Envelope Spectrum Analysis Research Articles

Weak Fault Feature Extraction for Rolling Element Bearing Based on a Two-Stage Method

Timely and effective feature extraction is the key for fault diagnosis of rolling element bearing (REB). However, fault feature extraction will become very difficult in the early weak fault stage of REB due to the interference of strong background noise. To solve the above difficulty, a two-stage feature extraction method for early weak fault of REB is proposed, which mainly combines feature mode decomposition (FMD) with a blind deconvolution (BD) method. Firstly, based on the impulsiveness and cyclostationary characteristics of the vibration signal of faulty REB, FMD is used to decompose the complex original vibration signal into several modes containing single component. Subsequently, the sparse index (SI) is calculated for each mode, and the mode containing sensitive fault feature is selected for further analysis. Subsequently, apply the deconvolution method on the selected mode for further enhancing the impulsive characteristic. At last, traditional envelope spectrum (ES) analysis is applied on the filtered signal, and satisfactory fault features are extracted. Effectiveness and advantages of the proposed method are verified through experimental and engineering signals of REBs.

Incipient Fault Feature Enhancement of Rolling Bearings Based on CEEMDAN and MCKD

A rolling bearing vibration signal fault feature enhancement method based on adaptive complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and maximum correlated kurtosis deconvolution (MCKD) is proposed to address the issue that rolling bearings are prone to noise in the early stage and difficult to extract feature information accurately. The method uses the CEEMDAN algorithm to reduce the noise of the rolling bearing vibration signal in the first step; then, the MCKD algorithm is used to deconvolve the signal to enhance the weak shock components in the signal and improve the SNR. Finally, the envelope spectrum analysis is performed to extract the feature frequencies. Simulation and experimental results show that the CEEMDAN-MCKD method can highlight the fault characteristic frequency and multiplier frequency better than other methods and realize the characteristic enhancement of incipient fault vibration signals of rolling bearings under constant and variable operating conditions.

Wheel Out-of-Roundness Detection Using an Envelope Spectrum Analysis.

This paper aims to detect railway vehicle wheel flats and polygonized wheels using an envelope spectrum analysis. First, a brief explanation of railway vehicle wheel problems is presented, focusing particularly on wheel flats and polygonal wheels. Then, three types of wheel flat profiles and three periodic out-of-roundness (OOR) harmonic order ranges for the polygonal wheels are evaluated in the simulations, along with analyses implemented using only healthy wheels for comparison. Moreover, the simulation implements track irregularity profiles modelled based on the US Federal Railroad Administration (FRA). From the numerical calculations, the dynamic responses of several strain gauges (SGs) and accelerometer sensors located on the rail between sleepers are evaluated. Regarding defective wheels, only the right wheel of the first wheelset is considered as a defective wheel, but the detection methodology works for various damaged wheels located in any position. The results from the application of the methodology show that the envelope spectrum analysis successfully distinguishes a healthy wheel from a defective one.

Combined Square Envelope Spectrum by Integrating Multiband Bearing Fault Information

Extraction of transient repetitive impact signature caused by localized defects on rolling bearings is challenging for the condition monitoring of the mechanical system. The envelope spectrum analysis demodulating the signal over the full frequency band is a typical method for identifying the rolling bearing fault but is susceptible to broadband noise. Kurtogram and its variants performing envelope spectrum analysis after bandpass filtering around carrier frequency have extensive applications in processing signals produced by periodic mechanisms. However, these methods are designed to identify a single informative frequency band (IFB) without the ability to integrate multiband diagnostic information for bearing fault detection. Therefore, this article proposes a framework for generating a weighted combined square envelope spectrum (WCSES) for rolling bearing fault diagnosis. A 1/3-binary tree filter bank constructed by using the empirical wavelet transform is utilized to decompose the raw signal into different narrowband without losing the length of the filtered signals. The WCSES that integrates multiband diagnostic information is obtained by the weighted average of all the SESs obtained from different decomposition levels. Note that various indicators can guide the construction of WCSES under the proposed framework. In addition, a targeted indicator named generalized frequency-domain signal-to-noise ratio (GR) is employed to quantify the diagnostic information hidden in each narrowband signal. The analysis of industrial bearing fault signals validated the effectiveness and superiority of the proposed WCSES in fault feature extraction.

