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Intelligent Fault Diagnosis and Health Detection of Machinery
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Intelligent Fault Diagnosis and Health Detection of Machinery

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 32449

Special Issue Editors

Dr. Xingxing Jiang

E-Mail Website
Guest Editor
School of Rail Transportation, Soochow University, Suzhou 215131, China
Interests: machinery intelligent fault diagnosis; health monitoring of rotating machines; adaptive signal decomposition
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaojian Yi

E-Mail Website
Guest Editor
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100811, China
Interests: system reliability; system design of prognostic and health management; RAMS engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern machinery is usually characterized by a complex electromechanical or mechanical-electro-liquid system. As these systems become increasingly complex, higher standards of reliability and safety are required. To ensure the reliable operation of machines, it has always been an issue of significance to comprehensively and accurately diagnose the latent faults of the machinery. In recent years, a multitude of techniques for intelligent fault diagnosis and health detection of machinery have been developed and described in the literature. This Special Issue welcomes any original and high quality papers dealing with but are not limited to:

(1) Early weak fault detection method of machines;

(2) Advanced signal processing techniques for feature extraction;

(3) Deep learning–based intelligent fault diagnosis of machines;

(4) Fault detection of machines under varying speed conditions;

(5) Health condition monitoring of electromechanical and mechanical-electro-liquid systems;

(6) Reliability analysis and evaluation of electromechanical and mechanical-electro-liquid systems.

Dr. Xingxing Jiang
Dr. Xiaojian Yi
Guest Editors

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Published Papers (17 papers)

