Measured Vibration Signals Research Articles

Attention-based multiscale denoising residual convolutional neural networks for fault diagnosis of rotating machinery

CNN-based fault diagnosis approaches have achieved promising results in improving the safety and reliability of rotating machinery. Most of the existing CNN models are developed on the assumption that the collected data is high-quality. However, since rotating machinery usually operates under fluctuating conditions, the critical pulse information of the measured vibration signals is easily submerged in noise. To promote the adaptability of CNN in noisy industrial scenes, an attention-based multiscale denoising residual convolutional neural network (AM-DRCN) is put forward in this study. First of all, a multiscale denoising module (MDM) is introduced as the basic building unit to help the network explore multiscale features and filter out irrelevant information. Then, a feature enhancement module (FEM) is leveraged to expand the receptive field and make full use of the side-out features. Further, a joint attention module (JAM) is explored to integrate the extracted features effectively. Finally, a lightweight CNN model named AM-DRCN is developed based on the above improvements. The practicality and effectiveness of AM-DRCN for monitoring machine health and stability states are verified through three case studies.

A Novel Method of Impeller Blade Monitoring Using Shaft Vibration Signal Processing.

The monitoring of impeller blade vibrations is an important task in the diagnosis of turbomachinery, especially in terms of steam turbines. Early detection of potential faults is the key to avoid the risk of turbine unexpected outages and to minimize profit loss. One of the ways to achieve this is long-term monitoring. However, existing monitoring systems for impeller blade long-term monitoring are quite expensive and also require special sensors to be installed. It is even common that the impeller blades are not monitored at all. In recent years, the authors of this paper developed a new method of impeller blade monitoring that is based on relative shaft vibration signal measurement and analysis. In this case, sensors that are already standardly installed in the bearing pedestal are used. This is a significant change in the accessibility of blade monitoring for a steam turbine operator in terms of expenditures. This article describes the developed algorithm for the relative shaft vibration signal analysis that is designed to run in a long-term perspective as a part of a remote monitoring system to track the natural blade frequency and its amplitude automatically.

On-line mechanical life prediction method for a conventional circuit breaker based on multi-parameter particle swarm optimization-support vector regression using vibration detection

For on-line mechanical condition monitoring of conventional circuit breakers, based on the analysis of vibration signal measurements and the generation principle in the circuit breaker breaking motion process, the time parameters for mechanism action with clear physical meaning are correlated with vibration events. Thus, an on-line mechanical life prediction method based on vibration detection and multi-parameter support vector regression (MP-SVR) optimized by particle swarm optimization is proposed. This method aims to use the action time parameter as the main monitoring parameter to reflect the degradation of mechanical conditions and the life failure criterion. First, the complete MPs of life degradation are obtained through the designed on-line measurement algorithm for the action time parameter and the assisted time-domain features of vibration signals in the corresponding time. Then, a quantitative life prediction model is constructed based on MP-SVR. Finally, the degraded MPs are used as input to the prediction model to predict the remaining mechanical life. Our experiment shows that this on-line model for life evaluation is definitely effective and practical in engineering applications.

Research on Communication and Monitoring of Construction Machinery under the Framework of Wireless Sensor Network

Aiming at the problem that the current wireless sensor network performance is low and cannot meet the requirements of high-precision synchronous acquisition of mechanical vibration signals, this paper proposes a synchronous acquisition method (SACD) based on cross-layer design. Based on this monitoring mode, an experimental platform is constructed to conduct a vibration signal measurement experiment, and compared with the wired vibration signal test and analysis method. The results show that the vibration monitoring mode based on the wireless sensor network can meet the basic requirements of mechanical vibration monitoring applications.

