Vibration Signal Analysis Research Articles

Modern methods and means of technical diagnostics of artillery weapons and prospects for their development

The analysis of full-scale military operations in the east of Ukraine indicates an increase in the variety of firing tasks for artillery weapons and the intensity of their execution. This, in turn, requires the maintenance of artillery weapons and systems that ensure their proper operation in a serviceable and ready-to-use condition, as well as the prevention of sudden breakdowns and failures during combat missions.
 Taking into account the influence of the technical condition on the effectiveness and safety of the combat use of artillery weapons, there is a need to improve the methods and means of its diagnosis.
 This article analyzes the available domestic and foreign studies devoted to the development and improvement of methods for diagnosing the technical condition of artillery weapons. Existing devices for measuring, inspecting, evaluating and controlling the technical condition are considered. The existing systematization was expanded, generalization and addition of known methods and tools was carried out. The main possibilities, advantages and disadvantages of known methods, as well as promising directions for further research are determined.
 It has been established that the majority of studies of possible methods of diagnosing the technical condition of artillery weapons are focused mainly on the analysis of the parameters of only the gun barrel and do not consider the ability of the studied methods to assess the technical condition of artillery weapons comprehensively.
 The analysis also showed that despite the large number of existing techniques for diagnosing the technical condition (determining the degree of wear), most of them are not suitable for use directly in field conditions, morally outdated or very expensive.
 In addition, it was determined that the application of the analysis of acoustic and vibration signals that occur during a shot is a promising direction for diagnosing the technical condition of artillery weapons, which potentially has the ability to detect more malfunctions, breakdowns and deviations of technical parameters.

Research on Micro-Turbine Operating State Characterization Based on Bearing Vibration Signals Analysis

An air flow-driven micro-turbine, widely used in air-condition control systems in aircraft cabins, train coaches, etc., exhibits complex vibration behaviors under stable or unstable inlet flow conditions, and especially has a certain correlation with speeds. In this paper, the vibration responses of the micro-turbine undergoing stable and unstable inlet flows measured on a test rig are analyzed and compared by using different signal processing methods, which include time and frequency domain methods, and statistical and nonlinear methods. First, the test rig system of the airflow-driven micro-turbine, the instrument system and four typical experimental cases are introduced. Then the measured vibration signals are analyzed and compared by time domain characteristic parameters (peak-to-peak value, RMS value and kurtosis value), statistical parameters (auto-correlation and BDS), and amplitude spectra in the frequency domain, statistic spectrum indicators (SSI) based on Welch's periodogram of power spectra, and the spectra of selected IMF components based on Hilbert-Huang Transform (HHT). In particular, some nonlinear feature analyzing methods, including Pseudo-Poincare mapping diagrams and Lempel-Ziv(LZ) complexity, are also used for analyzing measured vibration responses. The obtained results using the above multiple methods are compared and show that, when the inlet flow of the turbine fluctuates significantly, the nonlinear characteristics of the turbine bearings are significantly higher than those of the relatively stable inlet flow and speed conditions. Under these circumstances, commonly used time-frequency analysis methods cannot characterize the different speed operating state of the turbine, and LZ-Complexity and other nonlinear characterization methods should be used to better understand the characteristics of different speeds under unstable conditions.This study provides references for the aerodynamic stability monitoring of the micro-turbine and its design improvement.

Multi-Rate Vibration Signal Analysis for Bearing Fault Detection in Induction Machines Using Supervised Learning Classifiers

Vibration signals carry important information about the health state of a ball bearing and have proven their efficiency in training machine learning models for fault diagnosis. However, the sampling rate and frequency resolution of these acquired signals play a key role in the detection analysis. Industrial organizations often seek cost-effective and qualitative measurements, while reducing sensor resolution to optimize their resource allocation. This paper compares the performance of supervised learning classifiers for the fault detection of bearing faults in induction machines using vibration signals sampled at various frequencies. Three classes of algorithms are tested: linear models, tree-based models, and neural networks. These algorithms are trained and evaluated on vibration data collected experimentally and then downsampled to various intermediate levels of sampling, from 48 kHz to 1 kHz, using a fractional downsampling method. The study highlights the trade-off between fault detection accuracy and sampling frequency. It shows that, depending on the machine learning algorithm used, better training accuracies are not systematically achieved when training with vibration signals sampled at a relatively high frequency.

