gears. CAD models of the LCR and HCR gears are created using the rack cutter type tool. Static bending stress analyses are performed to define the starting point of the cracks and crack propagation analyses are conducted to define the realistic crack paths. Using the healthy and cracked gear geometries, the single tooth stiffness (STS) and the time-varying mesh stiffness (TVMS) of the LCR and HCR gears are calculated numerically. A six degrees of freedom dynamic model of the single-stage gear system is proposed. Dynamic force variation of the LCR and HCR gears are calculated by using the dynamic model. Statistical failure indicators, which are kurtosis, RMS, crest factor, mean, standard deviation, and variance, are used for the determination of the crack in the early stage. The results show that all statistical indicators increase with the increase in the crack length. The statistical indicators increase more for LCR gears than for HCR gears. Although HCR gears have many advantages over LCR gears, it has been determined that their crack detection capacity is lower than that of LCR gears. Full article
(This article belongs to the Special Issue Fault Diagnosis and Health Monitoring of Mechanical Systems)
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24 pages, 17144 KiB  
Article
A New Order Tracking Method for Fault Diagnosis of Gearbox under Non-Stationary Working Conditions Based on In Situ Gravity Acceleration Decomposition
by Yanlei Li, Zhongyang Chen and Liming Wang
Appl. Sci. 2024, 14(11), 4742; https://doi.org/10.3390/app14114742 - 30 May 2024
Viewed by 1207
Abstract
Rotational speed measuring is important in order tracking under non-stational working conditions. However, sometimes, encoders or coded discs are not easy to mount due to the limited measurement environment. In this paper, a new in situ gravity acceleration decomposition method (GAD) is proposed [...] Read more.
Rotational speed measuring is important in order tracking under non-stational working conditions. However, sometimes, encoders or coded discs are not easy to mount due to the limited measurement environment. In this paper, a new in situ gravity acceleration decomposition method (GAD) is proposed for rotational speed estimation, and it is applied in the order tracking scene for fault diagnosis of a gearbox under non-stationary working conditions. In the proposed method, a MEMS accelerometer is locally embedded on the rotating shaft or disc in the tangential direction. The time-varying gravity acceleration component is sensed by the in situ accelerometer during the rotation of the shaft or disc. The GAD method is established to exploit the gravity acceleration component based on the linear-phase finite impulse response (FIR) filter and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) methods. Then, the phase signal of time-varying gravity acceleration is derived for rotational speed estimations. A motor–shaft–disc experimental setup is established to verify the correctness and effectiveness of the proposed method in comparison to a mounted encoder. The results show that both the estimated average and instantaneous rotational speed agree well with the mounted encoder. Furthermore, both the proposed GAD method and the traditional vibration-based tacholess speed estimation methods are applied in the context of order tracking for fault diagnosis of a gearbox. The results demonstrate the superiority of the proposed method in the detection of tooth spalling faults under non-stationary working conditions. Full article
(This article belongs to the Special Issue Fault Diagnosis and Health Monitoring of Mechanical Systems)
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