Vibration Signals Research Articles

Sound and vibration control crocodile hatching

Abstract The birth of a baby or the hatching of an egg are moments when the risk of predation is particularly high. In oviparous species where clutches contain many eggs, one strategy for limiting predation is based on mechanisms that synchronise hatching, which reduces the individual risk of being targeted and may allow hatchlings to emerge under parental protection. These mechanisms involve communication signals between eggs and between eggs and parents. While previous studies in various species have shown the importance of sound and vibration signals in these exchanges, how embryos integrate this information remains poorly understood. Here we show that crocodile embryos respond differently to calls emitted by siblings (hatching calls) and to vibrations mimicking parental intervention (rubbing and scratching the shell) or the presence of a predator, suggesting that these two stimuli carry different information. Through playback experiments, we confirm that hatching calls elicit vocal responses and synchronous hatching in mature embryos, while embryos hatch earlier when they perceive vibrations. Our study underlines that the control of hatching by external factors can rely on differential apprehension of multiple sensory inputs by embryos.

A hybrid LSTM random forest model with grey wolf optimization for enhanced detection of multiple bearing faults

Bearing degradation is the primary cause of electrical machine failures, making reliable condition monitoring essential to prevent breakdowns. This paper presents a novel hybrid model for the detection of multiple faults in bearings, combining Long Short-Term Memory (LSTM) networks with random forest (RF) classifiers, further enhanced by the Grey Wolf Optimization (GWO) algorithm. The proposed approach is structured in three stages: first, time and frequency domain features are manually extracted from vibration signals; second, these features are processed by a dual-layer LSTM network, which is specifically designed to capture complex temporal relationships within the data; finally, the GWO algorithm is employed to optimize feature selection from the LSTM outputs, feeding the most relevant features into the RF classifier for fault classification. The model was rigorously evaluated using a dataset comprising six distinct bearing health conditions: healthy, outer race fault, ball fault, inner race fault, compounded fault, and generalized degradation. The hybrid LSTM-RF-GWO model achieved a remarkable classification accuracy of 98.97%, significantly outperforming standalone models such as LSTM (93.56%) and RF (98.44%). Furthermore, the inclusion of GWO led to an additional accuracy improvement of 0.39% compared to the hybrid LSTM-RF model without optimization. Other performance metrics, including precision, kappa coefficient, false negative rate (FNR), and false positive rate (FPR), were also improved, with precision reaching 99.28% and the kappa coefficient achieving 99.13%. The FNR and FPR were reduced to 0.0071 and 0.0015, respectively, underscoring the model’s effectiveness in minimizing misclassifications. The experimental results demonstrate that the proposed hybrid LSTM-RF-GWO framework not only enhances fault detection accuracy but also provides a robust solution for distinguishing between closely related fault conditions, making it a valuable tool for predictive maintenance in industrial applications.

Batch channel normalized-CWGAN with Swin Transformer for imbalanced data fault diagnosis of rotating machinery

Abstract In real scenarios, rotating machinery is mainly operated in optimal condition, leading to fault data scarce and difficult to collect. This issue results in imbalanced data, significantly limiting the effectiveness of intelligent fault diagnosis methods. To address this issue, a novel fault diagnosis method for rotating machinery is proposed in this paper, which combines the batch channel normalized conditional wasserstein generative adversarial network (BCN-CWGAN) with Swin Transformer. Firstly, the one-dimensional vibration signal is preprocessed into two-dimensional feature images using a symmetrized dot pattern (SDP). Subsequently, self-attention mechanism and deep feature learning module constructed by DenseNet are integrated into the generator of GAN to acquire more discriminative feature information. Meanwhile, the discriminator of GAN is combined with batch channel normalization strategy, which further enhances the generalization ability. Besides, a two time-scale update rule strategy enhances training stability and convergence speed by updating model parameters at different time scales. Then, the data augmentation capability of BCN-CWGAN is used to generate high-quality fault samples to augment the imbalanced dataset. Finally, Swin Transformer is combined to achieve accurate fault diagnosis. The performance enhancement of the proposed method is verified through comparison and diagnosis results of two engineering experiments, demonstrating its substantial value for research in engineering practice. With the proposed data augmentation method, the average accuracy of A1, B1, C1, and D1 datasets in experiment 1 reached 99.24%, 98.85%, 96.78%, and 96.04%, respectively. Meanwhile, the proposed method achieved the best accuracy in experiment 2.

