Characteristics Of Vibration Signals Research Articles

Rotating Machinery Structural Faults Feature Enhancement and Diagnosis Base on Low-pass Teager Energy Operator Intrinsic Time-scale Decomposition

Abstract Aiming to address the issue of the complex and harsh working environment of rotating machinery, the features of vibration signals associated with structural faults are often obscured by noise, resulting in low accuracy in fault diagnosis. This paper proposes a method for feature enhancement and diagnosis of rotating machinery structural faults, which combines the Low-pass Teager Energy Operator Intrinsic Time-scale Decomposition (LTEO-ITD) Recurrence
Plot (RP) with the ResNet18 network. Firstly, the low-frequency components of the vibrationsignal are extracted and enhanced using the low-pass Teager energy operator. The method effectively suppresses noise interference and enhances fault features. Then, the fault features are extracted using ITD. The component that contains the highest number of fault features is selected based on kurtosis analysis, followed by the generation of the corresponding recurrence plot. Finally, the data is input into the ResNet18 network for diagnostic verification. The effectiveness and feasibility of the proposed method are verified through vibration signals from the rotating machinery experimental platform and the comprehensive rotating machinery experimental platform. The proposed method achieves a diagnostic accuracy of 100% on both datasets. The comparative validation was conducted using five distinct image encoding methods. The experimental results show that the proposed method effectively extracts fault features of structural faults, thereby enhancing the accuracy of fault diagnosis.

A novel fault identification method for rolling bearing

Abstract One-dimensional local binary pattern (1D-LBP) identifies the running state of bearings by describing local textural features of vibration signals. However, 1D-LBP algorithm is rather sensitive to noise and intrinsic periodic features of rotating machinery which is not included in consideration. This makes it difficult for 1D-LBP algorithm to comprehensively dig fault information of bearings. To solve this problem, a new method has been brought forward by combining the intrinsic periodicity of rotating machinery and extraction of textural features. Before extraction of local features of signals, vibration signals are subjected to first-order difference operation to highlight local impact features of bearing failure. Meanwhile, to reduce the noise and further strengthen periodic features, differential signals are segmented by a complete period, and the signals after segmentation are subjected to cross-correlation successively to acquire the corresponding cross-correlation functions. Secondly, 1D-LBP is employed on cross-correlation functions (not raw signals) to extract local features and local textural signals (LTS) are obtained. Thirdly, feature parameters and vectors have been built according to LTS and the running state of bearings has been described from the perspective of textural feature. At last, according to established feature vector and classification algorithm, running state of bearing was determined. A comparative analysis is given to proposed method and other methods through public data sets and data from an experimental equipment of bearing. The results of comparative analysis indicate that with the 2 kinds of data sets, the identification rate of proposed method for unknown samples is over 97% and it can judge the running state and the type of faults more precisely.

A new cross-domain approach for bearing fault diagnosis based on multiscale convolutional networks and adversarial subdomain adaptation

ABSTRACT Fault diagnosis of rolling bearings is of paramount significance in the field of engineering, as it directly impacts the reliability and safety of mechanical systems. Although deep learning techniques have demonstrated promising performance in this field, their efficacy often diminishes under varying operational conditions or when labelled data is limited. To overcome these challenges, this paper introduces a more precise cross-domain approach for bearing fault diagnosis. The proposed method exploits the temporal multiscale characteristics of vibration signals and the inherent multiscale nature of bearing faults by constructing a one-dimensional multiscale convolutional neural network. This network extracts features at multiple scales from raw vibration signals, which are then fused to form robust and generalised representations. Additionally, the integration of the Efficient Channel Attention mechanism further refines feature selection, enhancing the overall performance of the model. The label classifier, in conjunction with the Nuclear-norm Wasserstein Discrepancy, serves as a domain discriminator to facilitate adversarial domain adaptation. Concurrently, local maximum mean discrepancy and adversarial domain adaptation techniques align both global and subdomain feature distributions. Furthermore, label smoothing is incorporated to enhance the model’s generalisation capabilities. Experimental validation on the CWRU and PU rotating machinery datasets demonstrates the method’s exceptional robustness and superior transferability.