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Research

25 pages, 24728 KiB  
Article
Application of Knowledge Graph Technology with Integrated Feature Data in Spacecraft Anomaly Detection
by Xiaojian Yi, Peizheng Huang and Shangjie Che
Appl. Sci. 2023, 13(19), 10905; https://doi.org/10.3390/app131910905 - 30 Sep 2023
Viewed by 2192
Abstract
Given the complexity of spacecraft system structures and functions, existing data-driven methods for anomaly detection face issues of insufficient interpretability and excessive dependence on historical data. To address these challenging problems, this paper proposes a method for applying knowledge graph technology with integrated [...] Read more.
Given the complexity of spacecraft system structures and functions, existing data-driven methods for anomaly detection face issues of insufficient interpretability and excessive dependence on historical data. To address these challenging problems, this paper proposes a method for applying knowledge graph technology with integrated feature data in spacecraft anomaly detection. First, the ontology concepts of the spacecraft equipment knowledge graph are designed according to expert knowledge, and then feature data are extracted from the historical operation data of the spacecraft in various states to build a rich spacecraft equipment knowledge graph. Next, spacecraft anomaly event knowledge graphs are constructed based on various types of anomaly features. During spacecraft operation, telemetry data are matched with the feature data in the knowledge graph, enabling anomaly device location and anomaly cause judgment. Experimental results show that this method, which utilizes spacecraft anomaly prior knowledge for anomaly detection and causes interpretation, has high practicality and efficiency. This research demonstrates the promising application prospects of knowledge graph technology in the field of spacecraft anomaly detection. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Health Detection of Machinery)
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21 pages, 8940 KiB  
Article
A Study of Fault Signal Noise Reduction Based on Improved CEEMDAN-SVD
by Sixia Zhao, Lisha Ma, Liyou Xu, Mengnan Liu and Xiaoliang Chen
Appl. Sci. 2023, 13(19), 10713; https://doi.org/10.3390/app131910713 - 26 Sep 2023
Cited by 4 | Viewed by 1344
Abstract
In light of the challenges posed by the complex structural characteristics and significant coupling of vibration signals in rotating machinery, this study proposes an adaptive noise reduction method called Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Additionally, an enhanced threshold screening [...] Read more.
In light of the challenges posed by the complex structural characteristics and significant coupling of vibration signals in rotating machinery, this study proposes an adaptive noise reduction method called Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Additionally, an enhanced threshold screening Singular Value Decomposition (SVD) algorithm is introduced to address the issues pertaining to noise identification and feature extraction in the context of vibration signals from rotating machinery, which are subjected to complex noise interference. The effectiveness of the proposed approach is substantiated through the evaluation of key metrics, such as the signal-to-noise ratio (SNR), as well as the utilization of advanced signal analysis techniques, including Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT). The experimental results validate the finding that the combination of the improved CEEMDAN and the enhanced threshold screening SVD algorithm effectively reduces noise interference in vibration signals from rotating machinery. This integrated denoising approach successfully preserves the informative characteristics of the vibration signals, thereby laying a foundation for the subsequent fault diagnosis of rotating machinery. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Health Detection of Machinery)
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13 pages, 2730 KiB  
Article
Detection of Broken Bars in Induction Motors Using Histogram Analysis of Current Signals
by Veronica Hernandez-Ramirez, Dora-Luz Almanza-Ojeda, Juan-Jose Cardenas-Cornejo, Jose-Luis Contreras-Hernandez and Mario-Alberto Ibarra-Manzano
Appl. Sci. 2023, 13(14), 8344; https://doi.org/10.3390/app13148344 - 19 Jul 2023
Cited by 6 | Viewed by 1744
Abstract
The lifetime of induction motors can be significantly extended by installing diagnostic systems for monitoring their operating conditions. In particular, detecting broken bar failures in motors is important for avoiding the risk of short circuits or other accidents with serious consequences. In the [...] Read more.
The lifetime of induction motors can be significantly extended by installing diagnostic systems for monitoring their operating conditions. In particular, detecting broken bar failures in motors is important for avoiding the risk of short circuits or other accidents with serious consequences. In the literature, many approaches have been proposed for motor fault detection; however, additional generalized methods based on local and statistical analysis could provide a low-complexity and feasible solution in this field of research. The proposed work presents a methodology for detecting one or two broken rotor bars using the sums and differences histograms (SDH) and machine learning classifiers in this context. From the SDH computed in one phase of the motor’s current, nine texture features are calculated for different displacements. Then, all features are used to train two classifiers and to find the best displacements for faults and health identification in the induction motors. A final experimental evaluation considering the best displacements shows an accuracy of 98.16% for the homogeneity feature and a few signal samples used in a decision tree classifier. Additionally, a polynomial regression curve validates the use of 50 samples to obtain an accuracy of 88.15%, whereas the highest performance is achieved for 250 samples. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Health Detection of Machinery)
