Vibration Signal Analysis Research Articles

Ball-Bearing Fault Classification Using Comparative Analysis of Wavelet Coefficient based on Entropy Measurement

Ball bearing is used to provide free rotation around a fixed axis. Various kinds of faults exist in the bearing such as inner race fault, outer race fault and ball race fault. One very effective method to diagnose the bearing fault is vibration signal analysis. Empirical mode decomposition (EMD) has been used for ball-bearing fault diagnosis in mechanical systems using vibration signal analysis. Classification of the ball-bearing fault has always been a challenging task. Various classification schemes such as Extreme learning machine (ELM), K-means Clustering, and Support vector machine (SVM), have been reported in the literature for ball-bearing fault classification. SVM is restricted by multiclass classification efficiency, and ELM is restricted by the longer training. In this paper, the entropy analysis of the wavelet coefficient obtained from the third level decomposition of the residue signal (obtained after subtracting the highest frequency component from the raw signal) has been done for ball-bearing fault classification. A comparative analysis of the wavelet coefficient based on entropy measurement has been presented here. High fault classification accuracy has been achieved using the proposed method for the detection of ball-bearing fault. Shannon entropy, Average Shannon entropy, and Renyi’s entropy are parameters for the justification of the proposed approach. The result shows the best wavelet to be chosen among the available discrete wavelet based on various entropy measurements.

Automatic detection of bearing faults

The potential for machine Condition Monitoring (CM) to enhance system performance and forestall harmful failures has been rising considerably. It is applicable to all or any rotating machinery where preventive fault diagnosis could be a must like Engines, drive trains, gearboxes, pumps, turbines, compressors, fans etc. Detection of bearing faults is one in all the foremost challenging tasks in bearing health condition monitoring, especially when the fault is at its initial stage. The defects in bearing unless detected in time may result in malfunctioning of the machinery. Acquisition of vibration signals coming from various machine components, performing its analysis for prediction of faults and understanding the root cause for high vibrations has been an established practice. However, while performing analysis of vibration signals, it is close to impossible to separate out and focus on ‘bearing frequencies’ especially in the presence of strong masking signals from other machine components. In this paper a study and implementation of ‘advanced’ vibration analysis technique viz envelope analysis for localization of bearing frequencies from the masking signals generated by other machine components is discussed. A smart graphical user interface-based software tool has been developed for automatic detection of bearing faults. The automatic detection techniques presented in this paper can be very helpful for condition monitoring especially for early diagnosis of bearing faults. This may help industries for minimizing the downtime due to machine breakdown. With the help of vibration sensor (accelerometer), FFT analyzer, set of faulty bearings installed with rotating arrangement and a graphical user interface-based software tool designed with the help of multi-paradigm programming language, the setup for automatic detection of bearing faults has been validated.

Adaptive multivariate chirp mode decomposition

Multivariate signal processing methods are becoming more prevalent as sensors and modern science and technology improve. However, most existing multivariate signal decomposition methods suffer from the following challenges: (i) requiring prior knowledge of multivariate signal modes; (ii) limiting to narrowband signal analysis; (iii) presenting mode mixing. To overcome the challenges mentioned above, this study proposes a novel method, named adaptive multivariate chirp mode decomposition (AMCMD). The method captures time-varying joint modes one-by-one in a recursive framework without knowing the precise number of modes. Specifically, a multivariate chirp mode (MCM) is modeled first based on AM–FM signals, with the constraint that there is a joint frequency component between all signal channels. Furthermore, demodulation techniques are used to entail the wideband multivariate signal mode exhibit narrowband characteristics. Finally, the objective function is established and the modes of each channel signal are estimated one by one. The efficacy and superiority of the method are verified by a series of numerical examples. In addition, the analysis of real-world time-varying vibration signals also confirms the practicality of the method. The findings demonstrate that the proposed method can converge faster to the same satisfactory results as existing state-of-the-art methods at a faster rate, even without knowing the precise number of modes.

