Multipoint Optimal Minimum Entropy Deconvolution Research Articles

Rolling Bearing Fault Feature Extraction Method based on SSA-VMD and MOMEDA

To address the challenge of extracting bearing fault features, this study proposes a new rolling bearing fault feature extraction method based on the Sparrow Search Algorithm (SSA) to optimize Variational Mode Decomposition (VMD) and Multipoint Optimal Minimum Entropy Deconvolution with Convolution Adjustment (MOMEDA). Firstly, SSA is employed to identify optimal parameters in VMD, followed by the utilization of correlation coefficients and kurtosis to filter relevant Intrinsic Mode Function (IMF) components. Subsequently, MOMEDA is applied to denoise the reconstructed signal, mitigating the interference caused by pulse fault signals. Finally, the envelope spectrum analysis is conducted on the denoised signal. Experimental results demonstrate the efficacy of the proposed method in extracting fault features and mitigating noise interference.

Automated ultrasonic testing for near-surface flaws in CFRP

ABSTRACT In ultrasonic testing of Carbon Fiber Reinforced Polymers (CFRP), signals of near-surface flaws are often submerged in interface signals, resulting in blind spots for defect detection. To address this issue, this paper presents an autocorrelation imaging algorithm that combines Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) with l 0 -norm sparse representation. Firstly, the ultrasonic signal can be modelled as a convolution process. By using MOMEDA, the sequence of reflected pulses in the ultrasonic signal is obtained. Then, the l 0 -norm sparse representation is utilised to enhance the temporal resolution of the sequence, successfully separating near-surface defect signals from interface signals. Finally, the processed data is input into the autocorrelation algorithm, achieving automated imaging of near-surface flaws. Simulation results demonstrate the efficacy of the algorithm in separating overlapping signals. Finally, experimental validation was conducted on flaws at three different depths. The algorithm presented in this paper is capable of identifying all near-surface flaws, with a defect size error rate consistently below 5%.

Compound fault diagnosis of rolling bearings based on AVMD and IMOMEDA

The intricate nature of compound fault diagnosis in rolling bearings during nonstationary operations poses a challenge. To address this, a novel technique combines adaptive variational mode decomposition (AVMD) with improved multipoint optimal minimum entropy deconvolution adjustment (IMOMEDA). The compound fault signal is isolated through AVMD, with internal parameters obtained via a new indicator termed integrated fault-impact measure index guiding the improved dung beetle optimizer. An adaptive selection method, using a weight factor, chooses the intrinsic mode function containing principal fault data. IMOMEDA whose key parameters are determined by a novel combinatorial strategy is then employed to deconvolute selected fault components, enhancing periodic fault impulses by removing complex interferences and ambient noise. The deconvoluted signal undergoes enhanced envelope spectrum processing to extract fault frequencies and identify fault types. Numerical simulations and experimental data confirm the method’s effectiveness and feasibility for compound faults diagnosis of rolling bearings, showcasing its superiority over existing techniques.

Early faint fault diagnosis of wheelset axlebox bearings in urban rail trains based on ICiSSA-MOMEDA

Circulant Singular Spectrum Analysis (CiSSA) performs well in the decomposition and extraction of the periodic components of nonstationary signals. However, the decomposition of signals in complex environments suffers from spectral aliasing and difficulties in extracting feature information. Therefore, based on CiSSA, an improved CiSSA and multipoint optimal minimum entropy deconvolution adjustment (ICiSSA-MOMEDA) is proposed and applied to the early faint fault diagnosis of axlebox bearings of urban rail train wheelsets. First, the optimal embedding dimension was computed adaptively and accurately using an improved Cao’s method. Then, the initial components obtained from the decomposition were reorganized using the K-ARs method. ICiSSA effectively solves the problems of spectrum confusion and fault-information dispersion. Finally, ICiSSA was combined with MOMEDA to improve its ability to detect weak fault information. The superiority of ICiSSA-MOMEDA was verified based on the analysis of the actual bearing data and comparison with other methods.

A Rolling Bearing Fault Feature Extraction Algorithm Based on IPOA-VMD and MOMEDA.

Since the rolling bearing fault signal captured by a vibration sensor contains a large amount of background noise, fault features cannot be accurately extracted. To address this problem, a rolling bearing fault feature extraction algorithm based on improved pelican optimization algorithm (IPOA)-variable modal decomposition (VMD) and multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) methods is proposed. Firstly, the pelican optimization algorithm (POA) was improved using a reverse learning strategy for dimensional-by-dimensional lens imaging and circle mapping, and the optimization performance of IPOA was verified. Secondly, the kurtosis-square envelope Gini coefficient criterion was used to select the optimal modal components from the decomposed components of the signal, and MOMEDA was used to process the optimal modal components in order to obtain the optimal deconvolution signal. Finally, the Teager energy operator (TEO) was employed to demodulate and analyze the optimally deconvoluted signal in order to enhance the transient shock component of the original fault signal. The effectiveness of the proposed method was verified using simulated and actual signals. The results showed that the proposed method can accurately extract failure characteristics in the presence of strong background noise interference.