Resonance Detection Method and Realization of Bearing Fault Signal Based on Kalman Filter and Spectrum Analysis

The rolling bearing is an important part of mechanical equipment, and its performance significantly affects the quality and life of the mechanical equipment. This article uses the integrated fiber Bragg grating resonant structure sensor excited by periodic micro-shocks caused by micro faults to realize the extraction of information relating to potential faults. Because the fault signal is weak and can easily be interfered with by ambient noise, in order to extract the effective signal, this article determines the autoregressive model of bearing vibration by the final prediction error criterion and the recursive least squares estimation algorithm. The augmented state space model is established based on the autoregressive model. A Kalman filter is used to reduce the noise interference, and then the reduction noisy signal is analyzed by power spectrum and improved autocorrelation envelope spectrum to realize the detection of bearing faults. Through data analysis and method comparison, the proposed improved autocorrelation envelope spectrum analysis can directly extract the bearing fault frequency, which is superior to other methods such as cepstral analysis.

A Fast and Adaptive Empirical Mode Decomposition Method and Its Application in Rolling Bearing Fault Diagnosis

Although the ensemble empirical mode decomposition (EEMD) method and the complementary EEMD (CEEMD) method can greatly improve the mode mixing of the original empirical mode decomposition (EMD) method, the difference in white noise amplitude and the number of ensemble trials have a great influence on the decomposition results, and the low computational efficiency cannot meet the requirements of the online monitoring system. To solve these problems, a fast and adaptive EMD (FAEMD) method was proposed in this article, which combines the advantages of the order statistics filter (OSF) with the original EMD. First, the upper envelope and lower envelope of the original signal were drawn by using the OSF. Then, the original signal was decomposed into a series of intrinsic mode functions (IMFs) by the mean envelopes. Finally, the fault feature information was obtained by using the envelope spectrum analysis. The simulation signal and two groups of test fault signals were taken as examples to verify the effectiveness of the proposed method. Compared with EMD, CEEMD, and CEEMDAN methods, FAEMD can effectively extract the key feature information of fault signals and has strong practicability because of the low calculation cost.

Multistate fault diagnosis strategy for bearings based on an improved convolutional sparse coding with priori periodic filter group

Bearings are a critical component of rotating machines; when they fail, critical equipment becomes unavailable, damage may occur beyond the bearing itself, and safety concerns arise. Determining that a bearing structure is compromised before catastrophic failure permits the protection of plant, people, and productivity. When bearings malfunction, the features of single and multiple faults are masked and accompanied by noise and other signal degrading artifacts affecting the signals from the vibrational sensors. In these circumstances, detection and diagnosis of multistate bearing faults is difficult. To overcome these challenges, an improved convolutional sparse coding (ICSC) model, based on a priori periodic filter groups (PPFG), is proposed to respond to the multistate fault problems of bearings. A Laplace wavelet is constructed with one-sided decay related to the vibration pattern of the signal. The best-matched wavelet is optimally determined by correlation analysis of the signal frequency domain parameters and the time domain damping parameters. The best-matched wavelet and the kurtosis criterion are used to construct a PPFG based on the theoretical period of the fault. The ICSC based on the PPFG obtains mapping coefficients characterizing different vibrational features of the signal. The envelope spectrum analysis of the various mapping coefficients identifies and confirms the fault-revealing components in the multistate signal. The ICSC results have a relatively good sparse time domain, and the fault-identifying features in the envelope spectrum are enhanced. Multiple faults can be easily identified. The effectiveness and robustness of the PPFG/ICSC are demonstrated through a complete experimental analysis of simulated, single-fault, and multifault signals, as well as a comparative analysis of the previous methods – Fast SK, CBPDN, and VMD-ICA – which verifies that the PPFG/ICSC is more robust, accurate, and efficient than the previous methods.

Analysis and Diagnosis of Rolling Bearing Faults from the Perspective of Frequency Domain

Rolling bearing components are broadly utilized in major mechanical fabricating businesses and guaranteeing the secure and steady operation of rolling heading could be a basic necessity of the fabricating prepare. Engineering for intelligent manufacturing has grown significantly in importance during the past several years in the manufacturing sector. The method for identifying mechanical faults based on “frequency domain analysis plus intelligent model” has developed rapidly. In this study, methods such as envelope spectrum analysis and spectral kurtosis are applied to process and analyze fault data to improve the service life of rolling bearing production equipment. In addition, we perform grid search tuning of the hyperparameters in spectral kurtosis, enabling faster frequency band selection for envelope spectral bandpass filtering.