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19 pages, 5933 KiB  
Article
A Novel Rolling Bearing Fault Diagnosis Method Based on MFO-Optimized VMD and DE-OSELM
by Yonghua Jiang, Zhuoqi Shi, Chao Tang, Jianan Wei, Cui Xu, Jianfeng Sun, Linjie Zheng and Mingchao Hu
Appl. Sci. 2023, 13(13), 7500; https://doi.org/10.3390/app13137500 - 25 Jun 2023
Cited by 2 | Viewed by 1293
Abstract
Rolling bearings are critical in maintaining smooth operation of rotating machinery and considerably influence its reliability. The signals collected from rolling bearings in field conditions are often subjected to noise, creating a challenge to extract weaker fault features. This paper proposes a rolling [...] Read more.
Rolling bearings are critical in maintaining smooth operation of rotating machinery and considerably influence its reliability. The signals collected from rolling bearings in field conditions are often subjected to noise, creating a challenge to extract weaker fault features. This paper proposes a rolling bearing fault diagnosis method that addresses the above-mentioned problem through the moth-flame optimization algorithm optimized variational mode decomposition (MFO-optimized VMD) and an ensemble differential evolution online sequential extreme learning machine (DE-OSELM). By using the dynamic adaptive weight factor and genetic algorithm cross operator, the optimization accuracy and global optimization ability of the moth-flame optimization (MFO) are improved, and the two basic parameters of VMD decomposition level and quadratic penalty factor are adaptive selected. Since the vibration characteristics of the signal cannot be fully interpreted by a single index, The effective weighted correlation sparsity index (EWCS) is utilized to extract the relevant intrinsic mode functions (IMF) of VMD decomposition and extract their energies as features. In order to improve the classification accuracy, The energy feature set is subsequently inputted into DE-OSELM for training and classification purposes, and the proposed method is assessed via a sample set with four different health states of actual rolling bearings. Our proposed method results are compared with other diagnosis methods, proving its feasibility to diagnose rolling bearing faults with higher classification accuracy. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Health Detection of Machinery)
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19 pages, 36702 KiB  
Article
Unsupervised Method Based on Adversarial Domain Adaptation for Bearing Fault Diagnosis
by Yao Li, Rui Yang and Hongshu Wang
Appl. Sci. 2023, 13(12), 7157; https://doi.org/10.3390/app13127157 - 15 Jun 2023
Viewed by 1516
Abstract
This paper contributes to improving a bottleneck residual block-based feature extractor as a set of layers for transforming raw data into features for classification. This structure is utilized to avoid the issues of the deep learning network, such as overfitting problems and low [...] Read more.
This paper contributes to improving a bottleneck residual block-based feature extractor as a set of layers for transforming raw data into features for classification. This structure is utilized to avoid the issues of the deep learning network, such as overfitting problems and low computational efficiency caused by redundant computation, high dimensionality, and gradient vanishing. With this structure, a domain adversarial neural network (DANN), a domain adversarial unsupervised model, and a maximum classifier discrepancy (MCD), a domain adaptation model, have been applied to conduct a binary classification of fault diagnosis data. In addition, a pseudo-label is applied to MCD for comparison with the original one. In comparison, several popular models are selected for transferability estimation and analysis. The experimental results have shown that DANN and MCD with this improved feature extractor have achieved high classification accuracy, with 96.84% and 100%, respectively. Meanwhile, after using the pseudo-label semi-supervised learning, the average classification accuracy of the MCD model increased by 15%, increasing to 94.19%. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Health Detection of Machinery)
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21 pages, 14593 KiB  
Article
Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor
by Fengxin Ma, Liang Qi, Shuxia Ye, Yuting Chen, Han Xiao and Shankai Li
Appl. Sci. 2023, 13(6), 4064; https://doi.org/10.3390/app13064064 - 22 Mar 2023
Cited by 1 | Viewed by 1559
Abstract
The permanent magnet synchronous motor (PMSM) has been used in electric propulsion and other fields. However, it is prone to the stator winding inter-turn short-circuit, and if no effective measures are taken, the ship’s power system will be paralyzed. To realize intelligent diagnosis [...] Read more.
