Vibration Signals Research Articles

Experimental investigation of the relation between operating conditions and offshore wind turbine drivetrain dynamics

Abstract This study investigates the impact of operating and environmental conditions on the vibration induced on an offshore wind turbine drivetrain. Furthermore, it explores the role of the dynamic response of the global wind turbine structure to vibration on the drivetrain. A prolonged experimental campaign dedicated to condition monitoring the drivetrain provides experimental vibration signals. These vibration signals are acquired under varying operating conditions across multiple locations of an offshore wind turbine drivetrain of a Belgian Offshore Wind farm and serve as the basis for analysis. The signals’ statistics facilitate establishing connections between dynamic behaviour and operational variables, such as active power, rotor speed, blade pitch angle, and turbulence intensity, sampled at every second and provided by Fast-SCADA. The initial phase of the analysis involves a comprehensive examination of various statistical properties of the vibration signals to link the drivetrain’s dynamic response to operational conditions. This exploration includes both active and standstill periods of the wind turbine. A comprehensive summary that relates the global vibration characteristics with the operating conditions is formed by scrutinizing the overall vibration signals. In the study’s second phase, a detailed investigation is conducted on the vibration signals recorded during standstill conditions. Investigation of standstill data ensures that the structural modes’ spectral content does not interfere with the machines’ kinematic content. The analysis of standstill data proceeds from determining the most energetic resonance frequencies through the average power spectral density of the vibration signals. These identified frequency bands enable a thorough exploration, revealing the interlink between the offshore wind turbine drivetrain’s dynamic response and the operating/environmental conditions.

Real-time monitoring of aero-engine vibration signals by wireless communication technology

Abstract The conventional way to monitor the vibration signals of aircraft engines is to use wired communication sensors to transmit and collect the signals on a fixed platform on the ground. The signal acquisition equipment of wired communication technology has various drawbacks, resulting in the inability to collect vibration signals when the object under test is in real operating conditions. Based on previous studies, this research team uses a 2.4G wireless transmission scheme to realize short-distance wireless transmission of aero-engine vibration signals, and a 4G cellular communication scheme to realize long-distance wireless transmission of aero-engine vibration signals. The acquired signals are displayed and monitored using the tool developed in LabVIEW2022. The designed tool is successfully applied in the engineering practice of engine maintenance, realizing the remote real-time monitoring of the engine operation status, shortening the monitoring cycle of the equipment from hourly to real-time level, and greatly facilitating the technical support work of engineers and technicians.

Sensitivity analysis for multi-measurement points based SHM in the mooring lines of floating offshore wind turbines

Abstract Structural health monitoring in floating offshore wind turbines’ mooring lines is vital for detecting early faults and preventing disruptions. Currently, sporadic and expensive monitoring is conducted via remote-operating vehicles. Efficient, automated, economical monitoring methods based on a continuous stream of data acquired via multiple measurement points in the wind turbine, are required. Such methods based on vibration signals have been investigated limitedly for steel chains. Recently, faulty synthetic mooring lines of a simulated 10MW semi-submersible wind turbine have been detected under varying environmental conditions and via the Functional Model Based Method (FMBM) equipped with functional models. The uncertainty due to the conditions’ stochasticity has been addressed by training the models using ten signal realizations per condition (wind) under the healthy wind turbine and from each of two measurement points. The current study presents a preliminary sensitivity analysis of the FMBM’s capability in detecting the simulated wind turbine’s faulty mooring lines when the functional models are trained using one signal realization per varying condition (wind) from each measurement point. The method’s effectiveness is evaluated with acceleration signals from 11 healthy/ 66 faulty (reduced stifness in one mooring line) cases. The FMBM detects all cases even when trained using one signal realization.

