Hummingbirds Research Articles

Train Bearing Fault Diagnosis Based on Time–Frequency Signal Contrastive Domain Share CNN

Train Bearing Fault Diagnosis Based on Time–Frequency Signal Contrastive Domain Share CNN

Adaptive stepsize forward–backward pursuit and acoustic emission-based health state assessment of high-speed train bearings

Compressed sensing (CS) is a promising tool for data compression reconstruction. However, fault diagnosis methods for high-speed train bearings based on CS and acoustic emission (AE) technologies have not been reported yet. Notably, the accuracy and speed of CS two-stage reconstruction methods are affected and restricted by prior initial conditions. Therefore, this article proposes adaptive dynamic thresholds applicable to adaptive stepsize forward–backward pursuit (ASFBP), and bearing health state assessment method. First, the adaptive dynamic thresholds for atom selection and deletion are constructed based on the residual feedback mechanism and the atom quality distribution law, which enables ASFBP to realize high-precision rapid reconstruction of signal without any atom priori initial conditions. Second, the initial dictionary length is improved based on the AE hit characteristics. Furthermore, a damage state comprehensive evaluation index (DSCEI) is established using principal component analysis based on AE time-domain hit parameters and compression-domain energy parameter. Compared with the kurtosis index and permutation entropy index, the DSCEI demonstrates better monotonicity and stability in the quantitative evaluation of high-speed train bearing condition. Finally, the validity and stability of the method are verified by testing under complex test conditions resembling actual high-speed train lines, providing valuable insights for the CS-based data-driven bearing fault diagnosis.

Fatigue failure mechanism of high-speed train bearing steel after long-term service

The remaining useful life prediction of the bearings after long-term service is a crucial issue. Exploring the fatigue failure mechanism of bearing is helpful to grasp the dynamic evolution law during its whole period. In this paper, the tensile and fatigue property degradation law of bearings for high-speed train under different service mileages were explored. The results display that there is a downward trend for the tensile/fatigue strength and elongation of the bearing as prolonging its service mileage. The area proportion of dimples declines while that of cleavage plane increases in bearing steel after long-term service. The variations of secondary crack length and equivalent diameters of cleavage plane indicate that more cracks initiated in damaged bearing under cyclic loading. Based on the energy principle, the fatigue failure mechanism of bearings after long-term service was elaborated with the plastic deformation, crack initiation and growth resistance. The results would provide theoretical reference for the fatigue property degradation law of high-speed train bearings.

RTSMFFDE-HKRR: A fault diagnosis method for train bearing in noise environment

The bearings have been exposed to a noisy environment for an extended period, making it challenging to identify fault characteristics accurately and resulting in low accuracy. In this study, we proposed a technique for diagnosing train-bearing faults in noisy environments using refined time-shift multiscale fractional order fuzzy dispersion entropy and hybrid kernel ridge regression (RTSMFFDE-HKRR) to enhance the precision of fault detection. Firstly, RTSMFFDE is proposed based on multiscale fuzzy dispersion entropy by combining the theories of refinement, time-shifting, and fractional order. Revising the refinement process helps to stabilize the entropy value when dealing with a large scale factor. Implementing time-shifting techniques can help preserve the original signal features more effectively. The theory of fractional order strengthens the noise-resistant performance of the algorithm, and the proposed RTSMFFDE has a superior feature extraction capability. Subsequently, a hybrid kernel function is formulated through the combination of the radial basis kernel function (RBF) and the linear kernel function, aiming to improve the nonlinear mapping ability and anti-noise ability of ridge regression classification algorithm. Finally, two sets of experimental cases were used to verify the proposed RTSMFFDE-HKRR. The results show that the fault feature information extracted by RTSMFFDE in noisy environment is more comprehensive, and the classification effect of HKRR is more significant. In the fault diagnosis of two bearing simulation test beds, the accuracy rate of RTSMFFDE-HKRR is up to 100%. In the noisy environment, the accuracy rate is 98.84% and 97.32%, which is much higher than other diagnostic models. RTSMFFDE-HKRR is suitable for train bearing fault diagnosis in noisy environment.