Chatter failure comparison for high-speed milling of aerospace GH4099 with silicon nitride ceramic end mills: A laboratory scale investigation

Chatter generated in the high-speed cutting process can seriously limit the machining efficiency and affect the surface machining quality, which urgently needs to be controlled. Aiming at brittle ceramic end mills that are very sensitive to vibration, a strategy based on the modal experimental method is proposed to construct effective and accurate stability lobe diagram (SLD) for chatter prediction and failure analysis in this work. Firstly, the tool tip frequency response function (FRF) of four kinds of self-developed ceramic end mills and commercial cemented carbide end mills with the same structure are established by means of hammering modal experiments, and the modal parameters are solved and identified. Then, based on our established high-speed milling chatter prediction model, the milling force coefficients are obtained by linear regression analysis and calculation, thereby the milling SLDs of five end milling tools are developed. Finally, a series of high-speed side milling experiments are carried out using consistent cutting conditions. The results show that the constructed SLDs can effectively predict the chatter failure region and milling stability for five kinds of end mills, and four ceramic milling tools exhibit more excellent chatter suppression performance according to frequency domain analysis of vibration signals and the observation of workpiece surface vibration marks, in which the S4 ceramic milling tool presents the most significant high-speed milling stability.

Robust prediction of remaining useful lifetime of bearings using deep learning

Predicting the remaining useful lifetime (RUL) of bearings in electric motors is crucial to reduce repair costs in industrial maintenance. With the technological advances of Industry 4.0, physical models for prognostics and RUL prediction have been replaced by data-driven models that require no expert feature extraction. Instead, the model itself learns which features are important. However, these models are normally trained and tested on the same dataset, i.e. under the same operating conditions. This limits the application of a model to other operating conditions unless the model is fine-tuned with data corresponding to those conditions. This paper proposes a novel robust health prognostics technique that detects inner-race bearing failures and predicts the RUL of electric motor bearings under various motor conditions without model retraining or fine-tuning. The model combines time and frequency-domain vibration signal analyses to extract features, a stacked variational denoising autoencoder (SVDAE) to fuse these features and build a Health Indicator and a bidirectional long short-term memory (BiLSTM) neural network to predict the remaining useful lifetime of the bearings. The proposed model is trained with a dataset, validated with another dataset and finally tested with seven additional datasets corresponding to vibrations gathered from different motors and operating conditions. The results are more robust and accurate than those of the literature, the robustness of the prediction with different motor and operating conditions is proven, and there is no need to retrain or fine-tune the model, making the proposed model suitable for recently installed equipment.

Advantages of periodic non-stationary random process model in vibration signal processing

The use of two different techniques for the analysis of vibration signals, whose carrier harmo¬nics are modulated by high-frequency narrow-band random processes is discussed. Periodically non-stationary random processes (PNRP) are suitable models for description of vibration signals of damaged mechanism. A proposed processing technique can be considered as an alternative to squared envelope analysis, kurtosis techniques, squared envelope spectrum (SES) and its use in the analysis of a vibration signal is discussed. It is shown that the spectral estimates obtained by the envelope square method are biased and inconsistent. The possibility of obtaining of the unbiased estimates by the PNVP method even for a signal/noise ratio equal to 0.07 has been demonstrated.

Research progress on bearing fault diagnosis with signal processing methods for rolling element bearings

As a prerequisite for rotating machinery to operate effectively, rolling element bearings play an essential role. The focus of condition monitoring has initially been on defect identification, then on its measurement, and eventually on automatic defect prediction. The improvement in signal processing has made this breakthrough possible. The quality of characteristics taken from the bearing signals strongly impacts how effective these techniques are. Aiming to provide the researchers with the option to choose and implement the optimum signal analysis method, the authors have described numerous signal processing techniques used to diagnose faults in rolling element bearings. The research study examines several important studies and explains their relevance to locating rolling bearing defects. It analyzed recent research, ones from the past, and developments in the field of diagnosing bearing defects. The main goal of the research is to investigate different vibration signal processing and analysis methods for locating and evaluating bearing faults. After that, each of these subjects is rigorously analyzed in order to draw conclusions, spot new trends, and pinpoint areas that still need more research. This article is meant to serve as a guide for those who operate in the condition monitoring domain.