Investigating bearing and gear vibrations with a Micro-Electro-Mechanical Systems (MEMS) and machine learning approach

Bearings and gears are the pivotal components of mechanical systems and are prone to faults that can impact the system's overall performance. These components' condition monitoring and fault diagnosis are vital for maintaining system reliability and efficiency. In this research, a MEMS setup is initially developed, comprising a Raspberry Pi 4B+ CPU module, a NucleoF401RET6 MCU, an OLED screen, and an Adxl1002z accelerometer for acquiring vibration signals at the desired sampling frequency stored in the CPU memory. Further, an RF model is also developed to classify different types of faults based on features extracted from the acquired vibration data. The model evaluates the precision and reliability of the MEMS setup in capturing and classifying vibration signals. A detailed signal analysis is also conducted to determine the performance of the developed MEMS setup and to investigate the effect of bearing vibration signature due to gear fault and vice versa. The results indicate that bearing faults cause irregularities in the shaft's rotational speed, leading to modulation of the gear mesh frequency (gmf) of gears mounted on the affected shaft. Conversely, gear faults disrupt the shaft's rotational motion, imposing excessive loads on shaft-supported bearings. These disruptions result in distinct vibration patterns characterised by increased harmonics and side bands within the bearing frequency range. The RF model effectively identifies and classifies faults with high accuracy by leveraging its ability to prioritise the most significant vibrational features, resulting in improved predictive performance and robustness.

Rolling bearing fault diagnosis based on acoustic-vibration data fusion and mode decomposition combined with the crested porcupine optimization algorithm

Variational Mode Decomposition (VMD) is extensively utilized in the domain of rotating machinery fault diagnosis. Nevertheless, the reliance on empirical parameter tuning and the limitations inherent in traditional single-signal (either vibration signals or acoustic signals) fault diagnosis methods present substantial challenges. These include incomplete information and high sensitivity to noise. To address these shortcomings, a new method is introduced that integrates multi-modal sensor data and implements an adaptive VMD approach, enhanced through the Crested Porcupine Optimization Algorithm (CPO) to automatically optimize the key parameter. Then, uses optimized VMD to decompose the time series into intrinsic mode functions of different frequencies to capture the multi-scale characteristics of data. Finally, the decomposed acoustic-vibro signal components are fed into the CNN-BiGRU-AT network, which learns the spatio-temporal dependencies to enhance the accuracy of fault classification. The experimental results conducted across diverse high-noise environments demonstrate that this method’s superior noise robustness compared to single-modality sensor approaches， highlighting the method's potential for machinery fault detection in noisy conditions.

Diagnosis of faults using a new VMD-WMRA approach, optimized by a new criterion: Industrial application

This paper proposes a new criterion for selecting the optimum number of Intrinsic Mode Functions (IMFs) from vibration signals measured on rotating machines. The criterion is based on calculating the variation of normalized Shannon entropy ([Formula: see text]) between two successive IMFs, enabling the optimization of the number of processed IMFs. The method combines Variational Mode Decomposition (VMD) with Wavelet Multi-Resolution Analysis (WMRA) using the newly introduced criterion. The key steps involve determining the optimal number of IMFs, decomposing the vibration signals using VMD, and applying WMRA to the selected IMFs. Numerical simulations and experimental results demonstrate its effectiveness in identifying various faults, including gear defects, shaft misalignment, insufficient backlash, and belt defects. These latter flaws are the primary cause of the elevated noise levels in the measured signals. Finally, to validate our approach in an industrial setting, we diagnosed a turbofan, which revealed the presence of several typical faults for this type of installation. The proposed approach addresses a critical industry need by increasing fault diagnosis accuracy in noisy environments. Its practical impact stems from its ability to improve early fault detection, which is critical for predictive maintenance strategies aimed at reducing equipment downtime and extending its lifespan.

