Multipoint Optimal Minimum Entropy Deconvolution Adjusted Research Articles

Multipoint Optimal Minimum Entropy Deconvolution Adjusted for Automatic Fault Diagnosis of Hoist Bearing

Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is a powerful method that can extract the periodic characteristics of signal effectively, but this method needs to evaluate the fault cycle a priori, and moreover, the results obtained in a complex environment are easily affected by noise. These drawbacks reduce the application of MOMEDA in engineering practice greatly. In order to avoid such problems, in this paper, we propose an adaptive fault diagnosis method composed of two parts: fault information integration and extracted feature evaluation. In the first part, a Teager energy spectrum amplitude factor (T‐SAF) is proposed to select the intrinsic mode function (IMF) components decomposed by ensemble empirical mode decomposition (EEMD), and a combined mode function (CMF) is proposed to further reduce the mode mixing. In the second part, the particle swarm optimization (PSO) taking fractal dimension as the objective function is employed to choose the filter length of MOMEDA, and then the feature frequency is extracted by MOMEDA from the reconstructed signal. A cyclic recognition method is proposed to appraise the extracted feature frequency, and the evaluation system based on threshold and weight coefficient removes the wrong feature frequency. Finally, the feasibility of the method is verified by simulation data, experimental signals, and on‐site signals. The results show that the proposed method can effectively identify the bearing state.

A fault feature extraction algorithm based on CEEMD-TVD-MOMEDA

ABSTRACT Rolling bearings are indispensable key components in mechanical equipment, and they are also one of the most easily damaged components. To solve the problem of bearing fault feature extraction under strong noise interference, a combination of complementary ensemble average empirical mode decomposition (CEEMD), total variation denoising (TVD) and multipoint optimal minimum entropy deconvolution adjusted(MOMEDA) is proposed. Firstly, decompose the vibration signal into several signal components. Secondly, the qualified IMF signal components are selected by combining with the cross-correlation analysis criteria for reconstruction, and TVD is used to reduce the noise of the signal. Thirdly, MOMEDA is used to filter the denoised signal, so as to enhance the periodic impact component. Finally, the envelope spectrum of the filtered signal is analyzed. The effectiveness of the proposed method is verified by the simulation signals and the bearing fault data set of Case Western Reserve University. The experimental results show that the proposed method can not only reduce the noise interference, but also effectively extract and identify the rolling bearing fault features. Compared with the results obtained by traditional LMD and ITD methods, it has better recognition effect.

Research on Fault Extraction Method of CYCBD Based on Seagull Optimization Algorithm

Maximum cyclostationarity blind deconvolution (CYCBD) can recover the periodic impulses from mixed fault signals comprised by noise and periodic impulses. In recent years, blind deconvolution has been widely used in fault diagnosis. However, it requires a preset of filter length, and inappropriate filter length may cause the inaccurate extraction of fault signal. Therefore, in order to determine filter length adaptively, a method to optimize CYCBD by using the seagull optimization algorithm (SOA) is proposed in this paper. In this method, the ratio of SNR to kurtosis is used as the objective function; firstly, SOA is used to search the optimal filter length in CYCBD by iteration, and then it uses the optimal filter length to perform CYCBD; finally, the frequency‐domain waveform is determined through Fourier transformation. The method proposed is applied to the fault extraction of a simulated signal and a test vibration signal of the closed power flow gearbox test bed, and the fault frequency is successfully extracted, in addition, using maximum correlation kurtosis deconvolution (MCKD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) to compare with CYCBD‐SOA, which validated availability of the proposed method.