The improved variational nonlinear chirplet mode decomposition via local maximum synchrosqueezing transform and recursive mode extracting scheme for robust estimation of nonlinear chirplet modes and application to fault detection of rotary machine

As an advanced time-frequency (TF) decomposition (TFD) method, variational nonlinear chirplet mode decomposition (VNCMD) decomposes the original signal into a series of nonlinear chirplet modes (NCMs), such that the inherent characteristic information contained in the signal can be revealed effectively. However, the decomposition ability of VNCMD is largely affected by the prior instantaneous frequency (IF) and the pre-set parameters. In practical engineering applications, the presence of noise and interference components often complicates the accurate determination of prior IFs and appropriate decomposition parameters. Considering the above issues, in order to precisely extract the NCMs and realize the effective analysis of mechanical vibration signals, this paper mainly focuses on the drawbacks of accurate prior IF and the decomposition parameters of VNCMD, and proposed an improved version via local maximum synchrosqueezing transform and a recursive mode extracting scheme. The performance of the proposed method is evaluated through simulation cases, and the results demonstrate its effectiveness. Finally, the proposed method is successfully applied to bearing data analysis and rub-impact fault detection.

Self-matching extraction fractional wavelet transform for mechanical equipment fault diagnosis

Time-frequency analysis (TFA) of vibration signals is an important way for fault diagnosis and predictive maintenance of mechanical equipment. However, the existing TFA methods, such as wavelet transform (WT) and synchrosqueezing transform, are incapable to describe local details of strongly time-varying signals, bringing such problems as time-frequency energy dispersion. Theoretically, it is more beneficial for time-varying band-limited signals to make multi-resolution analysis in the time-fraction frequency domain because such action can increase the selection chances of mother wavelets in any direction on the time-frequency plane by changing the angle . In order to improve the ability of analyzing strongly time-varying signals and the readability of the time-frequency plane, this paper has proposed a novel TFA method called self-matching extraction fractional WT (SMEFRWT). Firstly, within the theoretical framework of fractional WT (FRWT), a minimum entropy-based method is proposed to choose an optimal angle in the fractional domain. Such action can obtain the best aggregation angle of time-frequency energy and facilitate the separation of multi-component signals. Then, self-matching extraction operator, one instantaneous-frequency estimated operator that match the time-frequency structure of amplitude modulation signals, is constructed. The operator allows for the distribution of time-frequency energy of fast-changing signals in both frequency and time directions, making it possible to match the time-varying vibration signals from mechanical equipment more accurately. Finally, SMEFRWT is adopted to make TFA and extract fault features of the faulty experiment benches on gear and bearings, with its feasibility validated by experimental results.

Research on Improved Fault Detection Method of Rolling Bearing Based on Signal Feature Fusion Technology

Rolling bearings are the core transmission components of large-scale equipment. Once a fault occurs, the consequences may be catastrophic, posing a serious threat to life and the safety of the property. Aimed at the problem of rolling bearing faults, this paper analyzes the characteristics of different fault signals and proposes a fault diagnosis method based on fuzzy signals. Based on the definition of an incomplete mapping of a new connotation, this paper proposes a fuzzy fault diagnosis method by fuzzy mapping, gives a fuzzy signal processing algorithm, and discusses two judgment principles. Further, it carries out a vibration signal analysis of the rolling bearing. According to the fault diagnosis method in this paper, the fault rolling bearing is diagnosed. The experimental results show that the proposed method can effectively diagnose rolling bearing faults closer to their natural attributes and solves the problem of traditional generator bearing fault diagnosis that requires complex models and poor diagnosis speed. Further, it can be seen that the average time consumption of this method is reduced and the fault recognition accuracy rate is increased. Compared with the existing related methods, this proposed diagnosis method is superior to that of several existing methods. It not only has higher precision, stronger anti-noise capacity, and faster diagnosis speed, but also has lower effective information loss.

Variational Mode Decomposition based Vibro-Acoustic Analysis for Spur Gear Fault Detection

Feature extraction from dynamic response characteristics of the machinery serves as an index for quantifying its health status. Vibration response analysis based on the spectral distribution of gear-mesh frequency is the industrial norm for gearbox fault detection. Investigations based on the efficacy of other responses, such as sound generated by the gearbox, are limited. The air pressure induced around the vicinity of the machinery may also indicate the health status. This paper studies the applicability of sound pressure signals as the basis for gear fault detection. The paper also investigates the efficacy of combined sound and vibration analysis for fault detection. The fault detection ability of the above-mentioned single-signal analysis and combined-signal analysis are compared with traditional vibration signal analysis. Variational Mode Decomposition and Multivariate Variational Mode Decomposition are used to extract noise-free signals centred around the required centre frequency in the case of single-signal analysis and a combined signal analysis, respectively. The study is conducted on an in-house test rig with a seeded gear tooth fault.