Early Fault Detection of Rolling Bearings Based on Time-Varying Filtering Empirical Mode Decomposition and Adaptive Multipoint Optimal Minimum Entropy Deconvolution Adjusted.

Due to the early formation of rolling bearing fault characteristics in an environment with strong background noise, the single use of the time-varying filtering empirical mode decomposition (TVFEMD) method is not effective for the extraction of fault characteristics. To solve this problem, a new method for early fault detection of rolling bearings is proposed, which combines multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) with parameter optimization and TVFEMD. Firstly, a new weighted envelope spectrum kurtosis index is constructed using the correlation coefficient and envelope spectrum kurtosis, which is used to identify the effective component and noise component of the bearing fault signal decomposed by TVFEMD, and the intrinsic mode function (IMF) containing rich fault information is selected for reconstruction. Then, a new synthetic impact index (SII) is constructed by combining the maximum value of the autocorrelation function and the kurtosis of the envelope spectrum. The SII index is used as the fitness function of the gray wolf optimization algorithm to optimize the fault period, T, and the filter length, L, of MOMDEA. The signal reconstructed by TVF-EMD undergoes adaptive filtering using the MOMEDA method after parameter optimization. Finally, an envelope spectrum analysis is performed on the signal filtered by the adaptive MOMEDA method to extract fault feature information. The experimental results of the simulated and measured signals indicate that this method can effectively extract early fault features of rolling bearings and has good reliability. Compared to the classical FSK, MCKD, and TVFEMD-MOMEDA methods, the first-order correlated kurtosis (FCK) and fault feature coefficient (FFC) of the filtered signal obtained using the proposed method are the largest, while the sample entropy (SE) and envelope spectrum entropy (ESE) are the smallest.

Bearing fault diagnosis under non-stationary conditions based on a speed signal resonance component demodulation algorithm

This paper proposes a novel bearing fault diagnosis indicator, the resonance component, for motor speed signals. The speed signal was modulated into a higher-frequency band using a double-inertia system, and the bearing fault information was carried and reserved in this high-frequency resonance component. Variational mode decomposition was then used to separate the components unrelated to the resonance based on the parameters optimized using the artificial bee colony algorithm. After envelope demodulation and angular resampling, the multipoint optimal minimum entropy deconvolution adjusted algorithm was utilized to enhance the amplitude of the order spectrum at the corresponding faulty order. Additionally, experiments were successfully conducted on both the outer and inner raceway bearing faults under time-varying conditions using the proposed method. The results indicate that the ordinary diagnosis method using the basic bearing fault characteristic frequency as an indicator is less effective.

Improved Adaptive Multipoint Optimal Minimum Entropy Deconvolution and Application on Bearing Fault Detection in Random Impulsive Noise Environments.

Random impulsive noise is a special kind of noise, which has strong impact features and random disturbances with large amplitude, short duration, and long intervals. This type of noise often displays nonGaussianity, while common background noise obeys Gaussian distribution. Hence, random impulsive noise greatly differs from common background noise, which renders many commonly used approaches in bearing fault diagnosis inapplicable. In this work, we explore the challenge of bearing fault detection in the presence of random impulsive noise. To deal with this issue, an improved adaptive multipoint optimal minimum entropy deconvolution (IAMOMED) is introduced. In this IAMOMED, an envelope autocorrelation function is used to automatically estimate the cyclic impulse period instead of setting an approximate period range. Moreover, the target vector in the original MOMED is rearranged to enhance its practical applicability. Finally, particle swarm optimization is employed to determine the optimal filter length for selection purposes. According to these improvements, IAMOMED is more suitable for detecting bearing fault features in the case of random impulsive noise when compared to the original MOMED. The contrast experiments demonstrate that the proposed IAMOMED technique is capable of effectively identifying fault characteristics from the vibration signal with strong random impulsive noise and, in addition, it can accurately diagnose the fault types. Thus, the proposed method provides an alternative fault detection tool for rotating machinery in the presence of random impulsive noise.