Vibration signal characteristics of debris flow initiation and motion experiments by considering clay content

Micro accelerometers are crucial tools for monitoring and providing early warning of geological disasters. This study focuses on the potential debris flow in Wangjiayuan, Fang County, Hubei Province, aiming to explore the relationship between clay content and debris flow motion characteristics. Physical model experiments were designed with varying clay content to simulate debris flow motion, and micro accelerometers were employed to monitor and record vibration acceleration changes during debris flow initiation and motion. Frequency spectra were generated using Fourier transform to analyze the motion characteristics in the frequency domain. The results showed that the predominant period of debris flow initiation ranged from 15 Hz to 29 Hz, while the period during motion in the middle section of the flow area ranged from 36 Hz to 54 Hz. Higher clay particle content was associated with a decrease in both the frequency range and mean of the predominant period, as well as a reduction in the Fourier amplitude extremum. These findings provide a foundation for monitoring and predicting debris flows, and the vibration signal characteristics of debris flow initiation and motion can be used to infer the clay content, offering insights into early warning systems for such geological hazards.

Turning tool wear state recognition based on EMDResNeStTime and sequence-global-encoding

Abstract Tool wear is inevitable in machining and directly impacts the forming quality of the workpiece. Accurate monitoring of tool wear state can effectively improve machining efficiency and reduce the adverse effects caused by tool wear. This paper proposes an intelligent recognition approach for tool wear state in machining based on vibration signals. The EMDResNeStTime (ERT) module uses empirical mode decomposition (EMD) and two-dimensional convolution to extract the main trend features of vibration signals, and the Sequence-Global-Encoding (SGE) module uses the self-attention mechanism to extract the global features of vibration signals, are designed. A new Multi-feature Parallel-time (MFPT) feature extraction backbone is constructed using these two modules. The use of this backbone effectively improves the ability of the network model to extract key features of complex trends in vibration signals. Experimental results show that the proposed approach achieves 71.65%, 74.89%, 71.36% and 72.78% in accuracy, precision, recall and F1 score, respectively. Compared with other network models, it has higher recognition accuracy and more stable performance. The ablation experiment further demonstrates the classification effectiveness of the method used in this network model.

Research on a method of fault identification of rolling bearings based on time series shapelets

Recent research on utilizing artificial neural network (ANN) for fault identification of bearings ignores the features of vibration signals as time series, resulting in low interpretability and cannot classify signals of bearing vibration on different dimensions. Time series shapelets is the most identifiable subsequences of time series. Time series can be effectively classified through computing similarities between shapelets and each time series. This paper identifies faulty bearings with a machine learning-based shapelets algorithm, and through modeling, calculation and verification based on CWRU fault bearing data set, some szimportant conclusions are obtained. The results show that this meaningful new method is more accurate and can reduce time consumption compared with traditional time-laboring shapelets, enhancing interpretability of diagnosis in the meantime. Compared with other classification methods, shapelets transforms the classification problem of vibration signal time series into a similarity measurement problem between time series and subsequence shapelets, fully reflecting the essence of bearing vibration signals as a time series. Meanwhile, by using MINDIST distance instead of Euclidean distance in shapelets can effectively reduce the calculation time while ensuring accuracy, which could be a significant method of distance measurement in clustering tasks.

An enhanced deep intelligent model with feature fusion and ensemble learning for the fault diagnosis of rotating machinery

Vibration signals, serving as critical sources of information for monitoring the status of rotating machinery, demand effective extraction and rational utilization of its features to significantly enhance the accuracy and reliability of fault diagnosis. However, vibration signal features typically manifest as nonlinear and nonstationary, posing a significant challenge in industrial settings. To tackle this challenge, this article proposes an enhanced deep intelligent model based on feature fusion and ensemble learning for practical fault diagnosis of rotating machinery. First, a parallel network structure is introduced to comprehensively and accurately explore the fault characteristics of rotating machinery. This network comprises two branches: the first branch designs an improved one-dimensional convolutional neural network to extract locally robust features from raw signals; the second branch adopts variational mode decomposition to decompose raw signals into a set of intrinsic mode functions and extract comprehensive statistical features in both the time and frequency domains, significantly enhancing the signal representation capability. Subsequently, a deep neural network is used to extract more stable feature information. The features from the two branches are then fused, and the final network output is generated through a softmax regression function. Finally, ensemble learning uses a majority voting scheme to obtain more stable final outputs. To confirm the effectiveness of the proposed method, experiments are conducted on two laboratory cases and one industrial case. The experimental results demonstrate that the proposed method significantly improves fault diagnosis accuracy and reliability in controlled laboratory environments and real-world industrial applications, making it highly applicable for real-time monitoring and predictive maintenance of industrial machinery. These improvements can reduce maintenance costs and downtime, thus enhancing operational efficiency in various industrial settings.