The permanent magnet synchronous motor (PMSM) has been used in electric propulsion and other fields. However, it is prone to the stator winding inter-turn short-circuit, and if no effective measures are taken, the ship’s power system will be paralyzed. To realize intelligent diagnosis of inter-turn short circuits, this paper proposes an intelligent fault diagnosis method based on improved variational mode decomposition (VMD), multi-scale principal component analysis (PCA) feature extraction, and improved Bi-LSTM. Firstly, the stator current simulation dataset is obtained by using the mathematic model of the inter-turn short-circuit of PMSM, and the parameters of VMD are optimized by the grey wolf algorithm. Then, the data is coarse-grained to obtain multi-scale features, and the main features are selected as the sample data for fault classification by PCA. Subsequently, the Bi-LSTM neural network is used for training and analyzing the data of the sample set and the test set. Finally, the learning rate and the number of hidden-layer nodes of the Bi-LSTM are optimized by the whale algorithm to increase the diagnosis accuracy. Experimental results show that the accuracy of the proposed method for inter-turn short-circuited fault diagnosis is as high as 100%, which confirms the effectiveness of the method. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Health Detection of Machinery)
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16 pages, 1764 KiB  
Article
Reliability Optimization Design of Diesel Engine System Based on the GO Method
by Yuhang Cui, Huina Mu, Xiaojian Yi and Shijie Wei
Appl. Sci. 2023, 13(6), 3727; https://doi.org/10.3390/app13063727 - 15 Mar 2023
Cited by 1 | Viewed by 1607
Abstract
A reliability optimization design method based on the goal-oriented (GO) method is proposed in this study to tackle the problem of engine reliability optimization design. This proposed method considers a V-type diesel engine as the research object. Firstly, the reliability modeling and evaluation [...] Read more.
A reliability optimization design method based on the goal-oriented (GO) method is proposed in this study to tackle the problem of engine reliability optimization design. This proposed method considers a V-type diesel engine as the research object. Firstly, the reliability modeling and evaluation of diesel engines are conducted by the GO method. Secondly, the functional reliability is assigned according to the difference in diesel engine function. Finally, a three-objective diesel reliability optimization design model is constructed with the optimization objectives of maximizing robustness and reliability and minimizing cost. Then, an NSGA-II-PSO hybrid algorithm based on the GO method is designed to handle the problem, and the design interval of unit reliability is obtained. The results of the case study demonstrate that this method not only meets the requirements of reliability design but also achieves the purpose of reliability optimization design, providing a reference for other types of equipment. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Health Detection of Machinery)
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15 pages, 10039 KiB  
Article
Dynamic Simulation Model-Driven Fault Diagnosis Method for Bearing under Missing Fault-Type Samples
by Junqing Ma, Xingxing Jiang, Baokun Han, Jinrui Wang, Zongzhen Zhang and Huaiqian Bao
Appl. Sci. 2023, 13(5), 2857; https://doi.org/10.3390/app13052857 - 23 Feb 2023
Cited by 9 | Viewed by 1861
Abstract
Existing generative adversarial networks (GAN) have potential in data augmentation and in the intelligent fault diagnosis of bearings. However, most relevant studies only focus on the fault diagnosis of rotating machines with sufficient fault-type samples, and some rare fault-type samples may be missing [...] Read more.
Existing generative adversarial networks (GAN) have potential in data augmentation and in the intelligent fault diagnosis of bearings. However, most relevant studies only focus on the fault diagnosis of rotating machines with sufficient fault-type samples, and some rare fault-type samples may be missing in training in practical engineering. To address those deficiencies, this paper presents an intelligent fault diagnosis method based on the dynamic simulation model and Wasserstein generative adversarial network with gradient normalization (WGAN-GN). The dynamic simulation model of bearing faults is constructed to obtaining simulation signals to replace and complement the missing fault samples, which are combined with the measured signals as training data and then input into the proposed WGAN-GN model for expanding and enhancing the data. To test the effectiveness of the simulated samples, a fault classification model constructed by stacked autoencoders (SAE) is used to classify the enhanced dataset. According to the results, the proposed model performs well when used to diagnose faults under missing samples and is preferable to other methods. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Health Detection of Machinery)
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16 pages, 4470 KiB  
Article
An Experimental Setup to Detect the Crack Fault of Asymmetric Rotors Based on a Deep Learning Method
by Chongyu Wang, Zhaoli Zheng, Ding Guo, Tianyuan Liu, Yonghui Xie and Di Zhang
Appl. Sci. 2023, 13(3), 1327; https://doi.org/10.3390/app13031327 - 19 Jan 2023
Cited by 12 | Viewed by 1964
Abstract
Crack is a common fault of rotor systems. The research on crack fault detection methods is mainly divided into numerical and experimental studies. In numerical research, the current fault detection algorithms based on deep learning are mostly applied to bearings and gearboxes, and [...] Read more.