Vibration trend measurement of hydropower generating unit based on KELM optimized with HSMAHHO algorithm and error correction

As the core equipment of hydropower plants, the healthy condition of hydropower generating unit (HGU) plays a vital role in the safe and stable operation of hydropower plants. Therefore, it is of great significance to measure the vibration trend of HGU, which can effectively reflect the health condition of HGU, allowing the development of appropriate countermeasures to improve the safety and stability operation of HGU. Given this, a hybrid approach for measuring vibration signals of HGU coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), phase space reconstruction (PSR), kernel extreme learning machine (KELM) optimized by hybrid slime mold algorithm and Harris hawks optimization (HSMAHHO), and error correction with gate recurrent unit (GRU) network is proposed in this paper. Specifically, CEEMDAN is initially applied to decompose the raw vibration signals into several intrinsic mode functions (IMFs). Subsequently, PSR is adopted to convert each IMF into the input–output matrix of KELM for prediction. Meanwhile, HSMAHHO algorithm is utilized to optimize the critical parameters within KELM. Afterward, the predicted values of each IMF are superposed to obtain the predicted values of the raw vibration signals, and the error sequence to be corrected is constructed. Eventually, the error sequence is predicted by combining CEEMDAN, PSR, GRU and then summed up with the previous predicted values to get the final measuring result. In addition, the feasibility of the proposed hybrid approach is further verified by the experimental comparative analysis with seven comparative models. The experimental results demonstrate that (1) the proposed HSMAHHO algorithm could better optimize the internal parameters of KELM, which effectively improves the measuring results (2) the proposed error correction strategy could effectively enhance the measuring accuracy.

Condition Monitoring and Vibration Signal measurement of of Rolling Contact Bearing

In this paper, an investigation of the condition observing of the roller contact bearing is introduced. Bearing example information comprises of four unique circumstances as having flawed internal race, deficient external race, having deserts on roller and a solid bearing. For the readiness of the example bearing, laser machine is utilized for presentation of the miniature size deserts on the surfaces. A startling disappointment of the moving contact bearing might cause huge monetary misfortunes. Therefore, shortcoming conclusion in bearing has been the subject of serious examination. Vibration signal examination has been broadly utilized in the shortcoming recognition of pivot apparatus. The vibration sign of a moving contact bearing conveys the mark of the shortcoming in the bearing, and early issue identification of the bearing is conceivable by examining the vibration signal utilizing different sign handling procedures. From that vibration estimation strategy is generally utilized in the business for condition checking of various hardware and it tends to have the option to identify 92% of deficiencies or disappointment in the machines by the adjustment of vibration signals. Consequently it is vital to comprehension of the vibration signals related with moving component orientation.

A new type low-frequency FBG acceleration sensor based on cross reeds

PurposeThe purpose of this paper is to solve the problem of weak low-frequency vibration measurement capability of FBG accelerometer, and propose a FBG accelerometer based on cross reed.Design/methodology/approachThis study proposed a new type FBG acceleration sensor based on cross reeds. When the sensor vibrates, the mass block in the new structure rotates around the center of the cross reeds, which could eliminate the impact of friction, reduce the natural frequency of the sensor and improve its sensitivity. This study theoretically analyzed the impact of several structural parameters on the sensitivity and natural frequency of the proposed sensor and used COMSOL to perform static stress analysis and modal simulation; in this study, a test system was built to test the performance of the proposed sensor.FindingsThe test results revealed that the proposed sensor had a natural frequency of 94 Hz; within a low-frequency range of 1–65 Hz, its sensitivity response was flat, the dynamic range was 81.89 dB, the sensitivity was 243.59 pm/g and the linearity was 99.97%. The cross reeds effectively strengthened the structural stability, the relative standard deviation of the repeatability of the sensor was 0.89% and the transverse crosstalk in the working frequency band was −26.97 dB.Originality/valueThis study innovatively proposes the structure of the two symmetrical cross reeds, which can improve sensitivity by eliminating the influence of friction, and the structure of cross reeds can effectively suppress the influence of lateral crosstalk. The proposed sensor can realize real-time accurate measurement of low-frequency weak vibration signals.