Recursive prototypical network with coordinate attention: A model for few-shot cross-condition bearing fault diagnosis

In a practical industrial scenario, the variability in bearing operating conditions complicates the collection of a sufficient number of labeled samples, thereby limiting the effectiveness of traditional deep learning-based fault diagnosis methods. In addition, the influence of abnormal samples on the prototype features also severely limits the performance of prototypical network in few-shot fault diagnosis. To address the above issues, a recursive prototypical network based on meta-learning is proposed for few-shot cross-condition bearing fault diagnosis. Firstly, a feature extractor with coordinate attention mechanism is developed, which is able to deeply extract effective features in complex vibration signals. Furthermore, a recursive prototype computation module is introduced to alleviate prototype bias arising from abnormal samples, thereby achieving a more precise representation of prototypes. Finally, a metric module is utilized to obtain the similarity between the prototypes and the query set samples to achieve an accurate classification of faults. To verify the efficacy and superiority of the proposed method, its performance was evaluated on two bearing vibration datasets. The experimental results demonstrated that the method is significantly better than other deep learning methods with high accuracy and generalization, and greater suitability for few-shot cross-condition bearing fault diagnosis tasks.

Recognition and location of rotor–stator rub-impact failure of aircraft engine with periodicity feature of vibration signals

To precisely recognize and locate a rub-impact fault of aircraft engine, an approach combining Hilbert envelope (HE), adaptive motion window (AMW), and margin factor (MF) (HE-AMW-MF) was presented. HE can reflect the change rule and tendency of vibration signals and simplify the complexity of calculation of fault diagnosis. Therefore, the paper starts with the calculation of HE of signal and takes it in place of original signal for subsequent analysis. As vibration signal caused by rub-impact fault is often accompanied by periodic impact, the paper takes advantage of the characteristic of periodic impact (collision period instead of rotation period) to adaptively determine a length and step size of motion window and exactly distills the rub-impact fault characteristics of each period. Due to the sensitivity of margin factor (MF) to the trait of wearing and impact, it is brought in to represent the fault feature information contained in signal in each motion window. Equally, mean value of all MFs is calculated and built into feature vector to reduce an interference of noise. Finally, the feature vector is combined with classification algorithms (k-nearest neighbor, KNN; support vector machine, SVM; convolutional neural network, CNN) to recognize a rub-impact fault and its location. After comparative analysis with typical method, the result indicates that the proposed HE-AMW-MF method can identify and locate the rub-impact fault more precisely, and the precision is 32.0% and 36.2%, 22.5% higher than typical comparison method.

Intelligent Identification and Prediction of Roof Deterioration Areas Based on Measurements While Drilling.

During roadway excavation, the presence of roof deterioration zones, such as layered spaces and weak interlayers, significantly affects the stability of the surrounding rock. To achieve timely and effective support for roadways, it is essential to utilize drilling measurement signals obtained during the construction of anchorage holes for the identification and prediction of these deterioration zones. This study systematically investigates the response characteristics of thrust, torque, and Y-direction vibration signals to different combinations of rock layers through theoretical analysis, laboratory experiments, ABAQUS dynamic numerical simulations, and field measurements. The results indicate that these drilling parameters effectively characterize variations in rock structure and strength, with distinct signal features observed particularly in roof deterioration zones. Based on these findings, this paper proposes a deep learning algorithm that employs Long Short-Term Memory (LSTM) recurrent neural networks for classification prediction, along with a random forest algorithm for regression prediction, aimed at the intelligent identification and prediction of roof deterioration zones. The algorithm demonstrates outstanding performance in both laboratory experiments and field tests, achieving a 100% recognition rate for layered spaces and a 96.6% accuracy for identifying deterioration zones, with high accuracy at lower values of mechanical specific energy (MSE). The proposed method provides significant insights for real-time monitoring and control of roof deterioration zones, enhancing the safety and stability of roadway excavations, and serves as a valuable reference for future research and practical applications.