Compound fault diagnosis method for rolling bearings based on the multipoint kurtosis spectrum and AO-MOMDEA

In compound rolling bearing faults, different types of fault signals are coupled and cross-affected, which makes it difficult to separate and extract weak fault features, and making it easy to miss diagnoses or misdiagnose. Aiming at solving this problem, a compound fault diagnosis method for rolling bearings based on the multipoint kurtosis spectrum (MKS) and the aquila optimizer multipoint optimal minimum entropy deconvolution adjusted (AO-MOMEDA) method is proposed. Variational modal decomposition is used to reduce the noise of the original vibration signal and improve the recognition of the MKS so as to predict the fault type and preset the search range of the initial period value. A new fault feature strength index with relative amplitude difference is proposed and, combined with the kurtosis of the envelope spectrum, a multi-objective optimization function is constructed. Through the excellent optimization characteristics of the aquila optimizer, the optimal initial period value and filter length are automatically obtained. The optimized MOMEDA method is used for optimal deconvolution of different types of fault signals. Envelope demodulation of the deconvolution signal is carried out to obtain the 1.5-dimensional spectrum of the envelope signal. The types of rolling bearing composite faults are identified according to the fault features in the 1.5-dimensional spectrum. The feasibility of the proposed method is verified through measurements of the signals of rolling bearings with three different types of compound faults. The experimental results show that the proposed method can overcome the influence of strong fault signals and improve the accuracy of compound fault identification. Our results have application value in engineering practice.

A novel coupled array of multi-stable stochastic resonance under asymmetric trichotomous noise and its application in rolling bearing compound fault diagnosis

Rolling bearings are often subject to various types of damage in actual industrial operations. However, the early fault information is weak and completely overwhelmed by interference from noise, which is difficult to detect. In this paper, a novel coupled array of multi-stable stochastic resonance (NCAMSR) driven by asymmetric trichotomous noise is proposed for compound fault diagnosis. The Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is used to separate the information of different fault features in the compound fault signal, and the outputs of the coupled array system are weighted to find the best effect. The spectral mean signal-to-noise ratio (SMSNR) is designed and the system parameters and weights are optimized using the particle swarm algorithm (PSO). To alleviate the problem of output saturation, the NCAMSR potential model based on the improved Gaussian potential is proposed, and the superior unsaturation of the NCAMSR potential function model is proven by theoretical analysis and experiments. Finally, the excellent performance of NCAMSR in compound fault detection is further verified by using engineering experiments with bearing data from Machinery Faults & Rotor Dynamics Simulator and real damage samples generated from accelerated life tests at Paderborn University, Germany.

Blind Deconvolution Based on Correlation Spectral Negentropy for Bearing Fault.

Blind deconvolution is a method that can effectively improve the fault characteristics of rolling bearings. However, the existing blind deconvolution methods have shortcomings in practical applications. The minimum entropy deconvolution (MED) and the optimal minimum entropy deconvolution adjusted (OMEDA) are susceptible to extreme values. Furthermore, maximum correlated kurtosis deconvolution (MCKD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) are required prior knowledge of faults. On the basis of the periodicity and impact of bearing fault signals, a new deconvolution algorithm, namely one based on maximum correlation spectral negentropy (CSNE), which adopts the particle swarm optimization (PSO) algorithm to solve the filter coefficients, is proposed in this paper. Verified by the simulated vibration model signal and the experimental simulation signal, the PSO-CSNE algorithm proposed in this paper overcomes the influence of harmonic signals and random pulse signals more effectively than other blind deconvolution algorithms when prior knowledge of the fault is unknown.

Research on gear fault diagnosis method based on SSA–VME–MOMEDA

As a common mechanical part, gear is easy to be damaged because of its complex working environment, which can impact the running of the whole transmission device. Thus, it is very important to evaluate the health of gears in time. A gear fault diagnosis method based on multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and variational modal extraction (VME) is proposed to solve the problem that the periodic fault features of gears are difficult to be completely extracted from signals. Meanwhile, sparrow search algorithm (SSA) is introduced to optimize the initial parameters of VME and MOMEDA. First, SSA serves to hunt for the best α of VME, VME serves to obtain the signal near the gear fault frequency, and then SSA serves to hunt for the best L and T values of MOMEDA, and MOMEDA serves to strengthen the gear impact features. Finally, the gear impact features are extracted by envelope spectrum. Simulation and experiment show that this method can extract gear fault components from noise effectively with good results.