VMD method based on frequency domain correlated kurtosis and its application in multi-fault diagnosis of wheelset bearing

Parameter-adaptive VMD method is often used in rolling bearing fault diagnosis. The two key parameters K and α of VMD can be determined adaptively by using an intelligent optimization algorithm. The working environment of wheelset bearing is complex, and the existing parameter-adaptive VMD method is difficult to accurately extract the fault information of wheelset bearing. To solve this problem, a parameter-adaptive VMD method guided by the envelope power spectrum correlated kurtosis is proposed. Correlated kurtosis has strong directivity and is not affected by impact noise and cyclic stationary noise. Meanwhile, at low SNR, the frequency domain correlated kurtosis is more stable than the time-domain correlated kurtosis. Therefore, taking the local maximum envelope power spectrum correlated kurtosis as the objective function, and using the global searching ability of PSO, the optimal parameter combination of VMD can be adaptively determined. VMD is used to analyze the signal, calculate the envelope power spectrum correlated kurtosis of each component, and select the largest one for envelope spectrum analysis. Through simulation and experimental analysis, it is verified that this method can accurately extract the fault characteristics of wheelset bearing under complex interference.

A New Method of Wheelset Bearing Fault Diagnosis

During the movement of rail trains, trains are often subjected to harsh operating conditions such as variable speed and heavy loads. It is therefore vital to find a solution for the issue of rolling bearing malfunction diagnostics in such circumstances. This study proposes an adaptive technique for defect identification based on multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and Ramanujan subspace decomposition. MOMEDA optimally filters the signal and enhances the shock component corresponding to the defect, after which the signal is automatically decomposed into a sequence of signal components using Ramanujan subspace decomposition. The method’s benefit stems from the flawless integration of the two methods and the addition of the adaptable module. It addresses the issues that the conventional signal decomposition and subspace decomposition methods have with redundant parts and significant inaccuracies in fault feature extraction for the vibration signals under loud noise. Finally, it is evaluated through simulation and experimentation in comparison to the current widely used signal decomposition techniques. According to the findings of the envelope spectrum analysis, the novel technique can precisely extract the composite flaws that are present in the bearing, even when there is significant noise interference. Additionally, the signal-to-noise ratio (SNR) and fault defect index were introduced to quantitatively demonstrate the novel method’s denoising and potent fault extraction capabilities, respectively. The approach works well for identifying bearing faults in train wheelsets.

A Two-Stage Method for Weak Feature Extraction of Rolling Bearing Combining Cyclic Wiener Filter with Improved Enhanced Envelope Spectrum

Due to the interference of various strong background signals, it is often difficult to extract effective features by using conventional methods such as envelope spectrum analysis when early weak fault arises in rolling bearing. Inspired by the current two main research directions of weak fault diagnosis of rolling bearing, that is, the enhancement of impulse features of faulty vibration signal through vibration analysis and the selection of fault information sensitive frequency band for further envelope spectrum analysis, and based on the second-order cyclostationary characteristic of the vibration signal of faulty bearing, a two-stage method for weak feature extraction of rolling bearing combining cyclic Wiener filter with improved enhanced envelope spectrum (IEES) is proposed in the paper. Firstly, the original vibration signal of the rolling bearing’s early weak fault is handled by cyclic Wiener filter exploiting the spectral coherence (SCoh) theory and the noise components are filtered out. Then, SCoh is applied on the filtered signal. Subsequently, an IEES method obtained by integrating over the selected fault information sensitive spectral frequency band of the SCoh spectral is used to extract the fault features. The innovation of the proposed method is to fully excavate the advantages of cyclic Wiener filter and IEES simultaneously. The feasibility of the proposed method is verified by simulation firstly, and vibration signals collected from accelerated bearing degradation tests and engineering machines are used to further verify its effectiveness. Additionally, its superiority over the other state-of-the-art methods is also compared.

Study on the extraction method of the friction-induced vibration signal of rotary compressors

The operation noise level of air conditioners has been a main concern to consumers. The friction-induced vibration and noise have a contribution to the overall operation noise level of air conditioners. In this paper, two rotary compressors were tested to measure the vibration and noise of the compressors. The friction-induced vibration signals were extracted using a combination of the harmonic wavelet transform and the envelope spectrum analysis. The two rotary compressors were disassembled to check whether the compressor parts have been worn after testing. Results show that when the crankshaft was worn, the friction-induced vibration of compressors is strong. At low rotational speeds of the rotary compressor, the friction-induced vibration is easily excited, which is probably attributed to the boundary lubrication at the interface between the crankshaft and bearing. At high rotational speeds of the rotary compressor, the friction-induced vibration is not easy to be excited, which is probably attributed to the aerodynamic lubrication at the interface between the crankshaft and bearing.