Enhancing underwater thruster anomaly detection with support vector glow encoding description

As the driving system of autonomous underwater vehicles (AUVs), the healthy operation of underwater thrusters is crucial to ensure the performance of AUVs. Due to the challenge of obtaining faulty samples during normal operation, anomaly detection of underwater thrusters is often carried out using only normal samples. To address this, Support Vector Glow Encoding Description (SVGED) is proposed for anomaly detection of underwater thrusters trained with only normal samples. Specifically, the deep features of the operational vibration signals collected from the underwater thruster are extracted by a deep convolutional autoencoder based on a glow model. The glow-encoded data are then used to identify the boundary of normal operation for the underwater thruster. When a new sample enters the trained SVGED model, it can be effectively identified as representing a normal or anomalous condition of the underwater thruster. The developed SVGED was evaluated using AUV experiments. When using the proposed SVGED method, the accuracy reaches 99.81%, Results show that the proposed method can effectively real time detect anomalies in the underwater thruster compared to other methods. It lays the foundation for ensuring the healthy operation of AUVs.

Domain Adaptation Networks With Parameter-Free Adaptively Rectified Linear Units for Fault Diagnosis Under Variable Operating Conditions.

As an important component of the rotating machinery, rolling bearings usually work under the condition of variable speed and load, and vibration signals in the same health state are significantly different due to the change in operating conditions. To address the problem that the existing deep learning (DL) methods have fixed nonlinear transformations for all input signals in cross-domain fault diagnosis, we propose a new activation function, i.e., parameter-free adaptively rectified linear units (PfAReLU). The proposed activation function performs adaptive nonlinear transformations according to the input data and can better capture the fault features of vibration signals in the same fault state under different operating conditions. Furthermore, the number of PfAReLU parameters is zero, so that the risk of network overfitting is reduced. At the same time, deep parameter-free reconstruction-classification networks with PfAReLU (DPRCN-PfAReLU) are also constructed for cross-domain fault diagnosis. Specifically, DPRCN-PfAReLU consists of a shared encoder, a target domain decoder, and a source domain classifier. The shared encoder adds a parameter-free attention module at the output to enhance the weight of domain-invariant features without increasing network parameters. The shared encoded representation of source domain and target domain is learned by target domain decoder and source domain classifier. Compared with other methods under nine different operating conditions via real experiment studies, the proposed method shows superiority for cross-domain fault diagnosis.

A Mechanical Fault Identification Method for On-Load Tap Changers Based on Hybrid Time—Frequency Graphs of Vibration Signals and DSCNN-SVM with Small Sample Sizes

In engineering applications, the accuracy of on-load tap changer (OLTC) mechanical fault identification methods based on vibration signals is constrained by the quantity and quality of the samples. Therefore, a novel small-sample-size OLTC mechanical fault identification method incorporating short-time Fourier transform (STFT), synchrosqueezed wavelet transform (SWT), a dual-stream convolutional neural network (DSCNN), and support vector machine (SVM) is proposed. Firstly, the one-dimensional time-series vibration signals are transformed using STFT and SWT to obtain time–frequency graphs. STFT time–frequency graphs capture the global features of the OLTC vibration signals, while SWT time–frequency graphs capture the local features of the OLTC vibration signals. Secondly, these time–frequency graphs are input into the CNN to extract key features. In the fusion layer, the feature vectors from the STFT and SWT graphs are combined to form a fusion vector that encompasses both global and local time–frequency features. Finally, the softmax classifier of the traditional CNN is replaced with an SVM classifier, and the fusion vector is input into this classifier. Compared to the traditional fault identification methods, the proposed method demonstrates higher identification accuracy and stronger generalization ability under the conditions of small sample sizes and noise interference.