Run-Time Cutting Force Estimation Based on Learned Nonlinear Frequency Response Function

Abstract The frequency response function (FRF) provides an input–output model that describes the system dynamics. Learning the FRF of a mechanical system can facilitate system identification, adaptive control, and condition-based health monitoring. Traditionally, FRFs can be measured by off-line experimental testing, such as impulse response measurements via impact hammer testing. In this paper, we investigate learning FRFs from operational data with a nonlinear regression approach. A regression model with a learned nonlinear basis is proposed for FRF learning for run-time systems under dynamic steady state. Compared with a classic FRF, the data-driven model accounts for both transient and steady-state responses. With a nonlinear function basis, the FRF model naturally handles nonlinear frequency response analysis. The proposed method is tested and validated for dynamic cutting force estimation of machining spindles under various operating conditions. As shown in the results, instead of being a constant linear ratio, the learned FRF can represent different mapping relationships under different spindle speeds and force levels, which accounts for the nonlinear behavior of the systems. It is shown that the proposed method can predict dynamic cutting forces with high accuracy using measured vibration signals. We also demonstrate that the learned data-driven FRF can be easily applied with the few-shot learning scheme to machine tool spindles with different frequency responses when limited training samples are available.

A Vibration Signal Denoising Method of Marine Atomic Gravimeter Based on Improved Variational Mode Decomposition

As a high-precision gravity measuring device, a marine atomic gravimeter is highly sensitive to vibration signals. Accurate measurement and analysis of vibration signal is the primary condition to realize vibration compensation and vibration suppression. Denoising plays a crucial role in the processing of these vibration signals. The vibration signals of a marine gravimeter contain numerous nonlinear and nonstationary components. In this paper, a vibration signal denoising method of marine atomic gravimeter based on improved variational mode decomposition (VMD) was put forward to effectively suppress the noise. An improved genetic particle swarm optimization (GPSO) was first adopted for the parametric optimization of VMD by taking minimum permutation entropy (PE) as fitness function and adaptively determining the optimal parameters of VMD. PE was then utilized to calculate the proportion of noise-containing components in the intrinsic mode function (IMF) components obtained by VMD. The components were classified into noise and signal components by searching for the mutation points of two adjacent IMF permutation entropies. On this basis, noise components were denoised by Savitzky-Golay (SG) filter. In the end, the denoised components were reconstructed with the signal components to generate denoised vibration signals. To verify the effectiveness, the proposed method was applied in denoising, simulated and measured vibration signals of a marine atomic gravimeter, and compared with Daubechies (db) wavelet, Symlets (sym) wavelet, and empirical mode decomposition (EMD). The results showed that the proposed method could effectively remove the noise from nonlinear vibration signals and retain the authentic and useful information, so that it was able to provide the supporting data for gravity compensation of marine atomic gravimeter.

High-order synchroextracting transform for characterizing signals with strong AM-FM features and its application in mechanical fault diagnosis

In this paper, a novel time–frequency analysis (TFA) technique termed High-order Synchroextracting Transform (HSET), is proposed to better characterize the changing dynamics of multi-component signals with strong AM-FM components. Synchroextracting Transform (SET) is a TFA method has been developed recently. As a post-processing technology, unlike the parameterized TFA methods that requires different demodulation operators to be designed according to different types of signals, SET can generate high-quality time–frequency representation (TFR) only on the basis of short-time Fourier transform (STFT). However, since the core of SET is based on the assumption that the signal is composed of pure harmonics, SET cannot accurately extract the time-varying characteristics of signal with strong AM-FM components. To construct a method to well characterize the time-varying laws of strong AM-FM signals, this paper uses high-order Taylor expansion to estimate the time varying laws of the signal to obtain a more accurate frequency estimation operator, which we call high-order Synchroextracting operator (HSEO), and then HSET can be constructed through Dirichlet function to generate TFR with high TF resolution. Good reconstruction quality can be achieved through the inverse transformation of HSET while signal reconstruction operation can be completed concisely. In addition, this paper applies HSET to the processing of the measured vibration signals and feature extraction of gravitational-wave (GW) signal. Compared with other advanced TFA technologies, the effectiveness and competitiveness of the proposed method can be demonstrated.