Bearing fault diagnosis via a parameter-optimized feature mode decomposition

Because actual vibration signal collected from mechanical equipment (e.g., wind turbines and high-speed trains) are strongly non-stationary and have low signal-to-noise ratios, which indicates that it is arduous to excavate beneficial fault characteristics from collected vibration data via traditional detection methods, such as Fourier transform and envelope demodulation. Feature mode decomposition (FMD) is a recently proposed new signal analysis approach that has been smoothly applied to mechanical fault diagnosis. Nevertheless, input parameters (i.e., the mode number K and filtering length L) must be defined artificially when FMD is introduced for vibration signal analysis. That is, the decomposition performance of FMD is easily affected by its parameter setting. To handle this, this study proposes a parameter-optimized feature mode decomposition (POFMD) for bearing fault diagnosis. Firstly, a new index termed signal cycle kurtosis-to-noise ratio (SCKNR) is designed as an objective function of FMD in adaptive parameter searching, which can take the impact intensity and noise immunity of the signal into consideration simultaneously. Subsequently, particle swarm optimization (PSO) based on SCKNR is adopted to automatically select the optimal combination parameters (i.e., the mode number K and filtering length L) of FMD. Meanwhile, POFMD is used to separate the collected data into a series of mode components. Next, main mode component is selected based on maximum criterion of feature energy rate of squared envelope spectrum (SES-FER). Ultimately, squared envelope spectrum of main mode component is computed to achieve fault identification of wind turbines and high-speed train bearings. A study of simulated signals and two cases validate the availability of our presented approach. Furthermore, our presented POFMD is more efficient in extracting fault characteristics than the well-versed variational mode decomposition (VMD), symplectic geometry mode decomposition (SGMD) and spectral kurtosis (SK).

Use of Holo-Hilbert spectral analysis to reveal the amplitude modulation features of faulty rolling bearing signals

Time-frequency analysis (TFA) is a powerful tool for vibration signal analysis. However, it cannot comprehensively reflect the cross-scale coupling relationship between amplitude-modulated (AM) and frequency-modulated (FM) characteristics of the vibration signal collected from faulty rolling bearings. To address this shortcoming, the Holo-Hilbert spectral analysis (HHSA) method with combining the masking empirical mode decomposition (Masking-EMD) for signal separation is introduced to completely describe the internal modulation characteristics of faulty rolling bearing. In HHSA, first, the initial vibration signal of faulty rolling bearing is decomposed into numbers of intrinsic mode functions (IMFs) to extract its time-varying FM and AM characteristics. Next, the instantaneous frequency and the instantaneous amplitude of each IMF are estimated, respectively. Then, each first-layer IMF is decomposed into a series of IMFs with AM characteristics via reusing Masking-EMD. Finally, the FM and AM components are combined with their instantaneous modulation energy to realize the three-dimensional Holo-Hilbert spectrum (HHS). The simulation and experiment results illustrate that the introduced method can completely reveal the AM characteristics of non-linear vibration signals and the corresponding carrier frequency range.

Early Fault Warning and Identification in Condition Monitoring of Bearing via Wavelet Packet Decomposition Coupled With Graph

It has been well known that the fault of rolling element bearings (REBs) is one of the biggest causes for machine breakdown; thus, the early detection and type identification of a fault during REBs successive operations is necessary to help users take predictive maintenance action and/or schedule in order to avoid major machine failures. Wavelet packet decomposition (WPD) is a widely used technique to analyze vibration signal for health monitoring of REBs, but the resulting wavelet packet coefficients (WPCs) are still in high dimensionality, making them impossible for the direct use in practical usage. Keeping along the research line of high-level analysis for WPD enhancement, this article presents a new dynamic modeling approach, called graph-modeled wavelet packet coefficients (GMWPCs), that integrates WPD and graph theory in order to extract the correlation information between WPCs. By means of an adaptive input weight fusion, the GMWPCs can automatically confirm the weight of the frequency sub-band where the fault-induced information is more evident. By virtue of GMWPCs, a new two-phase framework for early warning detection and fault identification is proposed finally, which is able to not only detect the time location of the fault in its very early stage but also identify the fault type. We conducted different experiments to validate the early warning detection and fault identification separately. Experimental results, outperforming the state of the arts, verify the adequacy and effectiveness of the proposed framework and meanwhile reveal its appropriate use for real engineering applications.