COMPUTER DIAGNOSTICS OF THE CONDITION OF SHIP ROLLING BEARINGS DURING THEIR OPERATION

The results of statistical processing of vibration signals obtained during diagnostics of ship power plants during the operation process are offered. A distinctive feature of monitoring the technical condition of elements of ship power plants is the uncertainty in the fixation and duration of extreme loads. The peculiarity of computer vibration diagnostics of rotary mechanisms is determined, which is that, in particular, physical measurements of vibration signals require their use in mathematical models of the physical state of the bearing. Calculations of statistical characteristics of vibration signals in the time domain, such as standard deviation (Std), asymmetry (Skewness), kurtosis (Kurtosis), full range of oscillations (Peak2Peak), root mean square value (RMS), cross factor (CrestFactor), form- factor (ShapeFactor), impulse factor (ImpulseFactor), marginal factor (MarginFactor), energy (Energy). The variety of diagnostic signals leads to the need to merge indicators of different dimensions without losing available information into a single generalizing indicator, for which the method of principal components is used in the work. The methodology of statistical processing and practical implementation of diagnostics of vibration signals during the analysis of damage evolution of rolling bearings of turbochargers of marine power plants are described. A new computer information parameter of vibration diagnostics based on the analysis of the first principal component in the time domain and spectral excess in the frequency domain has been identified and experimentally confirmed. The use of new diagnostic parameters makes it possible not only to judge the degree of damage to the rolling bearing, but also to observe the dynamics of the development of the defect and make a relative forecast of the working life of the rolling bearing. A real practical situation is considered - computer diagnostics of vibration signals of ship rolling bearings of a point defect of an external defect of a rolling bearing ring. Determining the applicability of the principal component method to other types of defects showed their potential for vibrodiagnosis of defects located on the inner ring of the bearing and when the ball falls out of the holder.

Experimental study on the leakage identification for the buried gas pipeline via vibration signals

It is difficult to detect and locate small leakages, especially for buried pipelines. Fortunately, a non-intrusive leakage identification method for buried gas pipelines is proposed in this study. Accelerometers were arranged in the soil at a certain distance from the leakage orifice to capture the acceleration signals caused by leakage. A 7-layer wavelet transform was applied to extract the leakage characteristic frequency band. Meanwhile, the attenuation characteristics of vibration signals were analyzed and the signal amplitude attenuation patterns in the axial, radial, and circumferential directions were analyzed. The results show that the leakage recognition rate is nearly 100% by selecting a P-SNR threshold of 7.26. Thus, the non-intrusive method based on accelerometers can be successfully applied for the leakage monitoring of buried gas pipelines.

Study on Self-Learning System for Milling Chatter Feature Based on Hybrid Preprocessing Model and Improved Convolutional Clustering

Milling chatter is a self-excited vibration phenomenon during the cutting process, typically occurring in machining operations with lower stiffness. It significantly impacts the machining precision and surface morphology of the workpiece. To summarize the vibration signal features under operating conditions and enhance the precision of the intelligent recognition model for milling chatter features, this paper proposes an adaptive learning model for chatter feature recognition based on the improved convolutional clustering algorithm. This study uses wavelet analysis to extract chatter features and improved convolutional clustering to identify chatter and stable cutting signals. This method selected appropriate mapping techniques based on differences in chatter signals under various conditions and uses convolution for feature extraction. Improve convolutional clustering evaluates sample similarity through energy transfer, less influenced by signal space structure compared to Euclidean or Mahalanobis distances. This method provides a unified criterion for evaluating different signal feature representation spaces, thereby improving the accuracy and efficiency of chatter sample recognition. The recognition accuracy of chatter phenomena at different spindle speed ranges reaches $95$ % and $96.3$ % respectively. The results show that this method can effectively distinguish between chatter signals and stable cutting signals.