A machine learning-based model for real-time leak pinpointing in buildings using accelerometers

Modern water networks from municipal network to building networks are plagued with the threat of leaks. Leaks create a significant amount of loss of resources. Pressurized water pipelines are more susceptible due to the high pressure at which water travels. Multiple researchers have tried to utilize a variety of static (devices that are left in the network) and dynamic (devices that are mobilized to the suspected location) leak detection techniques to ensure the early detection and pinpointing of leaks in water transportation networks. The main goal is to provide quick and efficient tools that can identify and pinpoint leaks in buildings while being cost-effective. This article proposes a small-scale experimental static real-time monitoring system that can identify leaks and their location with high accuracy by measuring vibration signals via wireless accelerometers. The experiment utilizes one-inch and two-inch Polyvinyl Chloride (PVC) and iron pipelines, which are commonly used in residential buildings. Since the proposed system is static, the wireless accelerometers are placed on the exterior walls of the pipelines. The vibration signals, derived from each accelerometer, were calculated and analyzed. A leak is identified when a spike in the signal is detected. Once a leak was identified, the model would move to determine the source of the signal, that is, the leak location. The developed models proved to be capable of accurately pinpointing leaks within an accuracy of 25 cm. The main techniques that were used in model development were regression analysis and backpropagation of artificial neural networks models.

Integrated early fault diagnosis method based on direct fast iterative filtering decomposition and effective weighted sparseness kurtosis to rolling bearings

The fault vibration signal of rolling bearing is typical non-stationary and non-linear signals. It usually exhibits the characteristics of amplitude modulation and frequency modulation (AM-FM) due to the interaction between the various components within the bearing and modulation of the rotor speed. In addition, the incipient weak fault feature information is inevitably masked by the strong background noise and other vibration interference because of the influence of the working environment of mechanical equipment. Therefore, new high efficiency and effective processing methods are indispensable for feature extraction of bearing fault diagnosis signals. Direct fast iterative filtering (dFIF) is a new kind of adaptive mode decomposition method proposed by Antonio Cicone based on iterative filtering (IF) and fast iterative filtering (FIF). dFIF is used to quickly decompose multi-component signals into a set of intrinsic mode functions (IMFs) by means of fast Fourier transform (FFT). In order to study the decomposition performance and examine the feature extraction ability of the dFIF method, a group of harmonic signals with simple linear superposition, multi-components AM-FM signals with different data length and different signal-to-noise ratio (SNR) are simulated and analyzed. For comparison, other mode decomposition methods were also applied to the simulated signals, such as adaptive local iterative filtering (ALIF), local mean decomposition (LMD), and empirical mode decomposition (EMD). The results show that dFIF decomposition methods have faster decomposition speed and better accuracy than other methods. Furthermore, the results also demonstrate excellent performance in AM-FM feature extraction and anti-mode mixing. On the other hand, the effective weighted sparseness kurtosis (EWSK) index integrates the periodicity and intensity of impact in each mode, which can effectively identify the effective mode. Therefore, considering the above merits, EWSK is integrated on the dFIF to effectively accomplish the fault diagnosis for rolling bearing under strong background noise. Firstly, the raw vibration signals of rolling bearing fault are decomposed into a set of IMFs by using dFIF. The EWSK indicator is then utilized to select IMFs with rich bearing fault impulse information for reconstruction. Finally, the Hilbert envelope demodulation analysis is used to analyze the reconstructed signal, extract the bearing fault feature and then judge the fault type. This proposed method is applied to analyze the simulated and field measured vibration signals of rolling bearing faults. Simultaneously, the other above-mentioned adaptive TFA methods are also adopted to analyze the signals. The results compared show that the proposed method has excellent performance in the aspects of decomposition speed and accuracy under the influence of strong background noise. It demonstrates the effectiveness and applicability of the proposed method for signal processing and fault pattern recognition of rolling bearings.