Quantum error-correction using humming sparrow optimization based self-adaptive deep cnn noise correction module

The error correction model’s main purpose in heavy hexagonal quantum codes is to improve their reliability for quantum computing applications. Existing challenges include finding the optimal decoder for quantum error correction in heavy hexagonal codes. This research propels the frontier of quantum error correction, with a specific focus on tailoring topological quantum error-correcting codes for the unique challenges posed by superconducting qubits in quantum computers. In response, this research harnesses the power of deep learning, presenting a Humming sparrow optimization based self-adaptive deep CNN (HSO-based SADCNN) model designed for heavy hexagonal codes. This decoder incorporates a Self-adaptive Deep CNN (SADCNN) Noise Correction Module, a sophisticated component to refine error correction. The proposed decoder’s efficacy is rigorously evaluated across varying code distances (three, five, and seven) using the Humming Sparrow Optimization (HSO) algorithm. HSO, intricately designed to fine-tune the SADCNN decoder, significantly enhances its error correction capabilities for heavy hexagonal quantum codes. The algorithm seamlessly integrates advantageous characteristics of herding and tracing from Humming Bird optimization and Sparrow search optimization, representing a critical stride in advancing the reliability of quantum computing applications, particularly within the intricate domain of heavy hexagonal quantum codes. Based upon the achievements, the Training Percentage (TP) 90 metrics demonstrate significant progress, boasting a commendable accuracy of 97.35%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$97.35\\%$$\\end{document}, coupled with reduced logical error probability and a diminished bit error rate, marked at 5.51 and 3.72, respectively.

Improved metric-based meta learning with attention mechanism for few-shot cross-domain train bearing fault diagnosis

Traction motor bearings, as a crucial component of subway trains, play a pivotal role in ensuring the safety of train operations. Therefore, intelligent diagnosis of train bearings holds significant importance. However, due to the complex and dynamic nature of bearing conditions coupled with limited fault data availability, traditional diagnostic methods fail to yield satisfactory results. To address this issue, we propose an improved metrics-based meta-learning approach for accurate few-shot cross-domain fault diagnosis of train bearings. Firstly, we introduce a 1D-signal channel attention mechanism that effectively extracts latent features and enhances recognition accuracy. Secondly, by incorporating the Adabound algorithm into our model framework, we further enhance its classification performance. Finally, through several case studies, we demonstrate the effectiveness of our proposed method in comparison to other approaches within similar settings.

Ensefgram: An optimal demodulation band selection method for the early fault diagnosis of high-speed train bearings

Early fault diagnosis of high-speed train bearings under road spectrum shocks is challenging. As a conventional narrowband demodulation method, the Fast Kurtogram (FK) class of algorithms have some drawbacks: the fixed-band segmentation strategy makes it difficult for the method to find the optimal demodulation frequency band (ODFB); other major limitations are the poor robustness of the statistical index of the blind algorithm and the poor adaptiveness of the target algorithm given the need to the know the bearing operating state in advance. To address these issues, this paper proposes Ensefgram, an early fault detection method for high-speed train bearings with the acoustic emission signal as an example. First, a filter bank is designed by combining the spectral trend with the median dichotomy strategy, which adaptively segments the frequency band and retains the integrity of the sub-band fault components to the maximum extent. In addition, a novel statistical index, termed the maximum weighted spectral energy frequency factor, is proposed, and it is combined with the envelope spectrum negentropy (ES-negentropy) to form a composite index ENSEF, which can select the ODFB blindly. Finally, the proposed method was validated by simulation and applying test signals close to the real working conditions of a high-speed train. Compared with FK, Infogram, and other methods, Ensefgram provides more accurate detection results for the early fault diagnosis of bearings.