Crack is a common fault of rotor systems. The research on crack fault detection methods is mainly divided into numerical and experimental studies. In numerical research, the current fault detection algorithms based on deep learning are mostly applied to bearings and gearboxes, and there are few studies on rotor fault diagnosis. In experimental research, the rotors used in an experiment are mostly single-span rotors. However, there are complex structures such as multi-span rotor systems in the actual industrial field. Thus, the fault detection algorithms that have been successfully applied on single-span rotors have not been verified on complex rotor systems. To obtain a fault signal close to the actual asymmetric shaft system of an asymmetric rotor system and validate the fault detection method, the crack fault detection platform is designed and built independently. We measure the vibration signals of three channels under five working conditions and establish an intelligent detection method for crack location based on a residual network. The factors that influence fault detection performance are analyzed, and the influence laws are discussed. Results show that the accuracy and anti-noise performance of the proposed method are higher than those of the commonly used machine learning. The average accuracy is 100% when SNR (signal-to-noise ratio) is greater than or equal to −2 dB, and the average accuracy is 98.2% when SNR is −4 dB. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Health Detection of Machinery)
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15 pages, 4887 KiB  
Article
Blade Crack Diagnosis Based on Blade Tip Timing and Convolution Neural Networks
by Guangya Zhu, Chongyu Wang, Wei Zhao, Yonghui Xie, Ding Guo and Di Zhang
Appl. Sci. 2023, 13(2), 1102; https://doi.org/10.3390/app13021102 - 13 Jan 2023
Cited by 3 | Viewed by 2258
Abstract
The diagnosis of blade crack faults is critical to ensuring the safety of turbomachinery. Blade tip timing (BTT) is a non-contact vibration displacement measurement technique, which has been extensively studied for blade vibration condition monitoring recently. The fault diagnosis methods based on deep [...] Read more.
The diagnosis of blade crack faults is critical to ensuring the safety of turbomachinery. Blade tip timing (BTT) is a non-contact vibration displacement measurement technique, which has been extensively studied for blade vibration condition monitoring recently. The fault diagnosis methods based on deep learning can be summarized as studying the internal logical relationship of data, automatically mining features, and intelligently identifying faults. This research proposes a crack fault diagnostic method based on BTT measurement data and convolutional neural networks (CNNs) for the crack fault detection of blades. There are two main aspects: the numerical analysis of the rotating blade crack fault diagnosis and the experimental research in rotating blade crack fault diagnosis. The results show that the method outperforms many other traditional machine learning models in both numerical models and tests for diagnosing the depth and location of blade cracks. The findings of this study contribute to the real-time online crack fault diagnosis of blades. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Health Detection of Machinery)
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20 pages, 9361 KiB  
Article
Research on Interference Mechanism of 25 Hz Phase Sensitive Track Equipment from Unbalanced Current
by Lei Wang, Benchao Zhu, Jingcao Chen, Mingchao Zhou, Zhengyan Liu, Jinyan Li and Chunmei Xu
Appl. Sci. 2023, 13(2), 1033; https://doi.org/10.3390/app13021033 - 12 Jan 2023
Cited by 1 | Viewed by 1698
Abstract
In a 25-Hz phase-sensitive track circuit, traction backflow is unevenly distributed in the two rails, resulting in interference caused by the 50 Hz unbalanced current, which leads to misoperation of relays and other equipment in the circuit. Focusing on the mechanism of unbalanced [...] Read more.
In a 25-Hz phase-sensitive track circuit, traction backflow is unevenly distributed in the two rails, resulting in interference caused by the 50 Hz unbalanced current, which leads to misoperation of relays and other equipment in the circuit. Focusing on the mechanism of unbalanced current generation, this paper probes the causes of track circuit equipment interference and innovatively analyzes the mechanism of the choke transformer and relay affected, in order to find a method to suppress the interference of the 25 Hz phase-sensitive track equipment. Firstly, the mechanism of unbalanced current generation is explained, and the influence of the unbalanced impulse current on the choke transformer and binary two-bit relay is analyzed. Secondly, the DC magnetic bias, the second side voltage of the choke transformer and the excitation current, flux density, core loss of choke transformer and relay under a different unbalance impulse current are simulated. Then, the unbalanced current simulation test, unbalanced current test during driving and grounding wire test are carried out. Finally, it is concluded that the unbalanced impulse current causes magnetic saturation of the choke transformer, then affects voltage sag of the relay coil, resulting in misoperation of equipment. The conclusions of this paper can play an important guiding role in studying the influence of unbalanced current and restraining the interference of the 25 Hz phase-sensitive track circuit. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Health Detection of Machinery)
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19 pages, 4046 KiB  
Article
Feature Mining and Sensitivity Analysis with Adaptive Sparse Attention for Bearing Fault Diagnosis
by Qinglei Jiang, Binbin Bao, Xiuqun Hou, Anzheng Huang, Jiajie Jiang and Zhiwei Mao
Appl. Sci. 2023, 13(2), 718; https://doi.org/10.3390/app13020718 - 4 Jan 2023
Cited by 8 | Viewed by 2042
Abstract
Bearing fault diagnosis for equipment-safe operation has a crucial role. In recent years, more achievements have been made in bearing fault diagnosis. However, for the fault diagnosis model, the representation and sensitivity of bearing fault features have a great influence on the diagnosis [...] Read more.