Fault feature extraction for planetary bearing of CRF pump in nuclear power plant based on TFDC-QPSO-optimised MOMEDA

To address the issue of weak fault features and difficulty in feature extraction for planetary gearbox bearings of the circulation water pump (CRF pump) unit in the conventional island of a nuclear power plant, a deconvolution method, named the multipoint optimal minimum entropy deconvolution adjusted (MOMEDA), is optimised by a mixing operator improved quantum behaviour particle swarm optimisation (QPSO) algorithm, to extract the periodic fault impulse of planetary gearbox bearings. In the mixing operator, a differential evolution operator is introduced to improve particle swarms’ diversity and enhance the algorithm’s global optimisation capability. Meanwhile, a proposed adaptive CrossOver operator is incorporated into the algorithm to increase its convergence speed. Then, combining these two types of operators can construct a parameter optimisation algorithm displaying both global optimisation capability and high algorithm execution efficiency. Consider capturing the periodic impulsive features relating to bearing faults. A fitness function based on the characteristic information of the deconvolution signal in the time and frequency domain is proposed. This function serves as the objective function to facilitate the parameter optimisation on MOMEDA. Thereby, it enhances the optimisation capability of our feature extraction method. Experiments were conducted by adopting the proposed method on the signals collected from the inner- and outer-race faulty planetary bearing in a test bed, in which favourable feature extraction results are obtained. After the comparative analysis, we observed that both significances of features extracted by this method and the execution efficiency of the algorithm are superior compared to other methods. The on-site feature extraction results of the input shaft bearing in the planetary gearbox of a CRF pump unit in a nuclear power plant also demonstrated the engineering practicability of the method proposed in this work.

Acoustic feature enhancement in rolling bearing fault diagnosis using sparsity-oriented multipoint optimal minimum entropy deconvolution adjusted method

Rolling element bearings are of great importance and widely used in rotating machineries, whose fault detection and diagnosis (FDD) are essential for insuring reliability of the entire mechanical system. Therefore, extracting fault-related transient features from noisy signals to reveal bearing weak fault is a crucial prerequisite for long term condition monitoring. However, in complex working conditions, the measured acoustic signals are typically multi-component, submerged by strong interference noise and affected by unknown transmission path, resulting in the indistinctive fault-induced features and unsatisfactory diagnosis accuracy. To tackle this problem, a sparsity-oriented Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) method is proposed for bearing fault feature enhancement and diagnosis based on acoustic signals. As a non-iterative blind deconvolution approach, MOMEDA has been proven to be an effective tool to extract fault-related impulses from the noisy signal and compensate the complex unknown transmission path, enabling target propositioning and fault indication of bearings at an earlier termination condition. Furthermore, a sparsity operation which is originally designed for acoustic signal analysis, based upon the Laplace distribution characteristics of the fault-induced outliers, can further suppress the noise component and enhance the periodic fault impulses. The feasibility and effectiveness of the proposed sparsity-oriented MOMEDA method is validated by both simulated and experimental data. It is worth mentioning that bearing cage fault diagnosis and bearing mixed fault diagnosis, usually receiving limited attention before, are also investigated by the proposed method. The results demonstrate that the presented method gets preferable performance than existing methods, and it can achieve robust FDD of rolling bearings based on acoustic signals.

Compound Fault Diagnosis of a Wind Turbine Gearbox Based on MOMEDA and Parallel Parameter Optimized Resonant Sparse Decomposition.

Wind turbines usually operate in harsh environments. The gearbox, the key component of the transmission chain in wind turbines, can easily be affected by multiple factors during the operation process and develop compound faults. Different types of faults can occur, coupled with each other and staggered interference. Thus, a challenge is to extract the fault characteristics from the composite fault signal to improve the reliability and the accuracy of compound fault diagnosis. To address the above problems, we propose a compound fault diagnosis method for wind turbine gearboxes based on multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and parallel parameter optimized resonant sparse decomposition (RSSD). Firstly, the MOMEDA is applied to the preprocess, setting the deconvolution period with different fault frequency types to eliminate the interference of the transmission path and environmental noise, while decoupling and separating the different types of single faults. Then, the RSSD method with parallel parameter optimization is applied for decomposing the preprocessed signal to obtain the low resonance components, further suppressing the interference components and enhancing the periodic fault characteristics. Finally, envelope demodulation of the enhanced signal is applied to extract the fault features and identify the different fault types. The effectiveness of the proposed method was verified using the actual data from the wind turbine gearbox. In addition, a comparison with some existing methods demonstrates the superiority of this method for decoupling composite fault characteristics.