Maximum average kurtosis deconvolution and its application for the impulsive fault feature enhancement of rotating machinery

Blind deconvolution (BD) is a popular tool for vibration analysis, which has been extensively studied to extract useful information from contaminative signals for the diagnosis of rotating machinery. However, due to the disturbance of diverse interferences, good performance of conventional BD methods is usually hard to be guaranteed in some situations. Especially, when the rotating speed is time-varying, some advanced methods like maximum correlated kurtosis deconvolution (MCKD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) are even impracticable. To address these issues, the maximization of a new index named average kurtosis (AK) is treated as the objective function in this paper for deconvolution, i.e. maximum average kurtosis deconvolution (MAKD). AK inherently highlights the periodic impulses from angular domain, which is not only robust to some typical interferences, but also compatible with the variable speed condition. In this framework, an optimized Morlet wavelet is employed as the initial filter in the deconvolution process, which contributes to improving both the efficiency and performance of MAKD. The simulation analysis is conducted to demonstrate the robustness and capability of proposed method compared with several popular deconvolution methods, and experimental cases involving the failures of bearing and gear are further analyzed to clarify its practicability.

Identification of planetary gearbox weak compound fault based on parallel dual-parameter optimized resonance sparse decomposition and improved MOMEDA

Abstract Due to the complexity and harsh operating environment of planetary gear transmission system, compound fault may co-exist in the planetary gearbox, which results in different type faults coupling together and the weak fault impulse features being completely submerged by strong ambient noise, thus making it a great challenge to extract fault-related information from planetary gearbox. To address these issues, a novel compound fault features extraction technique based on parallel dual-parameter optimized resonance-based sparse signal decomposition (RSSD) and improved multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is proposed in this contribution. Firstly, according to the oscillation properties of different type faults, the parallel dual-parameter optimized RSSD constructs wavelet basis functions matching with fault features to adaptively decouple compound fault signal into high and low resonance components. Then, the improved MOMEDA is applied to deconvolute the resonance components to eliminate the interference of complex transmission path and strong ambient noise, thereby enhancing the weak periodic fault impulses. Finally, envelope demodulation processing for the enhanced signals is carried out to extract fault characteristic frequencies and identify different type faults. The effectiveness and feasibility of the proposed method are validated using both the numerical simulated signals and practical experimental dates from two different types of planetary gearbox compound fault. Moreover, comparisons with some existing methods illustrate the superiority of the proposed method to identify weak compound fault under strong ambient noise.

Research on Fault Feature Extraction Method Based on FDM-RobustICA and MOMEDA

Aiming at the difficulty of extracting rolling bearing fault features under strong background noise conditions, a method based on the Fourier decomposition method (FDM), robust independent component analysis (RobustICA), and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is proposed. Firstly, the FDM method is introduced to decompose the single-channel bearing fault signal into several Fourier intrinsic band functions (FIBF). Secondly, by setting the cross-correlation coefficient and kurtosis as a new selection criterion, it can effectively construct the virtual noise channel and the observation signal channel, which makes RobustICA complete the separation of the useful signal and noise well. Finally, MOMEDA is introduced to enhance the periodic impact components in the denoised signal, and then the filtered signal is analyzed by the Hilbert envelope spectrum to extract the fault characteristic frequency. Through the experimental analysis of the simulated signals and the actual bearing fault signals, the results show that the proposed method not only has the ability to suppress noise and accurately extract fault feature information but also has better performance than the traditional method of local mean decomposition (LMD) and intrinsic time-scale decomposition (ITD), highlighting its practicality in the fault diagnosis of rotating machinery.

Research on Extraction of Compound Fault Characteristics for Rolling Bearings in Wind Turbines.

Wind turbines work in strong background noise, and multiple faults often occur where features are mixed together and are easily misjudged. To extract composite fault of rolling bearings from wind turbines, a new hybrid approach was proposed based on multi-point optimal minimum entropy deconvolution adjusted (MOMEDA) and the 1.5-dimensional Teager kurtosis spectrum. The composite fault signal was deconvoluted using the MOMEDA method. The deconvoluted signal was analyzed by applying the 1.5-dimensional Teager kurtosis spectrum. Finally, the frequency characteristics were extracted for the bearing fault. A bearing composite fault signal with strong background noise was utilized to prove the validity of the method. Two actual cases on bearing fault detection were analyzed with wind turbines. The results show that the method is suitable for the diagnosis of wind turbine compound faults and can be applied to research on the health behavior of wind turbines.