Analysis of vibration signals and detection for multiple tooth cracks in spur gearboxes

Tooth crack diagnosis is important to ensure the reliability of gearboxes. However, most reported studies on tooth crack diagnosis only involved the scenario of one tooth crack in a gearbox, which is not always the case since gearboxes may also suffer from multiple tooth cracks in the industry. To overcome this problem, this study first brings some insights into the vibration characteristics of a spur gearbox with multiple tooth cracks using dynamic modelling. Three scenarios of multiple tooth cracks are considered, including two nonadjacent tooth cracks on the pinion and a healthy gear, two adjacent tooth cracks on the pinion and a healthy gear, and one tooth crack on the pinion and one tooth crack on the gear. Tooth mesh stiffness is analytically evaluated using the potential energy method and is further inserted into the spur gearbox dynamic model to generate vibration responses. Analyses of mesh stiffness and vibration responses are conducted in both time and frequency domains. Besides, a novel method focusing on the crack induced impulses (CII) is proposed to detect the number and locations of multiple tooth cracks. Firstly, the CII are extracted from gearbox vibration signals using a new strategy based on the singular value decomposition. Time synchronous average (TSA) is conducted on the CII to get the TSA signals for both the pinion and the gear. Afterwards, detection of the number and locations of multiple tooth cracks is achieved via analyzing the squared envelopes of the TSA signals for both the pinion and the gear. The obtained insights into vibration characteristics for multiple tooth cracks and the effectiveness of the proposed method for detecting the number and locations of multiple tooth cracks are demonstrated using both simulated gearbox vibration signals and experimental gearbox vibration datasets.

Application of multi-base fusion generalized chirplet basis transform in vibration signal analysis of multiple rotor rotating machinery

In rotating machineries, there are many signals with multiple nonproportional fundamental frequencies such as the vibration response signal from a dual rotor aeroengine, where the speeds of the high-pressure turbine and the low-pressure turbine are not synchronized. The conventional Chirplet transform (CT) based time–frequency analysis (TFA) methods are capable of processing nonstationary signals mixed with multiple synchronous components, or proportional frequency components, but not suitable for processing the nonstationary signals with multiple nonproportional fundamental frequencies. This paper presents a novel nonstationary signal TFA method, referred to as General Chirplet Basis Transform (GCBT), which is designed for processing nonstationary signals with multiple nonproportional fundamental frequencies. In the proposed GCBT, the local maximum value search method is utilized to find out all the Chirplet basis function parameters matched with the fundamental frequencies of each harmonic group contained in the signal. Then, the time–frequency representation (TFR) fusion criterion is established to effectively combine the TFA results of all the discovered Chirplet basis functions. The effectiveness and superiority of the GCBT are demonstrated by analyzing a simulation signal, the vibration signals collected in the laboratory using two independently operated small rotors, and the vibration signals from an aeroengine test. All the results indicate that the proposed GCBT has the ability to handle multi-component signals with nonproportional fundamental frequencies, synchronous frequencies, closely-spaced frequencies, as well as the signals with sever noise backgrounds.

Bearings Health Monitoring Based on Frequency-Domain Vibration Signals Analysis

Rotating machine health monitoring is critical for system safety, cost savings, and increased reliability. The need for a simple and accurate fault diagnosis method has led to the development of various monitoring techniques. They incorporate vibration, motor’s current signature, and acoustic emission signals analysis in condition monitoring. So, based on using vibration signal analysis, a test rig was built for bearing fault identification. The test rig replicates and investigates various bearing problems, such as those found in the inner and outer races. An accelerometer, type ADXL335, was interfaced to a data acquisition device (DAQ USB-6215) for collecting vibration signals under various operating circumstances. In addition, a load cell was embedded with the test rig, interfaced with a digital panel meter, and used for recording the applied load on the bearings. The time-domain signal analysis technique was used after acquiring vibration signals at various bearing health states. Then, the time-domain signal was converted to the frequency domain using the fast Fourier transform, and the result was analyzed to investigate the generated fault frequencies. Finally, the obtained frequencies were compared with the theoretical values extracted from the theoretical equations, and the method proved its effectiveness in detecting the fault generated.