Optimizing Failure Diagnosis in Helical Gear Transmissions with Stochastic Gradient Descent Logistic Regression using Vibration Signal Analysis for Timely Detection

Optimizing Failure Diagnosis in Helical Gear Transmissions with Stochastic Gradient Descent Logistic Regression using Vibration Signal Analysis for Timely Detection

Improved multi-order Vold-Kalman filter for order tracking analysis using split cosine and sine terms

Order tracking analysis is an effective tool for the analysis of non-stationary vibration and acoustical signals for the purposes of machinery fault diagnosis and operational modal analysis. The second-generation Vold-Kalman filter is widely used for order tracking analysis; however, the formulation of the data equation is based on incomplete complex kernels, which introduce an imaginary part error in the cost function. In this work, the data equation of the second generation Vold-Kalman filter is re-formulated using separated cosine and sine kernels that necessarily provide a real-valued sum. Doing so will minimize the error in the data equation and offer better optimization scheme to reduce the error in the structural equation and; hence, provide smooth envelopes. Unlike Vold-Kalman method which requires high filter weighting factors to suppress envelope artifacts, the proposed method can achieve high accuracy even when using small weighting factor as deduced from the simulation and experimental investigations. The proposed method can be used to measure the envelopes of close and crossing orders, as well as orders experiencing resonance conditions or orders with high slew rate due to lower filter decay time.

Comprehensive analysis of aero-engine vibration signals based on wavelet transform method

A single type of signal processing means that it is difficult to analyze vibration signals comprehensively and effectively. By comprehensively using wavelet analysis techniques, a comprehensive and in-depth study of aero-engine vibration conditions is realized as a way to carry out health management. By introducing various types of wavelet analysis techniques and using Labview2022 programming, corresponding signal processing tools are developed for the analysis of the collected vibration signals. The comprehensive analysis of aero-engine vibration signals based on the wavelet transform method is realized, and the corresponding products are successfully applied in engineering practice.

Time-Frequency Analysis and Neural Networks for Detecting Short-Circuited Turns in Transformers in Both Transient and Steady-State Regimes Using Vibration Signals

Transformers are vital elements in electrical networks, but they are prone to various faults throughout their service life. Among these, a winding short-circuit fault is of particular concern to researchers, as it is a crucial and vulnerable component of the transformers. Therefore, if this fault is not addressed at an early stage, it can increase costs for users and affect industrial processes as well as other electrical machines. In recent years, the analysis of vibration signals has emerged as one of the most promising solutions for detecting faults in transformers. Nonetheless, it is not a straightforward process because of the nonstationary properties of the vibration signals and their high-level noise, as well as their different features when the transformer operates under different conditions. Based on the previously mentioned points, the motivation of this work is to contribute a methodology that can detect different severities of short-circuited turns (SCTs) in transformers in both transient and steady-state operating regimes using vibration signals. The proposed approach consists of a wavelet-based denoising stage, a short-time Fourier transform (STFT)-based analysis stage for the transient state, a Fourier transform (FT)-based analysis stage for the steady-state, the application of two fault indicators, i.e., the energy index and the total harmonic distortion index, and two neural networks for automatic diagnosis. To evaluate the effectiveness of the proposed methodology, a modified transformer is used to experimentally reproduce different levels of SCTs, i.e., 0-healthy, 5, 10, 15, 20, 25, and 30 SCTs, in a controlled way. The obtained results show that the proposed approach can detect the fault condition, starting from an initial stage for consolidation and a severe stage to accurately assess the fault severity, achieving accuracy values of 90%.