Enhanced Characteristic Vibration Signal Detection of Generator Based on Time-Wavelet Energy Spectrum and Multipoint Optimal Minimum Entropy Deconvolution Adjusted Method

To overcome the shortage of low SNR (signal to noise ratio) of the multipole generator vibration signal which brings rigid difficulty to the fault diagnosis, a new method which combines the Time-Wavelet Energy Spectrum (TWES) with the Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) algorithm is proposed. This method uses TWES to extract and enhance the characteristic signal, while employing MOMEDA to optimize the spectrum structure and filter the noise. The application of this method to the simulating signal as well as the test stator vibration signal in a 6-pole generator before and after rotor interturn short circuit fault validates the effectiveness of the method. Moreover, the comparison among the proposed method and some other general methods such as the Empirical Mode Decomposition (EMD) and the maximum correlative kurtosis deconvolution (MCKD) suggests that the proposed method is superior to these methods.

Blind deconvolution assisted with periodicity detection techniques and its application to bearing fault feature enhancement

Abstract Maximum correlated kurtosis deconvolution (MCKD), multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and maximum second-order cyclostationarity blind deconvolution (CYCBD) have remarkable performances in extracting periodic impulses. However, these deconvolution methods highly rely on the prior period of measured signal and can only enhance the specific periodic impulses. Aiming at these limitations, six kinds of periodicity detection techniques (PDTs) are introduced to adaptively identify the period of repetitive impulses. Further, PDTs-assisted MCKD, MOMEDA and CYCBD are proposed for bearing fault feature enhancement. The improved deconvolution methods have two characteristics: first, the fault period is automatically identified by PDTs according to the characteristics of the measured signal; second, the impulses of different faults can be enhanced adaptively. The analysis results of simulated and experimental datasets demonstrated the better capability of the proposed methods in enhancing bearing fault features with respect to original deconvolution methods and the fast kurtogram method.

Adaptive Multipoint Optimal Minimum Entropy Deconvolution Adjusted and Application to Fault Diagnosis of Rolling Element Bearings

Extracting fault-related features from noisy vibration signals is a pivotal prerequisite for condition monitoring of rolling element bearings. Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is a non-iterative blind deconvolution algorithm and has been proven as an efficient tool for periodic impulses extraction of rotating machinery. However, the effectiveness of MOMEDA strongly relies on the accuracy of the prior impulse period. Additionally, the MOMEDA method suffers from serious edge effect, resulting in the filtered signal shorter than the original signal. To solve these limitations of MOMEDA, a novel deconvolution algorithm called adaptive MOMEDA (AMOMEDA) is presented in this paper. The new method adopts a periodic modulation intensity (PMI) based strategy to automatically estimate the impulse period rather than a fixed prior period. The impulse period can gradually approximate the real fault-related period with the update of the filtered signal after every iterative step. To overcome the edge effect of MOMEDA, a waveform extension strategy is designed to adaptively restore the length of the filtered signal as the original signal according to the local characteristics of the signal at the left boundary. Simulated and experimental results of railway axle-box bearing demonstrated the effectiveness and superiority of AMOMEDA by comparison with MOMEDA. Moreover, the proposed method is compared with fast kurtogram and the improved complete ensemble empirical mode decomposition with adaptive noise to show that the proposed method is more effective.