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 FCEEMD Energy Kurtosis Mean Filtering-Based Fault Feature Extraction Method

Aiming at the problem that fault feature extraction is susceptible to background noises and burrs, we proposed a new feature extraction method based on a new decomposition method and an effective intrinsic mode function (IMF) selection method. Firstly, pairs of white noises with opposite signs were introduced to neutralize the residual noises in ensemble empirical mode decomposition (EEMD) and suppress mode mixing. Both the reconstruction error (1.8445 × 10−17) and decomposition time (0.01 s) were greatly reduced through fast, complementary ensemble empirical mode decomposition (FCEEMD). Secondly, we integrated the energy and kurtosis of the IMF and proposed an effective IMF selection method based on energy kurtosis mean filtering, and the background noise of the signal was greatly suppressed. Finally, the periodic impacts were extracted from the IMF reconstruction signal by multipoint optimal minimum entropy deconvolution adjusted (MOMEDA). The fault frequencies were extracted from the periodic impacts through Hilbert demodulation, and the relative errors between the measured values and the theoretical values were all less than 0.05. The experimental results show that the proposed method can extract fault features more efficiently and provide a novel method for the fault diagnosis of rotating machinery.

Early fault diagnosis of rolling bearings based on parameter-adaptive multipoint optimal minimum entropy deconvolution adjusted and dynamic mode decomposition

Fault vibration signals of rolling bearings in early stages are affected by complex transmission paths and strong background noise, resulting in weak information about fault characteristics, which is difficult to extract clearly and accurately. To this end, a new diagnosis method for early faults of rolling bearings is proposed. First, the parameter-adaptive multipoint optimal minimum entropy deconvolution adjusted (PA-MOMEDA) algorithm is used to preprocess the fault signals by strengthening their shock components and weakening the influence of noise on their results. Second, the maximum envelope-spectrum characteristic energy ratio is employed as the selection criterion for the optimal truncation order of dynamic mode decomposition (DMD) to decompose and reconstruct the signals. Finally, the processed signals are subjected to the Hilbert envelope spectral transformation to accurately extract early fault characteristic frequencies. An analysis of simulated signals, public database signals, and bearing signals from a wind turbine has shown that the proposed PA-MOMEDA–DMD method can successfully extract the early fault characteristics of rolling bearings. Compared with the traditional pattern decomposition algorithms, the proposed method is much better at extracting fault characteristics and diagnosing early faults of rolling bearings. The facts have proved that the proposed method is promising in engineering applications.

Bearing Fault Diagnosis Based on an Enhanced Image Representation Method of Vibration Signal and Conditional Super Token Transformer.

Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is an advanced deconvolution method, which can effectively inhibit the interference of background noise and distinguish the fault period by calculating the multipoint kurtosis values. However, multipoint kurtosis (MKurt) could lead to misjudgment since it is sensitive to spurious noise spikes. Considering that L-kurtosis has good robustness with noise, this paper proposes a multipoint envelope L-kurtosis (MELkurt) method for establishing the temporal features. Then, an enhanced image representation method of vibration signals is proposed by employing the Gramian Angular Difference Field (GADF) method to convert the MELkurt series into images. Furthermore, to effectively learn and extract the features of GADF images, this paper develops a deep learning method named Conditional Super Token Transformer (CSTT) by incorporating the Super Token Transformer block, Super Token Mixer module, and Conditional Positional Encoding mechanism into Vision Transformer appropriately. Transfer learning is introduced to enhance the diagnostic accuracy and generalization capability of the designed CSTT. Consequently, a novel bearing fault diagnosis framework is established based on the presented enhanced image representation and CSTT. The proposed method is compared with Vision Transformer and some CNN-based models to verify the recognition effect by two experimental datasets. The results show that MELkurt significantly improves the fault feature enhancement ability with superior noise robustness to kurtosis, and the proposed CSTT achieves the highest diagnostic accuracy and stability.

An improved multipoint optimal minimum entropy deconvolution adjusted method for the diagnosis of rotating machinery under variable speed conditions

Deconvolution algorithms have been proved to be effective tools for fault feature extraction and diagnosis of rotating machinery. As a non-iterative algorithm, multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) shows its advantage in computation efficiency. However, MOMEDA focuses only on the extraction of impulses with precise periodicity while the fault feature excited under time-varying speed may not meet this requirement. To solve this problem, this paper proposes an improved method named as MOMEDAang to extend the application scenarios of MOMEDA under time-varying speed condition. Instead of using a periodic target vector, the new method calculates the time-domain intervals of fault impulses according to their nature of isometric distribution in angle domain, which can be free from the variation of speed. Moreover, the harmonic modulation intensity and average Gini index are further defined in this paper to highlight the inherent cyclic modulation order of filtered signal and conduct the health condition identification of monitoring equipment. The results of simulation and experimental cases reveal effectiveness and efficiency of the proposed method.