Damping identification of offshore wind turbines using operational modal analysis: a review

Abstract. To increase the contribution of offshore wind energy to the global energy mix in an economically sustainable manner, it is required to reduce the costs associated with the production and operation of offshore wind turbines (OWTs). One of the largest uncertainties and sources of conservatism in design and lifetime prediction for OWTs is the determination of the global damping level of the OWT. Estimation of OWT damping based on field measurement data has hence been subject to considerable research attention and is based on the use of (preferably operational) vibration data obtained from sensors mounted on the structure. As such, it is an output-only problem and can be addressed using state-of-the-art operational modal analysis (OMA) techniques, reviewed in this paper. The evolution of classical time- and frequency-domain OMA techniques has been reviewed; however, the literature shows that the OWT vibration data are often contaminated by rotor speed harmonics of significantly high energy located close to structural modes, which impede classical damping identification. Recent advances in OMA algorithms for known or unknown harmonic frequencies can be used to improve identification in such cases. Further, the transmissibility family of OMA algorithms is purported to be insensitive to harmonics. Based on this review, a classification of OMA algorithms is made according to a set of novel suitability criteria, such that the OMA technique appropriate to the specific OWT vibration measurement setup may be selected. Finally, based on this literature review, it has been identified that the most attractive future path for OWT damping estimation lies in the combination of uncertain non-stationary harmonic frequency measurements with statistical harmonic isolation to enhance classical OMA techniques, orthogonal removal of harmonics from measured vibration signals, and in the robustification of transmissibility-based techniques.

Fault Detection of Planetary Gears Based on Signal Space Constellations.

A new method to process the vibration signal acquired by an accelerometer placed in a planetary gearbox housing is proposed, which is useful to detect potential faults. The method is based on the phenomenological model and consists of the projection of the healthy vibration signals onto an orthonormal basis. Low pass components representation and Gram–Schmidt’s method are conveniently used to obtain such a basis. Thus, the measured signals can be represented by a set of scalars that provide information on the gear state. If these scalars are within a predefined range, then the gear can be diagnosed as correct; in the opposite case, it will require further evaluation. The method is validated using measured vibration signals obtained from a laboratory test bench.

Cutting Force Embedded Manifold Learning for Condition Monitoring of Vertical Machining Center

Cutting condition monitoring is essential for managing the operation of machine tools in manufacturing. The t-distributed stochastic neighbor embedding (t-SNE) method has been adopted to learn the underlying relationship between cutting parameters in a manifold. This paper focuses on cutting force embedding in the t-SNE, termed Ft-SNE, for applications in feature selection to achieve better performance for condition monitoring of machine tools. In the method, the measured vibration signals are used to build the fundamental feature space in the high-dimensional space with cutting force signals being introduced to improve its separability. Further analysis is performed to optimize the classification performance of the Ft-SNE by selecting the appropriate weighting coefficient by minimizing the Kullback-Leibler divergence. The experiments were implemented on a 3-axis vertical machining center. Vibration signals and cutting force signals under different cutting conditions were collected from the vertical machining center. The visualization results of the Ft-SNE and t-SNE were analyzed in low-dimensional space. By introducing the tangential cutting force, the classification performance is improved with classification evaluation indicators being around 98%, with an improvement of over 41% compared to the traditional t-SNE for cutting condition classification in machining process.

СПЕКТРАЛЬНИЙ АНАЛІЗ СИГНАЛУ ВІБРАЦІЇ З ВИКОРИСТАННЯМ ПЕРЕТВОРЕННЯ ФУР’Є

The paper analyzes the topical issue of timely detection and prevention of a dangerous state of critical machines and mechanisms using vibration diagnostics as a promising method. It is proposed to use the method of analysis of vibration spectra in order to identify malfunctions, by processing the output signal from the sensor, which is sent to a personal computer, where a spectral analysis of the measured vibration signal is performed using a diagnostic program based on MATLAB. It is proposed to use spectral analysis, which is a traditional method of vibration diagnostics. It is supposed to determine the faulty state according to vibration measurement data and then to determine the nature of the fault according to the vibration signals analysis using the Fourier transform. It is proposed to process the output signal from the vibration sensor to a personal computer, where a spectral analysis of the measured vibration signal is carried out using a diagnostic program based on MATLAB. The use of FFT algorithms is due to a decrease in the number of operations performed, and, consequently, an increase in performance. It is shown that the decomposition of a complex signal in a Fourier series gives the components of the spectrum, which are, in narrow-band analysis, the harmonic components of the complex signal. It has been determined that narrowband spectral analysis allows detecting a larger number of faults or defects and is used for monitoring and in-depth diagnostics of equipment. Knowing the frequencies at which malfunctions of the kinematic pairs of machinery are manifested, the amplitude of the spectrum components can be used to assess their state. It was determined that spectral analysis is widely used in the processing of vibration diagnostics data, being the main and classical method of analysis. To optimize the duration of the test, it is necessary to take into account the relationship between the spectrum bandwidth and the signal accumulation time. The pass-through characteristic reflects the dependence of the signal level on time at a certain frequency, illustrating the process of signal accumulation during narrow-band spectral analysis. The narrower the analysis strip, the longer the measurements take.