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 rolling bearing fault diagnosis method for imbalanced data based on multi-scale self-attention mechanism and novel loss function

Deep learning methods are widely used in the field of rolling bearing fault diagnosis and produce good results when faced with datasets with roughly equal numbers of normal and faulty samples. However, real-world data often has a serious imbalance, with the number of fault samples being significantly less than the number of normal samples. This dataset imbalance challenges the performance of traditional deep learning methods. To address this problem, this paper proposes an efficient imbalanced data rolling bearing fault diagnosis method. The method consists of two parts: a deep learning network based on a multi-scale self-attention mechanism and a novel loss function. In terms of the deep learning network, firstly, the one-dimensional vibration signal is converted into a two-dimensional image through the Gramian angular field. This conversion maximises the inherent feature extraction capability of the network. Subsequently, the multi-scale learning capability of the network is enhanced by implementing different expansion rates for the head of the multi-scale self-attention mechanism. This nuanced approach allows the network to capture the underlying information more efficiently. Finally, the inclusion of Ghost bottlenecks and feature pyramid networks (FPNs) helps to optimise network efficiency and improve generalisation performance. A novel loss function is also proposed to make the method more suitable for imbalanced data. During the training process, the classification of samples in each class is analysed using the recall metric of imbalanced classification and the real-time recall is used as a weight to weaken the dominance of the majority class. This weighting ensures the adaptability of the method to imbalanced datasets. The proposed method is evaluated using rolling bearing datasets from Case Western Reserve University, USA, and Southeast University, China. Comparison results with other state-of-the-art deep learning methods show that the proposed method has a robust performance when dealing with imbalanced data.

Locating method of buried PE pipeline based on vibration signal analysis

Abstract This paper proposes a buried PE gas pipeline positioning method based on vibration signal analysis. Firstly, the collected signal is denoised using the Dual-tree complex wavelet transform (DTCWT) method, and then the delay time of the denoised collected signal is extracted through the cross-correlation function to locate the buried PE pipeline. A simulation model is established on this theoretical basis, and sensor asymmetric and symmetric arrangements are simulated in COMSOL to observe the peak values of the collected signal in the time domain, determining the relationship between the time difference of the two signals and the pipeline position. Through simulation, it is known that the pipeline location can be judged by the magnitude of the delay time. The method proposed in this paper addresses the shortcomings of existing pipeline positioning methods and provides a basis for future pipeline positioning.

ICEEMDAN and improved wavelet threshold for vibration signal joint denoising in OPAX

ICEEMDAN and improved wavelet threshold for vibration signal joint denoising in OPAX

Research on design method of inertial interference self-compensating balance

This paper proposes a design method for a wind tunnel balance that can compensate for inertial interference, aiming to address the issue of short effective time in pulse wind tunnels. This method also tackles the problem of the force measuring system’s inability to dampen quickly and the coupling of inertia vibration signals with the output signal of the balance, which affects aerodynamic measurement accuracy. The method calculates vibration displacement at specific positions of the balance structure using its acceleration, constructs compensation signals based on the relationship between these displacements and balance outputs, and then superimposes them onto the balance signal for compensation. The effectiveness of this compensation method is evaluated through finite element simulations and wind tunnel tests. The results demonstrate that by compensating for the balance signal, there is a significant reduction in the amplitude of the inertia vibration signal, leading to an enhancement in the repeatability accuracy of the measurement. This compensation method effectively solves under-compensation or over-compensation issues when accelerometers are placed on complex models with various modes, where obtaining accurate compensation signals becomes challenging. It provides a new technical approach to improve aerodynamic measurement accuracy by mitigating inertial interference during pulse wind tunnel testing.

Application of the Multifractal Spectrum to the Analysis of Vibration Signals Generated During Testing of Selected Composite Materials

Application of the Multifractal Spectrum to the Analysis of Vibration Signals Generated During Testing of Selected Composite Materials