Bearing fault diagnosis for equipment-safe operation has a crucial role. In recent years, more achievements have been made in bearing fault diagnosis. However, for the fault diagnosis model, the representation and sensitivity of bearing fault features have a great influence on the diagnosis output results; thus, the attention mechanism is particularly important for the selection of features. However, global attention focuses on all sequences, which is computationally expensive and not ideal for fault diagnosis tasks. The local attention mechanism ignores the relationship between non-adjacent sequences. To address the respective shortcomings of global attention and local attention, an adaptive sparse attention network is proposed in this paper to filter fault-sensitive information by soft threshold filtering. In addition, the effects of different signal representation domains on fault diagnosis results are investigated to filter out signal representation forms with better performance. Finally, the proposed adaptive sparse attention network is applied to cross-working conditions diagnosis of bearings. The adaptive sparse attention mechanism focuses on the signal characteristics of different frequency bands for different fault types. The proposed network model achieves better overall performance when comparing the cross-conditions diagnosis accuracy and model convergence speed. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Health Detection of Machinery)
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18 pages, 4506 KiB  
Article
Structural Damage Detection Based on One-Dimensional Convolutional Neural Network
by Zhigang Xue, Chenxu Xu and Dongdong Wen
Appl. Sci. 2023, 13(1), 140; https://doi.org/10.3390/app13010140 - 22 Dec 2022
Cited by 4 | Viewed by 1962
Abstract
This paper proposes a structural damage detection method based on one-dimensional convolutional neural network (CNN). The method can automatically extract features from data to detect structural damage. First, a three-layer framework model was designed. Second, the displacement data of each node was collected [...] Read more.
This paper proposes a structural damage detection method based on one-dimensional convolutional neural network (CNN). The method can automatically extract features from data to detect structural damage. First, a three-layer framework model was designed. Second, the displacement data of each node was collected under the environmental excitation. Then, the data was transformed into the interlayer displacement to form a damage dataset. Third, in order to verify the feasibility of the proposed method, the damage datasets were divided into three categories: single damage dataset, multiple damage dataset, and damage degree dataset. The three types of damage dataset can be classified by the convolutional neural network. The results showed that the recognition accuracy is above 0.9274. Thereafter, a visualization tool called “t-SNE” was employed to visualize the raw data and the output data of the convolutional neural network. The results showed that the feature extraction ability of CNN is excellent. However, there are many hidden layers in a CNN. The outputs of these hidden layers are invisible. In the last section, the outputs of hidden layers are visualized to understand how the convolutional neural networks work. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Health Detection of Machinery)
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18 pages, 12233 KiB  
Article
An Accelerated Degradation Durability Evaluation Model for the Turbine Impeller of a Turbine Based on a Genetic Algorithms Back-Propagation Neural Network
by Xiaojian Yi, Zhezhe Wang, Shulin Liu, Xinrong Hou and Qing Tang
Appl. Sci. 2022, 12(18), 9302; https://doi.org/10.3390/app12189302 - 16 Sep 2022
Cited by 3 | Viewed by 1570
Abstract
Durability evaluation plays an important role in product operation and maintenance during the design stage. In order to ensure a long life, high reliability, and short development cycle, an accelerated degradation durability evaluation model for the turbine impeller of a turbine based on [...] Read more.
Durability evaluation plays an important role in product operation and maintenance during the design stage. In order to ensure a long life, high reliability, and short development cycle, an accelerated degradation durability evaluation model for the turbine impeller of a turbine based on a genetic algorithms back-propagation neural network is established. Based on the proposed model, we discuss two types of practical problems. One is the matching problem of the component strengthening test and whole machine system test. The other is the design problem of two kinds of bench tests. All in all, this work not only proposes a durability evaluation model to effectively solve the current turbine durability evaluation problems, but it also provides a feasible research idea for similar problems. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Health Detection of Machinery)
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15 pages, 3383 KiB  
Article
Data Augmentation in 2D Feature Space for Intelligent Weak Fault Diagnosis of Planetary Gearbox Bearing
by Rui Yang, Zenghui An, Weiling Huang and Rijun Wang
Appl. Sci. 2022, 12(17), 8414; https://doi.org/10.3390/app12178414 - 23 Aug 2022
Cited by 3 | Viewed by 1569
Abstract
Quickly detecting and accurately diagnosing early bearing faults is the key to ensuring the stable operation of high-precision equipment. In actual industrial applications, it is common to face the issues of big data and poor fault identification accuracy. To accurately and automatically realize [...] Read more.