Application of a New Enhanced Deconvolution Method in Gearbox Fault Diagnosis

When the mechanical transmission mechanism fails, such as gears and bearings in the gearbox, its vibration signal often appears as a periodic impact. Considering the influence of noise, however, the fault signal is often submerged in the noise, so it is necessary to propose a feasible and effective fault extraction method. MOMEDA (multipoint optimal minimum entropy deconvolution adjusted) overcomes the tedious iterative process of MED (minimum entropy deconvolution) and overcomes the resampling trouble in MCKD (maximum correlated kurtosis deconvolution). It is suitable for dealing with periodic impact signal. Besides, aiming at the poor ability of MOMEDA to capture the deconvolution result of target function in a strong noise environment, this paper proposes an improved MOMEDA gearbox fault feature extraction method. Considering that MOMEDA has poor anti-noise performance and can easily cause misdiagnosis in a strong noisy environment, this paper constructs an autoregressive mean sliding model to improve the noise immunity of MOMEDA. Firstly, the stability of the test signal is judged by the autocorrelation coefficient (ACF) and the partial correlation coefficient (PACF). Secondly, the ARMA (autoregressive moving average) model is constructed and a set of optimal model coefficients are obtained to filter the signal, which greatly improves MOMEDA’s ability to capture fault features. Thirdly, the fault feature is extracted by MOMEDA, and the fault information is extracted accurately under a strong noise environment. Finally, compared with AR-MED, ARMAMED, and other methods, the advantages of ARMAMOMEDA are verified. Moreover, the effectiveness and superiority of the proposed method are verified by simulation signals and experimental data from the Case Western Reserve University Bearing Data Center.

Research on Bearing Fault Diagnosis Method Based on Filter Features of MOMLMEDA and LSTM

As the supporting unit of rotating machinery, bearing can ensure efficient operation of the equipment. Therefore, it is very important to monitor the status of bearings accurately. A bearing fault diagnosis mothed based on Multipoint Optimal Minimum Local Mean Entropy Deconvolution Adjusted (MOMLMEDA) and Long Short-Term Memory (LSTM) is proposed. MOMLMEDA is an improved algorithm based on Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA). By setting the local kurtosis mean as a new selection criterion, it can effectively avoid the interference of false kurtosis caused by noise and improve the accuracy of optimal kurtosis position. The optimal filter designed by optimal kurtosis position has periodic and amplitude characteristics, which are used as the fault feature in this paper. However, this feature has temporal characteristics and cannot be used as input of general neural network directly. LSTM is selected as the classification network in this paper. It can effectively avoid the influence of the temporal problem existing in feature vectors. Accurate diagnosis of bearing faults is realized by training classification neural network with samples. The overall recognition rate is up to 93.50%.

An enhanced multipoint optimal minimum entropy deconvolution approach for bearing fault detection of spur gearbox

Previous research has shown that minimum entropy deconvolution (MED) is an effective technique for detecting impulse-like signals, such as the bearing fault and gear fault signals. However, some problems still exist in this technique. With the aim of overcoming these limitations, in this paper, an enhanced MED called multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is proposed. MOMEDA can succeed in detecting multiple impulses. Unfortunately, according to some simulations and real tests in this work, the results of applying this technique to the fault signals directly were grudgingly acceptable but not very satisfactory, especially under a harsh working condition. This means that MOMEDA is a little sensitive to intensive background noise and vibration interference. To overcome this drawback, a novel mode decomposition method, named time-varying filtering for empirical mode decomposition (TVFEMD), is applied to adaptively eliminate background noise and vibration interference prior to using MOMEDA. According to this proposed method, the weak bearing fault features can be identified clearly. The proposed approach is utilized in bearing fault detection of a spur gearbox and the results show its superiority and effectiveness.

MVMD-MOMEDA-TEO Model and Its Application in Feature Extraction for Rolling Bearings.

In order to extract fault features of rolling bearings to characterize their operation state effectively, an improved method, based on modified variational mode decomposition (MVMD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA), is proposed. Firstly, the MVMD method is introduced to decompose the vibration signal into intrinsic mode functions (IMFs), and then calculate the energy ratio of each IMF component. The IMF component is selected as the effective component from high energy ratio to low in turn until the total energy proportion Esum(t) ≥ 90%. The IMF effective components are reconstructed to obtain the subsequent analysis signal x_new(t). Secondly, the MOMEDA method is introduced to analyze x_new(t), extract the fault period impulse component x_cov(t), which is submerged by noise, and demodulate the signal x_cov(t) by Teager energy operator demodulation (TEO) to calculate Teager energy spectrum. Thirdly, matching the dominant frequency in the spectrum with the fault characteristic frequency of rolling bearings, the fault feature extraction of rolling bearings are completed. Finally, the experiments have compared MVMD-MOEDA-TEO with MVMD-TEO and MOMEDA-TEO based on two different data sets to verify the superiority of the proposed method. The experimental results show that MVMD-MOMEDA-TEO method has better performance than the other two methods, and provides a new solution for condition monitoring and fault diagnosis of rolling bearings.