A Novel Based on the Log-Squared Envelope Harmonic Noise Ratio Fault Transient Feature Extraction Method

To counter the sensitivity of kurtosis for the random pulse, a novel periodic detection index based on the log-squared envelope harmonic noise ratio (LSEHNR) is constructed, and then, a novel LSEHNRgram feature extraction method is proposed by introducing the maximal overlap discrete wavelet packet transform (MODWPT). Firstly, MODWPT is used to decompose the frequency band of the signal and a series of subsignals are obtained from each stage of decomposition, which are called nodes. Secondly, autocorrelation of the log-squared envelope for each node at each level is computed; calculate the LSEHNR, simultaneously. Then, the LSEHNR values of all nodes are calculated and the node with the maximal of the LSEHNR value is selected for spectrum analysis. Finally, the square envelope spectrum (SES) analysis is performed on the in-band filtered signal to extract the transient characteristics of the rolling bearing fault. The experimental results indicate that the LSEHNRgram method can accurately detect the demodulation frequency band and enhance the fault features under the condition of the low noise-to-signal ratio (SNR). The effectiveness of the method is verified on numerical and experimental datasets by comparison with autogram, infogram, and fast kurtgram methods.

Fault Diagnosis of Rolling Bearing Based on an Improved Denoising Technique Using Complete Ensemble Empirical Mode Decomposition and Adaptive Thresholding Method

PurposeThe vibration signals captured for rolling bearing are generally polluted by excessive noise and can lose the fault information at the early development phase. Therefore, denoising is required to enhance the signal quality by improving kurtosis parameter sensitivity and envelope spectrum performance for early fault detection.MethodsIn this paper, an improved denoising technique is proposed for early faults diagnosis of rolling bearing based on the complete ensemble empirical mode decomposition (CEEMD) and adaptive thresholding (ATD) method. Firstly, the bearing vibration signals are decomposed into a set of various intrinsic mode functions (IMFs) using the CEEMD algorithm. The IMFs grouping and selection are formed based upon the approximate entropy and correlation coefficient value. After IMFs selection, the noise-predominant IMFs are subjected to adaptive thresholding for denoising and then added to the low-frequency IMFs for signal reconstruction.ResultsThe effectiveness of the proposed method denoised signals are tested on two experimental datasets based on kurtosis value and the envelope spectrum analysis. The results on the first dataset shows significant improvement in the kurtosis parameter values such as 92.99, 90.92, 97.39, and 78.35 for inner race fault in the bearing and 113.1, 170, 195.1, and 197.4 for outer race fault in the bearing rotating at four different speeds of 1797, 1772, 1750, and 1730 rpm, respectively, and enhances the amplitude of envelope spectrum to easily detect the bearing fault characteristics frequencies. The developed methods are further tested on the second dataset yielding similar improvement in kurtosis value and envelope spectrum analysis.ConclusionThe presented method results on experimental datasets illustrate that the proposed approach is an effective denoising technique for early fault detection in the rolling bearing compared to the original and other two conventional approaches.

A low-frequency fault detection method for low-speed planetary gearbox based on acoustic signals

In some practical application of construction machinery and equipment, the planetary gearbox in the walking parts is wrapped with tires, and its speed is very low. When this planetary gearbox fails, the fault frequency is very low (less than 1 Hz) due to its complex transmission relationship. Because the planetary gearbox has the characteristics of low speed, low fault frequency and surrounded by tires, it is difficult to detect its fault condition in practical engineering. This work proposes a new low-frequency fault detection method. First, the pre-processing is used for the measured acoustic signals. Second, the pre-processed acoustic signals are decomposed into components by the multi-resolution singular value decomposition. Third, the autoregressive model is constructed to select fault-related component. Finally, the output component is further analyzed by envelope spectrum analysis. The results indicate that the proposed method can accurately detect the characteristic information of low frequency faults of planetary gearbox in practical engineering.