A global interactive attention-based lightweight denoising network for locating internal defects of CFRP laminates

Carbon fiber reinforced plastic (CFRP) has become one of the main structural materials for aerospace vehicles. However, some internal defects are prone to occur and have potential to cause significant losses of life and property. Currently, the detection of internal defects for CFRP mainly relies on ultrasonic, and other technologies, while they have disadvantages of low efficiency, and poor adaptability. Therefore, this paper explores a novel method to locate internal defects of CFRP laminates by analyzing vibration signals. Firstly, a signal acquisition scheme is designed. Then, a global interactive attention-based lightweight denoising network (GIALDN) is designed to analyze vibration signals and locate internal defects of CFRP laminates. In GIALDN, the threshold denoising method is used to eliminate noise-related features and improve feature discrimination; a global interactive attention module is designed, which makes the network pay more attention to the valid features while realizing the global interactive connection and obtains the rich contextual features; combining with the convolution layer of de-pooling strategy and multi-layer convolution using the residual connection, the backbone of the network is formed. Finally, an experimental platform is established to test the performance of GIALDN. Results show that the location accuracy of GIALDN can reach 98.68%, which is more than 15% higher than those of VGGnet11 and FaultNet, and is also superior to those of LSTM, RNN, Rsenet18, SEresnet18 and Densenet121. Lastly, the location accuracies of GIALDN on CFRP laminates with the same thickness and different stacking sequences are investigated and a good model applicability can be observed.

Cavitation detection in a Kaplan turbine based on multifractal detrended fluctuation analysis of vibration signals

Cavitation generally causes a decrease in efficiency and vibrations to a Kaplan turbine, but effectively identifying cavitation state is quite difficult. In this paper, a comprehensive test system consisting of a high-speed camera and a vibration testing system has been constructed based on a high-precision universal hydraulic machinery test stand. The vibration signals of the runner and the draft tube and cavitation images on the runner blade have been obtained for a Kaplan turbine under different cavitation states based on the system. In addition, the multifractal detrended fluctuation analysis (MF-DFA) method has been introduced to analyse the obtained vibration signals, in order to investigate the influence of runner blade cavitation on turbine vibration. Results show that the vibration signals of both the runner and the draft tube have multifractal characteristics. The Hurst exponent, the scaling exponent and the multifractal spectrum by MF-DFA are strongly dependent on turbine cavitation state. It is found that cavitation states including the incipient cavitation of the turbine could be effectively identified by using characteristic parameters extracted from the multifractal spectrum obtained by the MF-DFA of vibration signals. This work provides an effective method for cavitation identification of Kaplan turbines.

Fault Diagnosis Method of Escalator Step System Based on Vibration Signal Analysis

Fault Diagnosis Method of Escalator Step System Based on Vibration Signal Analysis

Vibration Signal Analysis Based on Spherical Error Compensation.

A vibrating screen is important equipment in industrial production. According to the principle of bionics, a vibrating screen can be divided into a linear vibrating screen, elliptical vibrating screen, ball vibrating screen, and banana vibrating screen. There are also great problems with the use of a vibrating screen. The vibrating screen works due to the vibration excitation force generated by vibration. This work studies the motion trajectory of a vibrating screen by taking the vibrating screen with line motion trajectory as the research object. In this study, the vibration information is detected by an intelligent sensor, and the signal is filtered by an intelligent algorithm. Then, the spherical error compensation is used to improve the calculation accuracy, and the least square method is used to evaluate the error. Finally, the accurate vibration trajectory of the vibrating screen is obtained. The acquisition of a vibration track can provide the working efficiency and safety performance of the vibrating screen, and has social and economic benefits.

Vibration signal collection and analysis of mechanical equipment failure based on computer simulation detection

Abstract This article addresses the challenge of large error rate and low accuracy of the vibration signal collection of mechanical equipment failure, and proposes a mechanical equipment failure vibration signal collection and analysis based on computer simulation detection. Then, it uses the Kalman filter algorithm for data filtering, according to the mathematical model established by the system, thus choosing a suitable noise covariance calculation method. In the integration process after filtering, using a piecewise integration method between acceleration peaks, the integration calculation is optimized to obtain the vibration displacement. The simulation results of this article show the vibration data collected by the main controller, after Kalman filtering and piecewise trapezoidal integration method optimization. The error of the proposed method is 0.5% when the frequency is 80 Hz, relative to the displacement measurement method of the three-axis acceleration sensor at 8.3%, and the error of data calculation results is greatly reduced. The greater the amplitude of vibration, the smaller the error. This method significantly improves the accuracy of vibration signal collection of mechanical equipment.