Quickly detecting and accurately diagnosing early bearing faults is the key to ensuring the stable operation of high-precision equipment. In actual industrial applications, it is common to face the issues of big data and poor fault identification accuracy. To accurately and automatically realize the diagnostics of rolling bearings, a convolutional neural network algorithm and fault feature enhancement method is proposed. A two-dimensional space feature extraction method based on the Cyclostationary theory and wavelet transform shows good results in noise suppression. Firstly, the cyclic demodulation of wavelet transform coefficients is performed on bearing vibration signals to convert one-dimensional vibration data into a two-dimensional spectrogram for enhancing the weak fault feature. Secondly, the image segmentation theory is introduced, which can obtain more data and improve the calculation accuracy and efficiency on the basis of data dimension reduction. Finally, the augmented 2D spectrograms are inputted into a convolutional neural network. Through the analysis of the actual planetary gearbox bearing data, and compared with other mainstream intelligence algorithms, the effectiveness and superiority of this method are verified. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Health Detection of Machinery)
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26 pages, 3837 KiB  
Article
Reliability Modeling and Analysis of a Diesel Engine Design Phase Based on 4F Integration Technology
by Meng Zhang, Shuangfeng Liu, Xinrong Hou, Haiping Dong, Chunsheng Cui and Yafen Li
Appl. Sci. 2022, 12(13), 6513; https://doi.org/10.3390/app12136513 - 27 Jun 2022
Cited by 7 | Viewed by 2452
Abstract
As one of the most important components within a vehicle, diesel engines have high requirements for reliability due to the harsh operating environments. However, previous studies have mainly focused on the reliability assessment of diesel engines, while less research has been conducted on [...] Read more.
As one of the most important components within a vehicle, diesel engines have high requirements for reliability due to the harsh operating environments. However, previous studies have mainly focused on the reliability assessment of diesel engines, while less research has been conducted on the modeling of the diesel engine reliability analysis and its management. For this reason, this paper proposes a comprehensive method for reliability analysis and its management based on the use of 4F integration technology in the early stages of diesel engine design. First of all, an expert group used FEMCA (failure mode, effects and criticality analysis) and FHA (functional hazard analysis) to find the most harmful level of fault mode. At the same time, a new method for the repair of dynamic fault trees to find the weak links at the component level was developed. Finally, a FRACAS (fracture report analysis and corrective action system) was used during the above analysis process. By applying this method to the reliability assessment of a diesel engine in the design stage, the problems of failure information feedback and the reuse of failure information in the actual reliability assessment can be solved. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Health Detection of Machinery)
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19 pages, 3327 KiB  
Article
Rolling Bearing Fault Diagnosis Based on Time-Frequency Compression Fusion and Residual Time-Frequency Mixed Attention Network
by Guodong Sun, Xiong Yang, Chenyun Xiong, Ye Hu and Moyun Liu
Appl. Sci. 2022, 12(10), 4831; https://doi.org/10.3390/app12104831 - 10 May 2022
Cited by 4 | Viewed by 2238
Abstract
The traditional rolling bearing diagnosis algorithms have problems such as insufficient information on time-frequency images and poor feature extraction ability of the diagnosis model. These problems limit the improvement of diagnosis performance. In this article, the input of the time-frequency image and intelligent [...] Read more.
The traditional rolling bearing diagnosis algorithms have problems such as insufficient information on time-frequency images and poor feature extraction ability of the diagnosis model. These problems limit the improvement of diagnosis performance. In this article, the input of the time-frequency image and intelligent diagnosis algorithms are optimized. Firstly, the characteristics of two advanced time-frequency analysis algorithms are deeply analyzed, i.e., multisynchrosqueezing transform (MSST) and time-reassigned multisynchrosqueezing transform (TMSST). Then, we propose time-frequency compression fusion (TFCF) and a residual time-frequency mixed attention network (RTFANet). Among them, TFCF superposes and splices two time-frequency images to form dual-channel images, which can fully play the characteristics of multi-channel feature fusion of the convolutional kernel in the convolutional neural network. RTFANet assigns attention weight to the channels, time and frequency of time-frequency images, making the model pay attention to crucial time-frequency information. Meanwhile, the residual connection is introduced in the process of attention weight distribution to reduce the information loss of feature mapping. Experimental results show that the method converges after seven epochs, with a fast convergence rate and a recognition rate of 99.86%. Compared with other methods, the proposed method has better robustness and precision. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Health Detection of Machinery)
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