Effective Fault Diagnosis Based on Wavelet and Convolutional Attention Neural Network for Induction Motors

Induction motors are important equipment in modern industry. However, the occurrence of fatigue failure following an extended period of operation invariably results in a catastrophic failure. As a result, monitoring and diagnosing induction motors is critical to avoiding unplanned shutdowns caused by premature failures. This article aims to develop an effective method for motor fault detection using time–frequency contents of vibration signals and an attention-based convolutional neural network model. First, the vibration signals are collected and labeled into five different categories: normal condition, outer ring fault, inner ring fault, misalignment condition, and broken rotor bar. Then, using the Morlet function, continuous wavelet transform (CWT) converts the vibratory time-series signals to the scalogram feature images. The time–frequency feature images are created after downsampling and converting the measured vibration signals to the frequency domain. These images are then resized and fed into the proposed convolutional attention neural network (CANN) to identify various induction motor failures. The experimental results demonstrate that the suggested model can provide an excellent diagnosis accuracy of 99.43%, significantly better than the state-of-the-art deep learning approaches for fault diagnosis. Moreover, the developed model’s robustness is validated against adversarial attacks based on the fast gradient sign method (FGSM) by including white Gaussian noise.

A speed measuring system of hermetic centrifuge

Based on the detected vibration signal, a measurement system was designed to solve the current problem of closed centrifuge speed measurement. The system collected and adjusted the signal collected by the vibration sensor, and then send it to the control unit STM32 for spectrum analysis to get the speed frequency. The sampling data was analysed and processed in PC through serial communication. Programs were written to achieve data sampling, filtering and storage, and the important parameters such as acceleration waveforms, vibration frequency and actual speed were displayed on the screen. The experimental results show that the system can accurately calculate the real-time speed of the closed centrifuge according to the measured vibration signals, which has the advantages of high stability, detection accuracy and real-time performance.

Phase-free static balancing for high-speed overhanging rotors

In this paper static balancing procedures for a machine with an eccentrically arranged overhanging rotor are discussed. The aim is a heuristic method for static balancing of such an overhanging rotor based on conventional acceleration measurements without any reference phase information. In conventional balancing processes, measured vibration signals proportional to the unbalance are evaluated with the aid of a stationary reference signal. Furthermore, limitations concerning balance weights and positions have to be considered. In our case, we have six available holes in the rotor at a specified radius to add a combination of metal studs with predefined weights. The speed of the rotor ranges up to 10.000 rpm. The experimental results of the proposed method are compared to a well-recognized graphical balancing method for rigid rotors. Special effort was made to use the already installed vibration monitoring accelerometer for the balancing process.

A Study of Effect of Various Normal Force Loading Forms on Frictional Stick-Slip Vibration

In this work, a comparative study is performed to investigate the influence of time-varying normal forces on the friction properties and friction-induced stick-slip vibration (FIV) by experimental and theoretical methods. In the experiments, constant and harmonic-varying normal forces are applied, respectively. The measured vibration signals under two loading forms are compared in both time and frequency domains. In addition, mathematical tools such as phase space reconstruction and Fourierspectra are used to reveal the science behind the complicated dynamic behavior. It can be found that the friction system shows steady stick-slip vibration, and the main frequency does not vary with the magnitude of the constant normal force, but the size of limit cycle increases with the magnitude of the constant normal force. In contrast, the friction system under the harmonic normal force shows complicated behavior, for example, higher-frequency larger-amplitude vibration occurs and looks chaotic as the frequency of the normal force increases. The interesting findings offer a new way for controlling FIV in engineering applications.