A preliminary study on wellbore flow interpretation of fiber optic vibration signals based on K-means clustering algorithm

The wellbore flow analysis of optical fiber vibration signal depends on distributed optical fiber logging. Distributed optical fiber logging technology identifies the fluid in the well through distributed optical fiber acoustic sensor (DAS) and distributed optical fiber temperature sensor (DTS). Distributed optical fiber sensor has the advantages of small underground interference, high efficiency and low cost. In this paper, the wellhead data extracted by the distributed optical fiber acoustic sensor is used to calculate the upper bound of the fluid sound frequency band in the pipe by nonlinear least squares fitting. The K-means clustering algorithm is used to cluster the optical fiber vibration signals in the low frequency band. According to the clustering results, the ratio of the optical fiber signal eigenvalues of each production layers is obtained, and the trend of the ratio of the optical fiber signal eigenvalues of each production layers is judged to be close to the trend of the water absorption intensity. Compared with traditional acoustic logging, the wellbore flow analysis using distributed optical fiber acoustic sensor can quickly determine the production contribution of each layer and the change of fluid phase state in the production cycle. Combined with traditional production logging technology, distributed optical fiber logging shows its reliability and accuracy in data collection, logging interpretation and production application. Starting from the principle of distributed optical fiber acoustic sensing technology, this paper briefly expounds the properties of distributed optical fiber acoustic sensor and the principle of injection profile logging, systematically introduces the processing of distributed optical fiber acoustic data, and emphatically introduces the accuracy of K-means clustering algorithm for analyzing distributed optical fiber acoustic signal and qualitative judgment of production layer, which provides a new idea for judging the accuracy of production layers.

A novel mathematical model of transmission path effect for the research of vibration characteristics of planetary gearboxes

Model-based vibration signal analysis to interpret and understand vibration characteristics can provide prior guidance for the condition monitoring and fault diagnosis of planetary gearboxes (PGs). Transmission path effects due to the rotational motion of planet gears with respect to the accelerometer mounted on the PG housing make understanding its real dynamic behavior challenging. To overcome this challenge, a novel mathematical model of the transmission path effect is proposed to support the construction of more realistic dynamic response of PGs. This model is derived only from the geometric parameters and material properties, and it has higher applicability than traditional models. Subsequently, a phenomenological vibration model is used to simulate all vibration sources, including sun–planet and ring–planet gear meshes. On these bases, a comprehensive vibration signal model is proposed by considering all vibration sources and the corresponding transmission path effects. Moreover, the theoretical spectral structure is deduced via Fourier series analysis. Finally, the vibration characteristics of the vibration signals obtained using the proposed mathematical model are analyzed and validated through simulations and experiments in the time and frequency domains.

Noise Reduction Based on CEEMDAN-ICA and Cross-Spectral Analysis for Leak Location in Water-Supply Pipelines

Due to the leakage vibration signal of the water-supply pipeline is easily interfered with by background noise, which leads to a significant leakage location error of time delay estimation. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and independent component analysis (ICA) combined noise reduction method based on cross-spectral analysis is proposed and applied to leak location of water supply pipelines. Firstly, determine the frequency band range of the source leakage signal by cross-spectral analysis of the leakage vibration signal. At the same time, the leakage vibration signal is decomposed into a finite number of intrinsic mode functions (IMFs) using CEEMDAN. Then, the IMF components are filtered according to the effective amplitude ratio criterion, and the filtered components are reconstructed into a virtual noise channel. Finally, the leakage vibration signal and virtual noise channel are unmixed using FastICA to obtain the best estimate of the source leakage signal, which is used to estimate time delay using cross-correlation for determining the leak location. Simulation and experimental results show that the proposed method effectively improves the signal-to-noise ratio (SNR) and time delay estimation accuracy, and reduces leak location error. Compared with cross-correlation, CEEMDAN-ICA based on correlation coefficients, and CEEMDAN based on cross-spectral analysis, the average relative positioning error of the proposed method is respectively reduced by 11.07%, 9.7%, and 1.8%. In addition, the superiority of the proposed method compared with other state-of-the-art noise reduction methods is verified by comparative experiments.