Motion Target Localization Method for Step Vibration Signals Based on Deep Learning

Motion Target Localization Method for Step Vibration Signals Based on Deep Learning

Assessing combustion characteristics of alternative fuels in a CI engine by use of a wavelet-based analysis of engine vibration signals

In the current work, a wavelet-based approach for assessing combustion performance of alternative fuels in compression-ignition engines, indicated that changes in cylinder pressure amplitude were reflected by commensurate changes in a proposed parameter, with a maximum of a 9% variation. The proposed parameter, the wavelet transform coefficient maximum amplitude (WMA), successfully accomplished this for various engine conditions. The wavelet transform coefficient standard deviation (WSD), a second proposed parameter, was able to track changes in the rates of pressure rise associated with changes in load conditions and fuel type, with a maximum variation in the parameter of 7%. The approach was assessed by testing six fuels in a test engine unit. Thermodynamic property data and vibration signal data were recorded for each of the fuels tested, at six loading conditions. Subsequently, combustion performance was evaluated using traditional thermodynamic parameters and using the WMA and WSD via a MATLAB based algorithm. Thus, it was surmised that the proposed approach can form the basis for a vibration-based assessment of alternative fuel combustion performance in reciprocating engines.

New Multi-Channel VSMFxLMS Algorithm for Vibration Reduction of Gear Systems

At present, the active control of gear vibration mostly relies on existing algorithms. In order to achieve effective vibration reduction of the gear system, particularly during the vibration process, this paper proposes a multi-channel VSMFxLMS algorithm based on the FxLMS algorithm. This novel approach takes into account the time-varying nature of the vibration signal during gear vibration. Adaptive filter power coefficients are updated in a skip-tongue variable-step manner using momentum factors. Firstly, the paper establishes the dynamics model of the gear system and analyzes the nonlinear dynamic characteristics of the system. It then examines the vibration damping effect of the FxLMS algorithm and analyzes its performance under different gear system motion states, considering different step lengths and momentum factors. Lastly, the proposed VSMFxLMS algorithm is compared with the FxLMS algorithm, highlighting the superiority of the former. Overall, this research highlights the potential of a multi-channel VSMFxLMS algorithm in reducing vibrations in gear systems. The study optimizes the performance of gear systems while using advanced control strategies.

Variation in thermal courtship activity curves across individuals exceeds variation across populations and sexes.

The dynamics of mating interactions can vary in response to a wide variety of environmental factors. Here, we investigate the potential for individuals to vary consistently in the environmental conditions under which they actively engage in courtship. Specifically, we quantify variation in how courtship activity changes with environmental temperature across levels of biological organization in Enchenopa binotata treehoppers. Male E. binotata produce vibrational courtship signals and receptive females respond with their own sex-specific vibrational courtship signal. We tested each individual twice for the production of courtship signals across a range of ecologically-relevant temperatures (18 - 36 °C). Then, we measured repeatability and variability in the resulting thermal courtship activity curves, including the temperature of peak activity and tolerance to thermal extremes. We also looked at patterns of variation across populations and sexes. We found minimal variation across populations, but significant variation across individuals. Specifically, we found prevalent repeatability in how thermally generalized or specialized individuals are. However, repeatability was limited to females only. We also found higher variability in female traits than in male traits, although patterns of variability did not always predict patterns of repeatability. These results suggest that thermal variation could alter the dynamics of mate competition, and that-due to potentially different selective optima for males and females-the sexes may respond to changes in temperature in different ways. Specifically, females show a higher potential to adapt but males show a higher potential to be more robust to changes in temperature due to overall higher courtship activity across temperatures.