An automatic college library book recommendation system using optimized Hidden Markov based weighted fuzzy ranking model

An automated book recommendation system has becoming a necessary role in increasing the efficacy and user experience of college library. The number of books in the library is very large, and it is difficult for students to choose the appropriate book from each department efficiently. The manual selection of books is time-consuming, and an automatic library book recommendation system is highly required. Therefore, this research proposes a novel ranking-based hybrid recommendation system for assisting each student in different department to select required books with minimal time. Initially, pre-processing is done to label each department name, which can effectively reduce the time complexity. Then, weighting is provided for each book using a timestamp based on the days the book was issued between the date of issue and return. After, Pearson correlation coefficient (PCC) is computed to find the similarity between the department and the corresponding books. Finally, the Hidden Markov-assisted Chaotic Artificial Humming Bird with Discriminant Analysis and weighted fuzzy ranking (HMCAHB_DA-WFR) model is proposed to classify the books according to the department accurately based on the generated ranks and provides better recommendation to the students. The proposed method is implemented in PYTHON software, and a real-time dataset is utilized from Bhilai Institute of Technology, Durg. The proposed method obtains a better accuracy of 99.2%, Kappa of 97.6%, MAE of 0.074, NMCC of 98.7%, FPR of 0.0085% and FNR of 0.034%. The attained simulation results prove the efficiency of proposed method over other existing methods.

An adaptive protection method with directional overcurrent relay coordination using hybrid chaotic artificial humming bird optimization

An adaptive protection method with directional overcurrent relay coordination using hybrid chaotic artificial humming bird optimization

A multi-layer feature fusion fault diagnosis method for train bearings under noise and variable load working conditions

The working characteristics of noise and variable load conditions make it challenging to extract the feature of train bearing vibration signal. Therefore, a multi-layer feature fusion inverted residual network (MFIRN) is proposed. Firstly, a joint shrinkage denoising module (JSDM) is proposed, and an inverted residual denoising module (IRDM) is designed by combining the JSDM with the inverted residual network. The IRDM is used as the basic unit to improve the anti-noise performance of MFIRN. Then, a global interactive awareness module (GIAM) is designed to perceive the information of different layers, which can effectively extract the fault characteristics of bearing signals under variable load conditions. Finally, the fault diagnosis capabilities of the MFIRN under noisy and variable load conditions are tested using two cases, and the effectiveness of the JSDM, GIAM and IRDM is verified by ablation experiments. The experimental results demonstrate that, compared with seven recent models, the MFIRN can filter out the interference information more effectively under different noise conditions. Furthermore, MFIRN can effectively obtain the correlation characteristics between different load signals with better diagnostic accuracy and domain adaptability, showing potential for practical application.

Analysis Evaluation of Resource Value Effects Territorial Defense by Broad-Tailed and Rufous Humming Birds

Hummingbirds of the species Selasphorous platycercus and Selasphorous rufus were studied in Colorado, where they displayed territorial behaviour at feeders containing 10%, 20%, and 30% sucrose solutions. At each of the three energy availability levels, the number of invaders, the number of territory owners, and the level of Defence were recorded. After being forced out of their breeding area by Rufous Hummingbirds, Broad-tailed Hummingbirds only bother to defend locations of inferior quality. Both Wide-tailed and Rufous hummingbirds, while keeping tabs on important locations, resort to more flamboyant modes of behaviour, such as extended pursuits augmented with chip cries and aimless wing flapping. According to certain probes, a chase is ready to start if chip calls and hovering are any indication. However, I learned that when an intruder was present, it was only natural to want to get rid of them. Hovering and chip calls were utilised to increase the intensity of the chase. Broad-tailed Hummingbirds were less likely than Rufous Hummingbirds to make chip sounds or hover without giving pursuit.

Study on dynamic characteristics of urban rail transit vehicle considering faulty axle-box bearings under variable speeds

Axle-box bearing is the key component of urban rail transit vehicles, and its performance status seriously affects the operation safety and stability of the train. Therefore, analyzing the dynamic response of a vehicle system by considering axle-box bearings is the premise of ensuring its safety. Besides, vehicles generally run under the condition of variable speeds, which leads to more complex dynamic response for the vehicle system. Accordingly, studying the influence of faulty axle-box bearings on the vehicle system under variable speeds can reflect the running situation of trains more realistically. In this paper, a dynamics model of axle-box bearings is considered in the vehicle-track coupling model, and the dynamic response of the vehicle system with faulty bearings under variable speeds is analyzed. Firstly, a vehicle-track coupling dynamics model considering axle-box bearings is established, which considers the coupling effect between axle-box bearings and other vehicle components. The bearing inner race is fixedly connected with the axle shaft, and the bearing outer race is fixedly connected with the axle-box housing. Then, an outer race defect model and an inner race defect model of axle-box bearings are respectively established to simulate the internal fault excitation which is often generated in the running process of train bearings. Finally, considering faulty axle-box bearings and variable speeds, the dynamic response of the vehicle system is simulated by the proposed model, whose effectiveness is validated through the theoretical analysis and field tests. The test results show that the axle-box bearing with an outer or inner race defect has a certain influence on the dynamic response of the vehicle system under variable speeds.