Detection of Uniform Gearbox Wear in Induction Motors Based on the Analysis of Stray Flux Signals Through Statistical Time-Domain Features and Dimensionality Reduction Techniques

Gearboxes are core elements in power transmission systems. Although gearboxes are reliable and high-efficiency components, the occurrence of different faults is frequent since they are often subjected to adverse operating conditions. Classical gearbox condition monitoring approaches are based on the analysis of vibration and current motor signals and rely on the identification of specific fault-related frequency patterns. In this regard, this article proposes a novel diagnosis methodology based on the analysis of stray flux signals for detecting uniform wear in the gear teeth. The proposed methodology is based on the processing of the stray flux signals through feature calculation and extraction stages that lead to a high-performance signal characterization by estimating a set of statistical time domain-based features and then reducing the dimensionally by means of the principal component analysis and linear discriminant analysis techniques. Additionally, an automatic fault diagnosis is achieved through a neural network-based classifier for the detection and identification of uniform wear in a gearbox. The obtained results prove the potential of the proposal for its incorporation in condition maintenance programs in the industry, becoming an excellent alternative to classical approaches.

Vibration signal analysis of a rotor-bearing system through wavelet transform and empirical mode decomposition

Vibration analysis is widely used for the monitoring of the health of rotating machinery. There are different methods to interpret the vibration signals like time-domain analysis and frequency domain analysis, where the conventional Fast Fourier Transform (FFT) method is applied. FFT has been used successfully to extract stationary parameters from the frequency domain data. Complex machines normally consist of many parts and their vibration response contains many non-stationary signals and nonlinear signals. The objective of this research is to explore the feasibility of utilizing the wavelet transform (WT) and empirical mode decomposition (EMD) to efficiently decompose the sophisticated vibration signals of a rotor-bearing system into a finite number of intrinsic mode functions so that the fault characteristics of the rotor-bearing system can be analysed. A test rig of a rotor-bearing system was used to perform the experiments, and the vibration signals were recorded through NI-DAQ system. Vibration signals received from the test rig were analyzed using MATLAB software to present the useful information. The analysis result showed that the proposed approach is capable of diagnosing the faults of the rotor-bearing system.

Selective weighted multi-scale morphological filter for fault feature extraction of rolling bearings

Morphological filtering shows effectiveness in vibration signal analysis because of its simplicity and efficiency. Considering that different structural elements have different effects on filtering results, a new multi-scale morphological filtering (MMF) method called selective weighted multi-scale morphological filter (SWMMF) is developed for integrating results of different scales based on adaptive weighting strategy. Firstly, four morphological operators (dilation–closing, closing–dilation, erosion–opening and opening–erosion) are integrated into a new combination difference morphological filter to strengthen effect of faulty component extraction. Secondly, this new morphological filter is further extended to multiple scales in order to overcome limitation of single scale filter. Finally, the filtered results of different scales are adaptively combined by using the whale optimization algorithm (WOA)-based selective weighting method. The effectiveness of multi-scale filter and selective weights is proved by comparing with single-scale and average weighting filter on simulation and real-world cases (bearing vibration signals with different defects). The testing results on vibration signals indicate that SWMMF is able to extract effectively defect frequency and the corresponding multiplication frequencies from bearing vibration signals with heavy noise. The testing results illustrate that SWMMF outperforms other representative MMFs (e.g., weighted multi-scale morphological gradient operator (WMMG), weighted multi-scale difference operator (WMDIF), weighted multi-scale average operator (WMAVG)) on impulsive feature extraction of bearing vibrations signals with various defects. Moreover, it is demonstrated that SWMMF has good applicability in bearing fault diagnosis due to setup of adaptive weights and selection of structure element.