A comprehensive method to analyze single-cell vibrations

All living cells vibrate depending on metabolism. It has been hypothesized that vibrations are unique for a given phenotype and thereby suitable to diagnose cancer type and stage and to pre-assess the effectiveness of pharmaceutical treatments in real time. However, cells exhibit highly variable vibrational signals, can be subject to environmental noise, and may be challenging to differentiate, having so far limited the phenomenon’s applicability. Here, we combined the sensitive method of force spectroscopy using optical tweezers with comprehensive statistical analysis. After data acquisition, the signal was decomposed into its spectral components via fast Fourier transform. Peaks were parameterized and subjected to principal-component analysis to perform an unbiased multivariate statistical evaluation. This method, which we term cell vibrational profiling (CVP), systematically assesses cellular vibrations. To validate the CVP technique, we conducted experiments on five U251 glioblastoma cells, using 8- to 10-μm polystyrene beads as a control for comparison. We collected raw data using optical tweezers, segmenting into 150+ 5-s intervals. Each segment was converted into power spectra representing a frequency resolution of 10,000 Hz for both cells and controls. U251 glioblastoma cells exhibited significant vibrations at 402.6, 1254.6, 1909.0, 2169.4, and 3462.8 Hz (p < 0.0001). This method was further verified with principal-component analysis modeling, which revealed that, in cell-cell comparisons using the selected frequencies, overlap frequently occurred, and clustering was difficult to discern. In contrast, comparison between cell-bead models showed that clustering was easily distinguishable. Our paper establishes CVP as an unbiased, comprehensive technique to analyze cell vibrations. This technique effectively differentiates between cell types and evaluates cellular responses to therapeutic interventions. Notably, CVP is a versatile, cell-agnostic technique requiring minimal sample preparation and no labeling or external interference. By enabling definitive phenotypic assessments, CVP holds promise as a diagnostic tool and could significantly enhance the evaluation of pharmaceutical treatments.

Enhancing gearbox fault diagnosis under phase estimation errors: A dynamic time warping and blind deconvolution approach

The blind deconvolution technique is widely used to extract subtle fault-related features from the noisy vibration signals of rotating machinery. However, utilizing conventional Minimum Entropy Deconvolution (MED) methods is challenging for analyzing gearboxes, where significant internal and external non-Gaussian disturbance signals are inherent. Although periodicity analysis with signal alignment can be employed to solve this challenge, most available methods require exact phase information for the signals; this must be measured by a specially installed encoder system. To address this issue, this paper introduces a novel method for the fault diagnosis of gearboxes that effectively addresses the significant challenge of extracting periodic fault characteristics from noisy vibration signals, particularly in scenarios characterized by significant phase estimation errors and without the need for an encoder system. By combining the strengths of blind deconvolution with Dynamic Time Warping (DTW)-based sequential signal alignment, our method facilitates the identification and alignment of samples with identical rotational phases. This approach enables the distinction between impulsive features caused by faults and those resulting from noise, thus overcoming the limitations of conventional techniques, which fail to extract periodic fault characteristics under phase estimation errors. The effectiveness of the proposed approach is verified through analytic simulation and experimental validation using data from 2-kW planetary gearbox testbeds at Seoul National University (SNU) and the Korea Research Institute of Standards and Science (KRISS). Results demonstrate the superiority of the proposed method for identifying fault conditions under significant phase estimation errors compared to conventional methods.

Vibration Signal Analysis for Intelligent Rotating Machinery Diagnosis and Prognosis: A Comprehensive Systematic Literature Review

Many industrial processes, from manufacturing to food processing, incorporate rotating elements as principal components in their production chain. Failure of these components often leads to costly downtime and potential safety risks, further emphasizing the importance of monitoring their health state. Vibration signal analysis is now a common approach for this purpose, as it provides useful information related to the dynamic behavior of machines. This research aimed to conduct a comprehensive examination of the current methodologies employed in the stages of vibration signal analysis, which encompass preprocessing, processing, and post-processing phases, ultimately leading to the application of Artificial Intelligence-based diagnostics and prognostics. An extensive search was conducted in various databases, including ScienceDirect, IEEE, MDPI, Springer, and Google Scholar, from 2020 to early 2024 following the PRISMA guidelines. Articles that aligned with at least one of the targeted topics cited above and provided unique methods and explicit results qualified for retention, while those that were redundant or did not meet the established inclusion criteria were excluded. Subsequently, 270 articles were selected from an initial pool of 338. The review results highlighted several deficiencies in the preprocessing step and the experimental validation, with implementation rates of 15.41% and 10.15%, respectively, in the selected prototype studies. Examination of the processing phase revealed that time scale decomposition methods have become essential for accurate analysis of vibration signals, as they facilitate the extraction of complex information that remains obscured in the original, undecomposed signals. Combining such methods with time–frequency analysis methods was shown to be an ideal combination for information extraction. In the context of fault detection, support vector machines (SVMs), convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, k-nearest neighbors (KNN), and random forests have been identified as the five most frequently employed algorithms. Meanwhile, transformer-based models are emerging as a promising venue for the prediction of RUL values, along with data transformation. Given the conclusions drawn, future researchers are urged to investigate the interpretability and integration of the diagnosis and prognosis models developed with the aim of applying them in real-time industrial contexts. Furthermore, there is a need for experimental studies to disclose the preprocessing details for datasets and the operational conditions of the machinery, thereby improving the data reproducibility. Another area that warrants further investigation is differentiation of the various types of fault information present in vibration signals obtained from bearings, as the defect information from the overall system is embedded within these signals.