Research and application of improved adaptive MOMEDA fault diagnosis method

In a strong noise environment, vibration signals are easily submerged by noise. In recent years, many scholars have studied a large number of noise reduction methods. In 2017, Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is applied to the fault diagnosis of gearbox. Although MOMEDA overcomes Maximum correlation kurtosis deconvolution (MCKD) defects and it can extract continuous impulse signal, but it still has the following problems: 1) It can only extract single periodic pulse. If we want to extract the characteristics of multiple periodic pulse signals, we need to further update the algorithm; 2) In a strong noise environment, MOMEDA can also search for a fixed periodic signal, but most of the information is false component, it is easy to cause misdiagnosis, therefore, the signal needs to be preprocessed; 3) The accuracy of MOMEDA noise reduction is affected by the search interval and filter size, and McDondald did not reasonably explain them, so an adaptive selection method is needed. Considering these problems. Firstly the article preprocesses the composite fault with ensemble empirical mode decomposition (EEMD) and then reconstructs the intrinsic mode function with the same time scale. Further, proposing kurtosis spectral entropy as the objective function, the grid search method is used to search the filter length of MOMEDA, and the reconstructed intrinsic mode function is further denominated by MOMEDA. Finally, the proposed method is used to search the complex fault pulse signals in strong noise environment. It proves its reliability.

Planet bearing fault diagnosis using multipoint Optimal Minimum Entropy Deconvolution Adjusted

The vibration signals of planet bearings exhibit periodic impulsive features and are modulated by various effects, including: 1) time-varying vibration transfer path; 2) load zone passing; 3) time-varying angles between gear pair mesh lines of action and impact force vector; and 4) bearing fault. At the same time, slippages of rolling elements in bearings can lead to randomized periods of impulses and blurs in the Fourier spectrum of planet bearing vibration signals. As a result, common methods have limitations in analyzing planet bearing signals. In this paper, we establish a model of planet bearing vibration signal considering slippages in rolling elements, then derive its Fourier spectrum to illustrate the impact of slippages on Fourier spectrum. In order to extract the features of impulses caused by faults, we apply a deconvolution algorithm called Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA), which is good at deconvoluting signals with periodic impulses. For adaption to planet bearing vibration, we redesign the target vector of MOMEDA to simulate the actual stochastic impulses caused by slippages. To quantify the severity of slippages and describe the randomness of impulses, we propose the slippage parameter and utilize negentropy to determinate it. Lastly, the proposed method is validated using both numerical simulation and experimental data. We detect faults in outer race, rolling element and inner race of planet bearings, respectively. Compared with traditional Fourier and envelope spectra as well as kurtogram filtering method, MOMEDA shows specific applicability for fault diagnosis of planet bearings.

Particle swarm optimization algorithm to solve the deconvolution problem for rolling element bearing fault diagnosis

Extraction of the fault related impulses from the raw vibration signal is important for rolling element bearing fault diagnosis. Deconvolution techniques, such as minimum entropy deconvolution (MED), MED adjusted (MEDA) and maximum correlated kurtosis deconvolution (MCKD), optimal MED adjusted (OMEDA) and multipoint optimal MED adjusted (MOMEDA), are typical techniques for enhancing the impulse-like component in the fault signal. This paper introduces the particle swarm optimization (PSO) algorithm to solve the filter of deconvolution problem. The proposed approaches solve the filter coefficients of the deconvolution problems by the PSO algorithm, assisted by a generalized spherical coordinate transformation. Compared with MED, MEDA, and OMEDA, the proposed PSO-MED and PSO-OMEDA can effectively overcome the influence of large random impulses and tend to deconvolve a series of periodic impulses rather than a signal impulse. Compared with MCKD and MOMEDA, the proposed PSO-MCKD and PSO-MOMEDA can achieve good performances even when the fault period is inaccurate. The effectiveness of the proposed methods is validated by the simulated signals. The study of experimental bearing fault signal shows that the PSO based deconvolution methods delivered better performance for rolling element bearing fault detection than the traditional deconvolution methods. Additionally, the proposed methods are compared with the following two popular signal processing methods: the ensemble empirical mode decomposition (EEMD) and fast kurtogram, which are used to highlight the improved performance of the proposed methods.