Motor Bearing Failure Identification Using Multiple Long Short-Term Memory Training Strategies

In the context of condition-based maintenance of rotating machines in manufacturing systems, the early diagnosis of possible faults related to rolling elements of the bearing is mainly based on techniques from artificial intelligence, namely, Machine Learning (ML) and Deep Learning (DL). Approaches based on using Deep Learning methods have been the most coveted in recent years. Among a variety of models, the type of architecture known as Long-Short-Term Memory (LSTM) of Recurrent Neural Network (RNN) has both the ability to capture long-term dependencies and to adapt to sequential data modeling. It is therefore able to work on data without any preprocessing. This paper studies using four types of LSTM networks to diagnose bearing faults in a classification approach. It aims to intervene on both the input parameters and the network architecture, to achieve high performance. The proposed method is carried out in two different ways. In the first case, the data inputs are raw frames of vibration signals. However, in the second case, the network inputs are pre-computed time-frequency features. The results clearly showed that LSTMs are more accurate with the latter.

Asymmetric-Based Residual Shrinkage Encoder Bearing Health Index Construction and Remaining Life Prediction.

Predicting the remaining useful life (RUL) of bearings is crucial for maintaining the reliability and availability of mechanical systems. Constructing health indicators (HIs) is a fundamental step in the methodology for predicting the RUL of rolling bearings. Traditional HI construction often involves determining the degradation stage of the bearing by extracting time-frequency domain features from raw data using a priori knowledge and setting artificial thresholds; this approach does not fully utilize the vibration information in the bearing data. In order to address the above problems, this paper proposes an Asymmetric Residual Shrinkage Convolutional Autoencoder (ARSCAE) model. The asymmetric structure of the ARSCAE model is characterized by the soft thresholding of signal features in the encoder part to achieve noise reduction. The decoder part consists of convolutional and pooling layers for data reconstruction. This model can directly construct HIs from the original vibration signals collected, and comparisons with other models show that it constructs better HIs from the original vibration signals. Finally, experiments on the FEMTO dataset show that the results indicate that the HIS constructed by the ARSCAE model has better lifetime prediction capability compared to other methods.

A Spectral-Based Blade Fault Detection in Shot Blast Machines with XGBoost and Feature Importance

The optimal functionality and dependability of mechanical systems are important for the sustained productivity and operational reliability of industrial machinery, and have a direct impact on its longevity and profitability. Therefore, the failure of a mechanical system or any of its components would be detrimental to production continuity and availability. Consequently, this study proposes a robust diagnostic framework for analyzing the blade conditions of shot blast industrial machinery. The framework explores the spectral characteristics of the vibration signals generated by the industrial shot blast for discriminative feature excitement. Furthermore, a peak detection algorithm is introduced to identify and extract the unique features present in the peak magnitudes of each signal spectrum. A feature importance algorithm is then deployed as the feature selection tool, and these selected features are fed into ten machine learning classifiers (MLCs), with extreme gradient boosting (XGBoost (version 2.1.1)) as the core classifier. The results show that the XGBoost classifier achieved the best accuracy of 98.05%, with a cost-efficient computational cost of 0.83 s. Other global assessment metrics were also implemented in the study to further validate the model.

Hilbert-Huang Transform and machine learning based electromechanical analysis of induction machine under power quality disturbances

Monitoring and predicting Power Quality (PQ) is crucial for quickly minimizing risk, protecting induction machines (IMs), and increasing productivity. This paper proposes an electromechanical analytical framework for IM working with various PQ disturbances based on machine learning (ML). This work collects the motor characteristics and PQ data, motor vibration signals, and other sensor data to identify the failures. The complicated electromechanical data is broken down into intrinsic mode functions (IMFs) using the Hilbert-Huang Transform (HHT), which reveals the modes present in the signals. This frequency-domain data is retrieved, paired with time-domain characteristics, and used as input features for ML algorithms. Support Vector Machines (SVMs) trained on labeled datasets in which features extracted from PQ disturbances are used to categorize the disturbances into several groups. The PQ disturbances caused by voltage sags, swells, harmonics, and transients can all be efficiently classified using SVM classifiers, allowing for real-time determination of the kind of disturbance influencing the IM. The accuracy for the SVM in the proposed scheme is 97.2 %. The SVM method is trained with PQ data and classification results using MATLAB classifier and Python software.