DIAGNOSIS OF ROLLING BEARING DEFECTS BASED ON WAVELET ANALYSIS

Purpose. Development and improvement of a method for analyzing vibration signals from rolling bearings based on wavelet analysis for the detection and identification of equipment defects. Research methods. Wavelet analysis was employed for processing vibration signals from rolling bearings. Threshold wavelet filtering was applied to highlight weak impulse components in the signals, and Morlet wavelet was used to ensure effective filtration. Results. The research results indicate that the proposed method, utilizing wavelet filtering, enhances the speed and reliability of vibration diagnostics for bearings. This allows for the efficient extraction of characteristic frequencies associated with various types of rolling bearing defects. In comparison with other signal analysis methods, the use of the developed method based on continuous wavelet analysis has proven to be particularly effective in extracting characteristic diagnostic frequencies. This method not only allows for the identification of specific types of defects in rolling bearings but also ensures universality, enabling its successful application for analyzing other types of non-stationary signals. Experimental studies have confirmed the high efficiency of the developed method, especially in the early stages of defect development. The application of this method is evident not only in its ability to effectively highlight the characteristic frequencies of bearings but also in its capacity to conduct signal analysis for the identification of equipment defects as a whole. This makes the proposed method a promising and versatile tool for the diagnosis and monitoring of the condition of technical systems. Scientific novelty. Application of the proposed method for processing vibration signals from rolling bearings based on wavelet analysis to improve the effectiveness of defect detection and identification in equipment. Practical value. The developed method can be applied in the industrial sector for the analysis and diagnostics of rolling bearings in equipment. It enables the timely detection of defects, reduces the risk of equipment failure, and lowers operational maintenance costs. Thus, this method has practical value in enhancing the reliability and productivity of industrial equipment.

Intelligent Fault Diagnosis of Planetary Gearbox Across Conditions Based on Subdomain Distribution Adversarial Adaptation.

Sensory data are the basis for the intelligent health state awareness of planetary gearboxes, which are the critical components of electromechanical systems. Despite the advantages of intelligent diagnostic techniques for detecting intricate fault patterns and improving diagnostic speed, challenges still persist, which include the limited availability of fault data, the lack of labeling information and the discrepancies in features across different signals. Targeting this issue, a subdomain distribution adversarial adaptation diagnosis method (SDAA) is proposed for faults diagnosis of planetary gearboxes across different conditions. Firstly, nonstationary vibration signals are converted into a two-dimensional time-frequency representation to extract intrinsic information and avoid frequency overlapping. Secondly, an adversarial training mechanism is designed to evaluate subclass feature distribution differences between the source and target domain. A conditional distribution adaptation is employed to account for correlations among data from different subclasses. Finally, the proposed method is validated through experiments on planetary gearboxes, and the results demonstrate that SDAA can effectively diagnose faults under crossing conditions with an accuracy of 96.7% in diagnosing gear faults and 95.2% in diagnosing planet bearing faults. It outperforms other methods in both accuracy and model robustness. This confirms that this approach can refine domain-invariant information for transfer learning with less information loss from the sub-class level of fault data instead of the overall class level.