A Parameter Adaptive MOMEDA Method Based on Grasshopper Optimization Algorithm to Extract Fault Features

The nonstationary components and noises contained in the bearing vibration signal make it particularly difficult to extract fault features, and minimum entropy deconvolution (MED), maximum correlated kurtosis deconvolution (MCKD), and fast spectral kurtosis (FSK) cannot achieve satisfactory results. However, the filter size and period range of multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) need to be set in advance, so it is difficult to achieve satisfactory filtering results. Aiming at these problems, a parameter adaptive MOMEDA feature extraction method based on grasshopper optimization algorithm (GOA) is proposed. Firstly, the multipoint kurtosis (MKurt) of MOMEDA filtered signal is used as the optimization objective, and the optimal filter size and periodic initial value which matched with the vibration signal can be determined adaptively through multiple iterations of GOA. Secondly, the periodic impact contained in the vibration signal is extracted by the optimized MOMEDA, and the fault features in the impact signal are extracted by Hilbert envelope demodulation. Finally, the simulation signal and bearing signal are processed by the proposed approach. The results show that the introduction of GOA not only solves the problem of parameter selection in MOMEDA, but also achieves better performance compared with other optimization methods. Meanwhile, the feasibility and superiority of the approach are fully proved by comparing it with the three methods MED, MCKD, and FSK after parameter optimization. Therefore, this approach provides a novel way and solution for fault diagnosis of the rolling bearing, gear, and shaft.

Feature extraction for rolling element bearing weak fault based on MOMEDA and ICEEMDAN

There are always the nonlinear and non-stationary characteristics and periodic pulse in vibration signals of rolling element bearings when there are partial faults in those bearings. Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) overcomes the presence of spurious modes and residual noise in Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), but it cannot clearly and accurately extract the weak fault feature of rolling element bearings under the strong background noise. Here, Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) was proposed. A bearing simulator was used to collect vibration signals of bearing inner and outer race, which was enhanced by MOMEDA, decomposed into several Intrinsic Mode Functions(IMFs) by ICEEMDAN, and analyzed by the envelope demodulation, finally gaining the frequency of shaft speed, BPFI (ball pass frequency, inner race) and harmonics, sidebands spaced, BPFO (ball pass frequency, outer race) and harmonics. The results show that this method can be used to accurately extract different frequency components of bearing fault vibration signals and diagnose bearing different fault location.

Application of an Improved Multipoint Optimal Minimum Entropy Deconvolution Adjusted for Gearbox Composite Fault Diagnosis.

The fault feature extraction of gearbox is difficult to achieve under complex working conditions, and this paper presents a hybrid fault diagnosis method for gearbox based on the combining product function (CPF) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) methods. First, ensemble local mean decomposition (ELMD) is utilized to reduce the noise in original signal, and get a series of product functions (PFs), through the correlation coefficient method to remove false components and residual components. Then, multi-point kurtosis of the definition is achieved by calculating the multi-point kurtosis spectrum of each layer PF, and the fault feature period is extracted and the PFs without periodic impact are removed. After that, in order to maintain the integrity of the original signal, the PFs with the same period are recombined by the combined product function method. Finally, the different cycle interval is configured, reduce the noise through MOMEDA on the combined signal, to further extract the fault feature. The method is applied to the feature extraction of gear box composite fault to verify the feasibility of this method.