Synchronous Decomposition Match-Reassigning Transform and Its Application in Planetary Gearbox Fault Diagnosis

Abstract Under time-varying operating conditions, the instantaneous frequency (IF) of the vibration signal of the planetary gearbox exhibits non-stationary time-varying closely spaced characteristics as well as non-proportional and non-synchronous characteristics, making it difficult for traditional time-frequency analysis (TFA) methods to accurately identify its fault features and obtain accurate time-frequency representations (TFR). To address this challenge, this paper proposes a TFA method based on Synchronous Decomposition Match-Reassigning Transform (SDMRT). Firstly, the successive variational mode decomposition (SVMD) is used to adaptively separate non-synchronous frequency components in the original vibration signal. Then, the chirp rate (CR) describing the signal harmonic structure is used to synchronously match each frequency component, obtaining the TFR corresponding to each component. Next, all TFRs are merged to obtain a complete TFA result. Finally, Finally, the energy spread is re-aggregated to the ridge of the IF using the reassignment principle, resulting in a new frequency estimation operator called the Synchronous Decomposition Match-Reassigning Operator (SDMRO). SDMRT can adaptively separate non-proportional and non-synchronous IFs and achieve precise matching of time-varying closely spaced IFs, thereby completing the TFR of the vibration signal generated by the planetary gearbox. By analyzing simulated signals and measured vibration signals from planetary gearboxes, the method proposed in this paper is significantly superior to other TFA methods, thereby validating the effectiveness and superiority of SDMRT.

Rolling element bearing fault diagnosis under time-varying speed conditions based on improved wavelet packet transform and envelope order tracking

Abstract Rolling element bearings (REBs) are crucial components of rotating machinery and play an important role in industrial production. Failure of REBs can lead to significant economic losses and catastrophic accidents. In order to ensure their safe and stable operation, we propose a novel approach called EOT-IWPT which combines the envelope order tracking (EOT) with an improved wavelet packet transform algorithm (IWPT) for diagnosing failures in REBs under time-varying speed conditions. First, the time- domain vibration signal is resampled at constant angle intervels according to the phase of the rotating shaft to obtain the angle-domian signal. Next, the resampled signal is decomposed to sub-band signals using IWPT. Third, the squared envelope spectra (also called envelope order spectra) of the last layer wavelet packet coefficients are obtained by the Hilbert transform and the Fourier transform. Finally, the diagnostic results can be easily determined based on the squared envelope spectra. The procedure and performance of the proposed approach are illustrated and evaluated through simulation analysis, proving that the proposed method can effectively extract the fault features. Furthermore, experimental analysis results indicate that the proposed method outperforms both EOT-WPT and SK-EOT-BPF.

In Situ Unraveling Surface Reconstruction of Ni‐CoP Nanowire for Excellent Alkaline Water Electrolysis

The surface reconstruction behavior of transition metal phosphides precursors is considered as an important method to prepare efficient oxygen evolution catalysts, but there are still significant challenges in guiding catalyst design at the atomic scale. Here, the CoP nanowire with excellent water splitting performance and stability is used as a catalytic model to study the reconstruction process. Obvious double redox signals and valence evolution behavior of the Co site are observed, corresponding to Co2+/Co3+ and Co3+/Co4+ caused by auto‐oxidation process. Importantly, the in situ Raman spectrum exhibits the vibration signal of Co–OH in the non‐Faradaic potential interval for oxygen evolution reaction, which is considered the initial step in reconstruction process. Density functional theory and ab initio molecular dynamics are used to elucidate this process at the atomic scale: First, OH− exhibits a lower adsorption energy barrier and proton desorption energy barrier at the configuration surface, which proposes the formation of a single oxygen (–O) group. Under a higher –O group coverage, the Co–P bond is destroyed along with the POx groups. Subsequently, lower P vacancy formation energy confirm that the Ni‐CoP configuration can fast transform into a highly active phase. Based on the optimized reconstruction behavior and rate‐limiting barrier, the Ni‐CoP nanowire exhibit an excellent overpotential of 1.59 V at 10 mA cm−2 for overall water splitting, which demonstrates low degradation (2.62%) during the 100 mA cm−2 for 100 h. This work provide systematic insights into the atomic‐level reconstruction mechanism of transition metal phosphides, which benefit further design of water splitting catalysts.