A New Type of Dynamic Vibration Fiber Sensor.

A new-type vibration sensor based on a fiber Bragg grating combined with a special structure-packaged design is proposed for monitoring the mechanical vibration signals. Three different sensing structures, including the film squeeze type, new film squeeze type, and elastic tape squeeze type are proposed for measuring the vibration signals with the frequency range from tens to thousands of Hz. In the comparison to experimental results, the new film squeeze structure has a nice sensing performance in the range from 100 to 1000 Hz with a sensitivity of 0.302 mV/g. For the elastic tape squeeze structure, the elastic tape is designed to encapsulate the optical fiber with a good frequency response from 1100 to 3100 Hz. In addition, by using the new film squeeze structure to measure the steady-state and non-steady-state vibration signals, the spectral components of sensing signals are analyzed by using the wavelet transformation for confirming the testing signals. These vibration fiber sensors can be applied in the measurement of high-end manufacture-facility vibration or earthquake vibrations etc.

Modeling of drilling tool vibration in the process of drilling blast wells

Purpose. To determine the characteristic features and model bit vibration during its interaction with rock in the process of drilling technical (blast) wells in open pit mines. Methodology. The following methods were used in the work: analysis of scientific and practical solutions; statistical methods for processing the results of experimental studies; methods of analytical synthesis; computer modeling methods for synthesis and analysis of mathematical models. Findings. The process of interaction between a drill bit and a rock was analyzed. The conditions and causes of vibration of drilling equipment are determined. The spectral analysis of the vibration signal, the formation and analysis of the map of the order of rotation of the rotating parts of the drilling rig during the drilling of technical (blast) wells were performed to identify the bit in the frequency domain of the measured concomitant integrated vibration signal as a source of high-amplitude vibration. The modulated signal measured in the time domain at the specified frequency carries information about the physical and mechanical characteristics of the drilled rock and the state of the bit. The analysis of the experimental studies and modeling of the process of interaction of the bit with the rock allows us to conclude that the obtained statistical indicators of the accompanying vibration signal really adequately characterize the process of well drilling. Originality. A method for determining the characteristics of the interaction of a drill bit with rock in the process of drilling technical (blast) wells based on measuring the parameters of the accompanying vibration signal is proposed. The method differs from the known ones in the fact that in the process of changing the operating mode of the drive of the rotating parts of the drilling rig, an order map is formed over the entire range of its revolutions, the frequency of high-amplitude vibration of the bit is determined, which corresponds to a certain peak order of revolutions, and at this frequency the statistical parameters of changes are measured. Practical value. This approach to the process of drilling wells in open pit mines makes it possible to quickly determine the physical and mechanical characteristics of the drilled rock and adjust the process parameters accordingly to increase its productivity and energy saving.

The unsupervised bearing fault diagnosis method based on the dual-framework Siamese network

Abstract Aiming at the problem of insufficient bearing fault samples in practical engineering, an unsupervised bearing fault diagnosis method with double frame twin network is proposed in this paper. This method only uses the source domain data to diagnose the target domain data, and can minimize the problem of domain difference. The concrete method is firstly to carry on continuous wavelet transform to the rolling bearing signal, and the processed signal is used as the input feature of convolutional neural network. The twin network structure is optimized and Manhattan distance is used as the loss function in the training process. The proposed CM network is applied to the diagnosis of inner ring fault, outer ring fault and normal state of rolling bearing. The results show that CM network can achieve over 90% accuracy on SQ variable speed vibration signal data set (SQV), Shijiazhuang Railway University data set (STD) and Case Western Reserve University data set (CWRU), which is suitable for bearing fault diagnosis under different working conditions and provides a new direction for fault diagnosis. The PyTorch code is available at https://github.com/xiaotianqu/The-unsupervised-bearing-fault-diagnosis-method-based-on-the-dual-framework-Siamese-network.