Efficient Network-based Fault Detection in Elevator Vibration Signals: A Weighted Fusion Approach of Displacement and Acceleration Data

This research addresses human vibration analysis, rule-based lift fault detection algorithm noise interference, and early fault diagnosis. A new network-based defect diagnosis system uses lift sensor acceleration and displacement data. Convolutional Neural Networks CNNs automatically extract features like motor current (average, maximum, lowest values during a recording session) from this fused data stream and sensor data. CNNs learn key features from pooled data to reduce noise and detect faults early. To build a baseline for normal operation, 132 vibration signal characteristics (RMS, peak-to-peak, mean, standard deviation), lift ID, timestamp, operating mode (Up/Down/Idle), and load condition (Empty/Low/Medium/High) data points are used. Regular operational data and 201 gearbox fault data points are collected. The problem affects motor behaviour via vibration signals, timestamps, lift data, and motor current values. Learning to distinguish this defect type from normal operational data allows the network-based technique identify it. Research shows that network-based strategies outperform traditional methods. It finds weaknesses better than before researches. This method automatically extracts features from the fused data stream to detect defects in real time. Displacement and acceleration data, motor current readings, and CNNs improve noise resistance and failure signal detection. Network-based lift failure detection is advanced in real time problem identification. Precision, early detection, noise resistance, and real-time defect categorization are its strengths. Study affects lift maintenance and find defects early to keep lifts functioning. Real-time defect classification eliminates laborious analysis, simplifying maintenance in which enhancements improve lift performance and reliability.

Experimental study of wet and dry milling effect on surface roughness of TC4 titanium alloy

Titanium alloys are important engineering materials used in various industry areas, such as aviation, aerospace and medical devices. The surface roughness is a significant parameter for assessing the machining quality of titanium alloys. In this study, in order to compare the effects of dry and wet milling conditions on the surface roughness of titanium alloys, we established a test system to obtain the surface roughness, three-dimensional milling vibration and three-dimensional milling force signals. The gray correlation theory was applied to investigate the correlation between surface roughness and milling vibration or milling force under dry and wet milling conditions. Using the least squares method, the prediction models of surface roughness corresponding to dry and wet milling conditions were developed according to the features of vibration or force signals. Furthermore, the combined prediction models of surface roughness that integrate multiple signal features and milling parameters were established. The comparison results revealed that there was a stronger correlation between roughness and milling force, milling vibration in wet milling. Wet milling exhibited significantly lower roughness compared to dry milling under the same machining parameters, with up to a 59% reduction. The correlation coefficient of the wet milling model is as high as 0.95.

Enhanced fault diagnosis of rolling bearings using an improved inception-lstm network

ABSTRACT Rolling bearings are integral to the operation of various mechanical systems, where their condition directly impacts equipment reliability and performance. Accurate fault diagnosis is essential for preventing unexpected failures and minimising downtime in industrial settings. This study presents a novel diagnostic model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, enhanced by an improved Inception module. This integrated approach captures both spatial and temporal features of vibration signals, leading to superior diagnostic accuracy under diverse operating conditions. Experimental results using the Case Western Reserve University (CWRU) bearing dataset demonstrate an impressive average fault diagnosis accuracy of 99.83%, outperforming conventional models. The robustness of the proposed model against noise and its adaptability to varying operational environments underscore its practical value for real-world engineering applications, offering a reliable solution for maintaining the safety and efficiency of critical machinery.

Online vibration monitoring using laser vibrometer and acoustic emission techniques in robotic milling CFRP composites

Carbon fiber reinforced polymer (CFRP) material has superior mechanical properties, often utilized in the aerospace industry. However, its prone to surface defects such as burrs and delamination under the influence of machining vibration, thereby compromising component quality. This study evaluated the relevant vibration signal features via signal processing methods, and investigated the effect of machining parameters on machining stability during robotic milling of CFRP composite. Specifically, the study assessed machining status by analyzing milling signals and compared surface quality between vibration and stable machining stages, the former corresponds to a larger Sa on the machined surface, with more pronounced profile fluctuations. Machining vibration manifests distinct characteristics in laser vibrometer signals, exhibiting in time-frequency plots with short-term signal enhancements at multiple frequencies. This phenomenon occurs more frequently during entry and exit stages. The results indicated that feed speed has a limited effect on milling stability within the selected parameter range, and severe machining vibration occurred at lower spindle speed (12,000 rpm) and higher milling depth (2 mm). Corresponding surface defects formation mechanisms were discussed, revealing that the formation of uncut burrs at the top was related to axial milling force, while interlayer debonding cracks stemmed from variations in fiber orientation between adjacent layers.