Application of Machine Learning Methods to Analyze the Dependence of Vibration Acceleration Signals on the Tightening Forces of Bolted Connections

The article is devoted to the application of machine learning methods for the analysis of vibration acceleration signals in bolted joints that occur under impact. The relevance of the work is due to the need to ensure the reliability and operability of bolted joints under loads. One of the promising areas of non-destructive testing is the analysis of the characteristics of vibration acceleration signals that change with changes in the state of structures, for example, with an increase in the tightening torque. This allows for the timely detection of possible defects and the prevention of emergency situations, ensuring the safety and durability of structures. The vibration acceleration signals under impact action on a bolted joint were analyzed. After calculating the signal characteristics, data sets were formed, including the values of tightening torques and the corresponding calculated signal characteristics, a search for a correlation between the tightening torque of bolted joints and the obtained set of vibration signal parameters was carried out. The frequency spectrum of signals, the Higuchi fractal dimension, detrended fluctuations, spectral power density and position of signal peaks were calculated. Machine learning models such as decision trees, the nearest neighbor method, the k-means method and neural networks were used for the analysis. For these methods, an optimal set of parameters correlating with the tightening torque was searched for. The main results show that machine learning methods are effective in classifying signals and finding correlations with stress state parameters. They allow us to detect the relationship between a set of signal characteristics and tightening torque, which opens up opportunities for more accurate and reliable monitoring of the condition of bolted connections. This should help to increase their operational reliability and durability, as well as reduce the likelihood of failures and accidents. The use of such methods can improve the quality of monitoring and diagnostics of bolted connections.

Method for enhancing fault feature signals of rolling bearings in gas turbine engines

Aiming at the problem that the weak fault signal of rolling bearings in gas turbine engines (GTEs) is affected by environmental noise, which leads to the difficulty of effective information extraction and easy to ignore, the characterization method for cyclic extraction of main bearing fault feature components in GTEs is proposed. The proposed method begins by subjecting raw vibration signals to wavelet packet decomposition and correlating correlation coefficient and kurtosis values for each node component. These values are then normalized and amalgamated into a comprehensive parameter denoted as P. Subsequently, a confidence interval is established to categorize node components into three groups: high signal-to-noise ratio signals, low signal-to-noise ratio signals, and high-noise signals. Then, the high signal-to-noise ratio signals are continuously filtered according to the feature component cyclic extraction criterion until the termination condition is reached. Finally, all high signal-to-noise ratio signals are reconstructed, followed by envelope demodulation to extract subtle bearing fault characteristics. The findings underscore the efficacy of this approach in extracting fault features within the intricate transmission path of rolling bearings, offering a robust solution for the intricate signal processing and diagnosis of complex rolling bearing faults in GTEs.

Fault diagnosis method of switch machine based on adaptive neural fuzzy network

Aiming at the problem of a large number of switch machines, harsh working environment and high frequency of equipment failures caused by many factors, in order to accurately diagnose railway switch machine faults, a switch machine fault diagnosis method based on adaptive neural fuzzy network (ANFIS) optimized by dynamic weight particle swarm (DPSO) algorithm is proposed by analyzing the vibration signals generated during the operation of the switch machine. Firstly, the working vibration signal is decomposed into several intrinsic mode functions (IMFs) and selected by using the ensemble empirical mode decomposition (EEMD) algorithm; then, the feature values of IMFs are extracted by using the improved time domain multiscale scatter extraction (ITMDE) algorithm, and then input into the optimized ANFIS model to learn and realize fault diagnosis; finally, the method is compared and analyzed with various diagnostic model algorithms and learning algorithms. The experimental results show that the proposed method can effectively diagnose switch machine faults, which has certain reference significance for intelligent diagnosis of switch machine faults and related research in the future.