Tri-SeizureDualNet: A novel multimodal brain seizure detection using triple stream skipped feature extraction module entrenched dual parallel attention transformer

The timely and accurate detection of epileptic seizures is highly needed to enhance the quality of a patient’s life. The state-of-the-art works to design and utilize many deep learning techniques to detect and predict seizures using EEG, fMRI, and a combination of EEG and fMRI modalities respectively. Whereas the existing models are highly vulnerable to complexity, and overfitting issues due to improper feature analysis. To this end, we design a novel seizure detection model named Triple stream skipped feature extraction module and Dual parallel attention transformer Network (Tri-SeizureDualNet) using EEG and fMRI modalities. The major scope of this research is to enhance the detection accuracy of seizures with better performance. The data acquired for this research is from the CHB-MIT database for EEG and the UW Madison database for fMRI. The designed model tends to perform feature extraction, feature selection, feature engineering, and classification respectively with high feature extraction accuracy and less complexity. Beforehand, we perform pre-processing on both the EEG and fMRI data using ICA, PCA, and high pass filtering. Followed by we perform feature extraction in three streams with skip connections from both modalities to improve the extractor capability and feature extraction accuracy. The extracted features are selected in the feature selection module using the Humming Bird Optimization (HBO) algorithm based on the feature importance ratio. The selected features from both modalities were fused and provided to the Dual Parallel Attention Transformer (DPAT) for feature engineering and context learning respectively. The utilized DPAT is composed of two regularized attention modules with a gating mechanism to picture the feature similarity rate. At last, the softmax layer classifies the features into three classes such as focal onset, general onset, and unknown onset seizures respectively. The experimental results were conducted with two utilized datasets such as CHB-MIT and UW Madison datasets separately for EEG and fMRI data in which the proposed work achieves an overall accuracy rate of 99.67% and AUC rate of 0.989 respectively. The results reveal that the proposed research outperforms the existing works.

Study on the Extraction Method for Track-Side Acoustic Features Based on Cyclic Stationary Analysis

Because of its non-contact measurement characteristics, trackside acoustic technology is now utilized for train bearing fault diagnosis. However, the collected acoustic signal produces Doppler distortions that can impact the accuracy of bearing fault diagnosis. Additionally, when a fault occurs in the train bearing, it is analyzed using cyclostationary methods. In this study, we combine bearing fault characteristics with Doppler distortion correction and cyclostationary analysis methods. The trackside acoustic test platform is employed to collect and test the fault signals from bearings. These signals are processed and analyzed using Doppler distortion correction algorithms and cyclostationary techniques. A comparison between time domain maps and power spectrum maps before and after correction reveals an increase in SNR (signal to noise ratio) and a more concentrated energy distribution within the fault signals—at least a 50% improvement is observed. To further validate our method’s effectiveness, we select existing TADS equipment from a depot to collect bearing signals for analysis and processing using our proposed bearing fault diagnosis method. Comparison of time domain maps and power spectrum maps before and after correction shows clearer overall images and amplitude increase of nearly 125%. Therefore, we have successfully developed a stepwise method for bearing fault diagnosis based on cyclostationary Doppler distortion correction.

Dual Classifier-Discriminator Adversarial Networks for Open Set Fault Diagnosis of Train Bearings

Dual Classifier-Discriminator Adversarial Networks for Open Set Fault Diagnosis of Train Bearings

Personalized fault diagnosis of rolling bearings in trains based on digital twin

Applying methods such as deep learning improves the efficiency of bearing fault diagnosis and reduces trains’ operation and maintenance costs. However, in practical applications, the deficiency of historical data and the imbalance of data types often limit the effectiveness of the diagnosis. The variability between operating conditions also restricts the availability of transfer learning including domain adaptation. To address this challenge, a digital twin (DT) framework is established to fill the data for train fault diagnosis. A train bearing dynamics model is optimized using virtual-reality mapping in the DT framework with measured health data as a baseline to generate data closer to reality. Finally, the fault diagnosis uses a hybrid dataset that mixes measured and simulated data as a source domain for transfer learning. The Case Western Reserve University dataset is used as an example, and the accuracy reaches up to 99.40%, which verifies the method’s effectiveness.