Unraveling air–water two-phase flow patterns in water pipelines based on multiple signals and convolutional neural networks

Abstract Flow pattern identification (FPI) is crucial for evaluating air entrapment in water pipelines and ensuring the safety of pipeline operations. The presence of two-phase flow in water pipelines not only leads to pressure fluctuations but also induces pipeline vibration. However, current research has primarily focused on using pressure-related signals for FPI, and the analysis of vibration signals in FPI is rare. In this study, FPI in water pipelines is investigated based on convolutional neural networks (CNNs) using high-frequency vibration signals. The information fusion of vibration signals in FPI is newly proposed via the stacked generalization technique. The proposed method is compared with pressure signal-based FPI methods and the effect of signal sampling parameters on FPI accuracy is discussed. The results show that the performance of vibration signals (including axial or radial acceleration signals) outperforms pressure signals in both time and frequency domains. Moreover, the fusion of vibration signals shows the superior results compared to any univariate signals. The duration of sampling has a more significant impact on the results of FPI than the sampling frequency. This study provides a new way that FPI theory is applied to solve air entrapment evaluation in water pipelines.

Intelligent fault diagnosis of rotating machinery under variable working conditions based on deep transfer learning with fusion of local and global time–frequency features

With the development of deep learning methods, the data-driven fault diagnosis methods have attracted a great deal of interest. However, as for the data-driven fault diagnosis methods, technology has to overcome various difficulties in the practical industrial scenarios, such as variable working conditions, insufficient effective samples, and environmental noise interference. Combining with the time–frequency analysis of vibration signals, a domain adaptation fault diagnosis model based on ResNet and Transformer (DAFDMRT) is proposed in this work, aiming to solve the problems encountered by current rotating machinery fault diagnosis methods in the field of application. Firstly, the vibration signal is processed by wavelet packet transform and the time–frequency information grayscale maps is constructed. Next, a deep fusion feature extraction network combining ResNet and Transformer encoder, is designed for the extraction and fusion of the local and global features of multi-scale time–frequency information. Finally, the multi-kernel maximum mean discrepancy is applied to measure and minimize the distribution difference between the deep features of source and target domain, thereby improving the diagnostic performance of the diagnosis model in variable working conditions. In this work, comparative experiments are conducted as for bearing and gearbox datasets under variable working conditions. The results indicate that DAFDMRT can show excellent performances in terms of fault diagnosis and generalization ability.

Semi-supervised diagnosis method of refrigeration compressor hidden defect based on convolutional transformer autoencoder model

Improving product quality and service life by diagnosing defects in compressors through vibration signal analysis of the compressor's shell is challenging due to strong noise interference and limited labeled samples. The paper proposed a semi-supervised diagnosis method based on the convolution Transformer autoencoder (CTAE) for diagnosing hidden defects with small samples and strong noise. Firstly, an optimized variational modal decomposition is utilized to decouple and reduce noise from the raw vibrations, and the decomposition results are concatenated with the vibration signals as input to the model; Secondly, the CTAE is employed to learn the feature distribution of unlabeled samples and to extract and fuse local and global features from the input data; Finally, a labeled samples are used to fine-tune the model and to fuse features from multi-sensor information. The results of using a compressor dataset for validation show that the proposed method has high diagnosis accuracy and robustness with limited labeled samples and different signal-to-noise ratios.

Vibration Signal Analysis of Complex Mechanical Systems and Early Wear Detection and Forecasting for Gears

Vibration Signal Analysis of Complex Mechanical Systems and Early Wear Detection and Forecasting for Gears

Discrete wavelet transforms analysis of vibration signals for correlating tool wear in diamond turning of additive manufactured Ti-6Al-4V alloy

Ultra-precision machining (UPM) of Ti-6Al-4V alloy is widely regarded as a challenging material processing due to excessive tool wear and chemical reactivity of the tool and workpiece. Tool wear has a significant influence on the surface quality and also causes damage to the substrate. Therefore, it is critical to consider the tool condition during diamond turning, especially as precision machining moves toward intelligent systems. Consequently, there is a need for effective ways for in-process tool wear monitoring in UPM. This study aims to monitor the diamond tool wear using time-frequency-based wavelet analysis on vibrational signals acquired during the machining of Additively Manufactured (AM) Ti6Al4V alloy. The analysis employed Daubechies wavelet (db4, level 8) to establish a correlation between the Standard Deviation (SD) of the magnitude in the decomposed vibrational signal obtained from both the fresh and used tools. The analysis revealed that at a feed rate of 1 mm/min, the change in SD is 32.3% whereas at a feed rate of 5 mm/min, the change in SD is 8.4%. Furthermore, the flank wear and microfractures are observed using a scanning electron microscope on the respective flank and rake face of the diamond tool.