Remaining Useful Life of the Rolling Bearings Prediction Method Based on Transfer Learning Integrated with CNN-GRU-MHA

To accurately predict the remaining useful life (RUL) of rolling bearings under limited data and fluctuating load conditions, we propose a new method for constructing health indicators (HI) and a transfer learning prediction framework, which integrates Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Multi-head attention (MHA). Firstly, we combined Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) to fully extract temporal and spatial features from vibration signals. Then, the Multi-head attention mechanism (MHA) was added for weighted processing to improve the expression ability of the model. Finally, a new method for constructing Health indicators (HIs) was proposed in which the noise reduction and normalized vibration signals were taken as a HI, the L1 regularization method was added to avoid overfitting, and the model-based transfer learning method was used to realize the RUL prediction of bearings under small samples and variable load conditions. Experiments were conducted using the PHM2012 dataset from the FEMTO-ST research institute and XJTU-SY dataset. Three sets of 12 migration experiments were conducted under three different operating conditions on the PHM2012 dataset. The results show that the average RMSE of the proposed method was 0.0443, indicating high prediction accuracy under variable loads and small sample conditions. Three different operating conditions and two sets of four migration experiments were conducted on the XJTU-SY dataset, and the results show that the average RMSE of the proposed method was 0.0693, verifying the good generalization of the model under variable load conditions. In summary, the proposed HI construction method and prediction framework can effectively reduce the differences between features, with high stability and good generalizability.

Image representation of vibration signals and its application on fault diagnosis of rotating machinery with convolutional neural network: A benchmark study

Intelligent fault diagnosis methods on the basis of two-dimensional (2D) image representation of vibration signals (IRVS) and the convolutional neural network (CNN) have been extensively applied in rotating machinery. However, as a large number of IRVS methods are being used, their performance has not been fairly and comprehensively evaluated, which leads to difficulties for researchers to make choices. To address this issue, this paper conducted a benchmark study aimed at comparing the comprehensive performance of different IRVS methods, including computation time, fault diagnosis accuracy, and noise resistance performance. Firstly, the IRVS methods and the fault diagnosis method combining IRVS and CNN were summarized. Then, the general process of the IRVS-CNN method was proposed. Finally, 17 types of IRVS methods were selected, and the performance of the IRVS method was compared based on three datasets and two classic CNN models. The results indicate that the computing time taken to generate 2D images using unthresholded recurrence plot (UTRP), and frequency-domain unthresholded recurrence plot (FDUTRP) methods is relatively longer. The vast majority of frequency-domain methods and time-frequency domain methods can achieve or approach a high accuracy rate in all experiments, demonstrating their outstanding performance. Frequency-domain color vibration image (FDCVI), FDUTRP, and continuous wavelet transform (CWT) have good noise resistance. These results provide a reference for future researchers in selecting IRVS methods.

Analysis of friction vibration characteristics of the end face of a T-slot sealing ring under thermal deformation

Aiming at the friction vibration problem caused by dry friction contact during the start-stop process of a dry gas sealing device, this paper takes the end face of a T-slot dry gas sealing ring as the research object and establishes a numerical calculation model of the end face of the T-slot sealing ring. The thermal coupling method is used to obtain the thermal deformation law of the seal ring end face. The transient dynamics analysis method was applied to study the frequency and vibration mode of the friction vibration instability of the seal ring end face under thermal deformation. A vibration test bench was set up for the friction ring, and the friction vibration signals of the test pieces were collected, and the characteristic frequencies were extracted. The results show that: After thermal deformation, the end face of the sealing ring changes from a parallel surface to an inward converging surface, and the friction system of the sealing ring will become unstable. When considering the influence of thermal deformation, there are more unstable frequencies and lower frequency values that are most prone to frictional vibration. As the thermal deformation of the sealing ring increases, the frictional vibration becomes greater. In areas where the characteristic frequency values of frictional vibration are concentrated during thermal deformation, the occurrence rate of frictional vibration noise is higher.