Parameter estimation of solar PV models with artificial humming bird optimization algorithm using various objective functions

Parameter estimation of solar PV models with artificial humming bird optimization algorithm using various objective functions

Fingerprint feature and dynamic threshold mechanism based on acoustic emission for bearing fault detection

The acoustic emission (AE) technology has emerged as a promising diagnostic tool for the damage detection of bearings. However, the conventional time-domain feature extraction based on AE hits lacks adequate consideration of the periodicity related to faults. To address this issue, taking the outer ring of a bearing as an example and studying the periodic AE hit behavior of bearings, a condition monitoring method, called fingerprint feature (FPF), is established in this study, providing a visual mode for bearing fault detection, along with the ability to track the bearing state. Since the dynamic threshold is essential to the formation of FPF, the dynamic threshold mechanism is investigated in detail, and a threshold coefficient Rt is defined to obtain the adaptive dynamic threshold, which allows instant AE hit extraction. The proposed distributed aggregation index can not only optimize Rt but also help evaluate the damage state of bearings. In addition, based on the FPF, a fingerprint fault spectrum (FFS) is proposed by associating the hit statistics per unit time with the fault characteristic frequency. Finally, the validity of the proposed method is proved by Hilbert envelope demodulation and its capability and practicality are verified using test rig data of rolling bearings and high-speed train bearings that operate close to actual working conditions. This study provides valuable insights into the development of online condition monitoring for bearings in industrial environment.

A new weak fault diagnosis approach for train bearings based on improved grey wolf optimizer and adaptive variational mode decomposition

Accurately identifying the health status of train running gear bearings is crucial to ensure the quality of operation. As the early fault information of bearings is weak and submerged in the complex noise environment, which is difficult to diagnose. Therefore, a new weak fault diagnosis approach for train running gear bearings based on variational mode decomposition (VMD) with improved performance and refined weighted kurtosis (RWK) index is proposed to solve this problem. First, an improved grey wolf optimizer (IGWO) based on a variety of strategies is proposed. Secondly, the VMD performance is improved using the IGWO algorithm, and the improved VMD is used to process the early weak signals of bearings. A new fault-sensitive index called the RWK is proposed to detect the mode with the most fault information. Finally, the envelope analysis of the characteristic signals is performed to achieve the early weak fault diagnosis of bearings. Compared with the other nine optimization algorithms, the IGWO algorithm has strong optimization ability, stable performance and a fast convergence speed. Four cases verify that the RWK index has the highest sensitivity to fault information and can more effectively filter out modal components containing rich fault information than the comparison methods.

Financial Fraud Detection using Improved Artificial Humming Bird Algorithm with Modified Extreme Learning Machine

More and more industries, including the financial sector, are moving their operations online as internet usage continues to rise at an exponential rate. As a result, financial fraud is on the rise in all its guises and in all parts of the world, causing enormous economic damage. The purpose of financial fraud detection systems is to identify potential dangers, such as unauthorised access or unusual attacks. In recent years, this problem has been attacked using a variety of machine learning and data mining methods. Aalgorithms, on the other hand, are better able to deal with only a small quantity of labelled data and a large amount of unlabeled data, making them useful in situations where it would be impractical to rely solely on supervised learning algorithms to train a good-performing classifier. In this research, we propose a Semi-supervised Extreme Learning Machine (SKELM) built on top of the weighted kernel, which we call SELMWK. For the purpose of detecting financial fraud, this research proposes an enhanced artificial hummingbird algorithm (IAHA). The algorithm combines two essential techniques to enhance its capacity for optimisation. To begin, the Chebyshev chaotic map is used to seed the first population of artificial hummingbirds, which boosts the population's overall ability to do global searches. Second, the guided foraging phase incorporates the Levy flight to enlarge the search field and forestall early convergence. The experimental results demonstration that the suggested technique recovers the Internet monetary fraud detections.