Informative singular value decomposition and its application in fault detection of planetary gearbox

The fault features of planetary gearboxes are modulated complexly and are submerged by other signal components, for its vibration signal has the characteristics of multi-source and multi transmission path. A fault detection method of planetary gearboxes based on informative singular value decomposition and envelope spectrum analysis (ISVD-ESA) is proposed in this paper. In this method, the advantage of blind source separation of singular value decomposition (SVD) method is combined with the ability of negentropy and cyclic autocorrelation (CA) in non-Gaussian characteristics recognition. The fast SVD is firstly performed to decompose the vibration signal into a series of singular value decomposition component signals (SVCSs). Secondly, the detector of negentropy combined with CA is applied to estimate the fault informativeness of each SVCS. The SVCSs are amplified by the fault informativeness and reconstructed to the out signal of ISVD. Finally, the fault features can be extracted by the ESA from the output signal of ISVD. The performance of the proposed method is verified by simulation and experimental studies. Results show that the proposed ISVD-ESA strategy can enhance the weak features of multi-modulation and accurately extract the faults of tooth tip pitting and misalignment of sun gear of the planetary gearbox.

A novel method for diagnosing rolling bearing faults based on the frequency spectrum distribution of the modulation signal

Bearing fault diagnosis is required to monitor the running status of rolling bearings, and can greatly reduce the loss caused by rolling bearing faults. It is a very important aspect of prognostic and health management. In this paper, a new method for fault diagnosis, based on an improved fast kurtogram and novel envelope spectrum analysis, is proposed to diagnose rolling bearing faults. In the proposed method, the improved fast kurtogram method is used to select the center frequency and bandwidth of the optimal signal filter which is used to filter the raw bearing vibration signals. Then, the filtered signal is transformed to the frequency domain. Novel envelope spectrum analysis is used to analyze the amplitude distribution of the envelope spectrum waveforms in order to extract more useful features from different zones rather than the whole frequency domain. The extracted features are used to calculate the fitting ratio for diagnosing bearing faults. The proposed method is validated on the fault data of rolling bearings provided by CWRU and QPZZ-II platforms. The experimental results show that the proposed method can efficiently extract features and diagnose rolling bearing faults.

A compound fault diagnosis method of rolling bearing based on wavelet scattering transform and improved soft threshold denoising algorithm

The vibration signal of faulty rolling bearing of rotating machine carries a large amount of information reflecting its fault categories. However, compound fault features are easily mixed together, and can cause missed diagnosis and misjudgment, which is still a challenging task in mechanical fault diagnosis. A compound fault detection method using wavelet scattering transform (WST) and an improved soft threshold denoising algorithm is proposed to extract compound faults in bearings. First, the wavelet scattering transform is used to calculate the original scattering coefficients from vibration signals. Second, the improved soft threshold denoising algorithm is applied to obtain the renewable scattering coefficients, which are further employed to reconstruct the denoising signals. Third, process the envelope spectrum analysis on the denoising signal to extract fault features. Finally, both the simulations and experiments in associate with comparison investigations proved that this method can effectively detect compound faults in bearings.

Friction-Excited Oscillation of Air Conditioner Rotary Compressors: Measurements and Numerical Simulations

The oscillation of a rotary compressor was measured and analyzed in the frequency and time domains. The harmonic wavelet transform was used to dissolve the oscillation signal into a series of single-frequency components. A power spectrum analysis of the single-frequency components shows that there are many vibration components whose frequencies are one, double, triple, quadruple, and even tens of times the rotating frequency. An envelope spectrum analysis shows that some single-frequency components originate from the friction-excited oscillation of the compressor. A full-size mode-coupling model of the rotary compressor was established to forecast the friction-excited oscillation of the compressor using the transient dynamics method and the complex eigenvalue method. The measurement results are consistent with the predictive results. A sensitivity analysis of the main parameters shows that the coefficient of friction has a major impact on the development tendency of the friction-excited oscillation of rotary compressors.

A Novel Method to Classify Rolling Element Bearing Faults Using K-Nearest Neighbor Machine Learning Algorithm

Abstract A novel method is proposed in this work for the classification of fault in the ball bearings. Applications of K-nearest neighbor (KNN) techniques are increasing, which redefines the state-of-the-art technology for defect diagnosis and classification. Vibration characteristics of deep groove ball bearing with different defects are studied in this paper. Experimentation is conducted at different loads and speeds with artificially created defects, and vibration data are processed using kurtosis to find frequency band of interest and amplitude demodulation (Envelope spectrum analysis). Bearing fault amplitudes are extracted from the filtered signal spectrum at bearing characteristic frequency. The decision of fault classification is made using a KNN machine learning classifier by training feature data. The training features are created using characteristics amplitude at different fault and bearing conditions. The results showed that the KNN's accuracies are 100% and 97.3% when applied to two different experimental databases. The quantitative results of the KNN classifier are applied as the guidance for investigating the type of defects of bearing. The KNN Classifier method proved to be an effective method to quantify defects and significantly improve classification efficiency.