Weak Fault Feature Research Articles

The Fusiongram: a periodic weak fault feature extraction strategy and its application in bearing fault diagnosis

Abstract The weak periodic transient impact responses caused by localized defects in rolling bearings are often obscured by complex interferences, such as white noise, random transient impact responses, and periodic responses from system operations. Meanwhile, the fault feature information contributing to damage detection may be distributed across different frequency bands in the vibration signal. Therefore, under the influence of complex interference, it is a challenging problem regarding how to 1) accurately select the frequency bands containing rich fault feature information, 2) effectively extract damage information from different frequency bands, and thereby, 3) successfully achieve fault diagnosis for machinery condition monitoring. To address these issues, this research introduces a novel signal processing strategy, termed as Fusiongram, for extracting weak periodic fault features amidst the influence of complex interferences. Firstly, the method of complementary hierarchical decomposition is proposed, in which the signal is decomposed into multiple components with overlapping frequency contents. Then, an index with interference resistance is constructed to select the components carrying rich damage feature information. Finally, the adaptive threshold denoising and multicomponent normalized averaging techniques are employed to fuse the information from the squared envelope spectra of the selected components, thus obtaining the reconstructed squared envelope spectra for fault diagnosis. The Fusiongram is able to achieve the goal of weak fault feature extraction from signals with complex interference. The analysis results of numerical simulation and experimental testing verify the effectiveness and advantages of the proposed strategy.

Fault stands out in contrast: Zero-shot diagnosis method based on dual-level contrastive fusion network for control moment gyroscopes predictive maintenance

Control moment gyroscopes (CMGs) are the most common control actuators in spacecraft. Their predictive maintenance is crucial for on-orbit operations. However, due to the scarcity of CMG fault data, constructing a diagnosis system for predictive maintenance with CMGs poses significant challenges. Therefore, a zero-shot fault diagnosis method based on a dual-level contrastive learning fusion network was proposed. First, to address the difficulty in training CMG fault diagnosis models without fault data, a contrastive learning method based on CMG clusters was proposed to extract invariant features from healthy CMGs and achieve zero-shot diagnosis for predictive maintenance. Second, considering the limitations of information from a single sensor, a cross-sensor contrastive learning method was proposed to fuse features from different sensors. Third, to tackle the challenges of extracting weak potential fault features, a dual-level joint training method was introduced to enhance the model’s feature extraction capability. Finally, the proposed method was validated using real dataset collected from CMGs serviced on an in-orbit spacecraft. The results demonstrate that the method can achieve zero-shot fault diagnosis for control moment gyroscopes predictive maintenance. The code is available at https://github.com/IceLRiver/DCF.

An early fault warning and diagnosis model based on a two-step analysis strategy for wind turbine bearings

Early bearing fault diagnosis plays a crucial role in ensuring the reliability and safety of wind turbines. This process encounters two primary challenges, that is, weak fault characteristics and strong background noise. Consequently, they restrict the effectiveness of conventional diagnosis methods. To address these difficulties, a two-step early fault diagnosis model is proposed based on an anomaly monitoring index [Formula: see text] and improved successive variational mode decomposition-fast spectral correlation (ISVMD-FSC). Initially, [Formula: see text] enables early anomaly detection. Subsequently, the fault type is further identified using ISVMD-FSC. The ISVMD adaptive decomposition of early warning signals can effectively reduce the noise and isolate the fault impact characteristics. Then, the fault characteristic frequency is extracted using spectral coherence analysis to achieve the purpose of fault diagnosis. This model is tested and validated on simulated data, laboratory accelerated bearing life span data, and real high-speed bearing fault data of wind turbines. Notably, the comparative study shows that [Formula: see text] is capable of detecting the fault earlier than the spectral L2/L1 norm, the sum of the weighted normalized square envelope, index 8, and index 9. Additionally, the fault feature extraction effect of ISVMD-FSC is better than many more advanced time-frequency analysis methods. These results emphasize the model’s excellent performance in early bearing fault diagnosis and its good generalization ability.

A rotating machinery feature enhancement method based on improved symplectic geometry mode component sparsity

Gears and bearings are vital parts of rotating equipment. Weak fault feature enhancement is crucial to fault diagnosis. Therefore, a feature enhancement method based on the improved symplectic geometry mode component sparsity (ISGMCS) is proposed. Firstly, the improved symplectic geometry mode decomposition (ISGMD) method is proposed to reduce redundancy. ISGMD parameter optimization is based on Particle Swarm Optimization (PSO) and Kurtosis-to-Eigenvalue (KE). The max-kurtosis redundancy reduction component of ISGMD with less redundancy is used for sparse analysis. Subsequently, the sparse feature mining method is proposed to enhance the fault features of the redundancy reduction component. A sparse model optimization solution based on Forward-Backward Splitting (FBS) and the Mean Amplitude Ratio (MAR) metric is implemented. Finally, the effectiveness and superiority of ISGMCS are verified by simulated experiments, fault experiments, and comparison experiments. Meanwhile, the Mean Value of Amplitude (MVA) metric is designed to evaluate the effect of ISGMCS.

Research on multi-source sparse optimization method and its application on gearbox compound fault detection

In general, gearbox is prone to occur compound fault frequently because of its harsh working environment, its fault vibration signal often contains polymorphic-oscillatory components and is corrupted by heavy background noise, which brings great difficulty to diagnose fault. Sparse decomposition is often utilized to extract weak fault feature among heavy background noise. In order to solve the problems of traditional sparse decomposition method, such as lacking signal fidelity, causing local optimal solution by using the non-convex objective function, and presenting poor universality, a multi-source sparse optimization objective function with convexity is constructed based on the generalized mini-max concave penalty function. By using forward–backward splitting algorithm combination with Laplace wavelet dictionary, Morlet wavelet dictionary and DFT dictionary, the sparse coefficients corresponding to polymorphic-oscillatory components can be computed efficiently and each oscillatory component can be extracted accurately. Finally, simulation and experimental signal validate that the proposed method can decompose fault signal according to oscillatory property and diagnose gearbox compound fault without the prior knowledge of specific fault numbers.

Period enhanced feature mode decomposition and its application for bearing weak fault feature extraction

Abstract Decomposition methods which can separate the fault components into different modes have been widely applied in bearing fault diagnosis. However, early fault diagnosis is always a challenge for the signal processing methods as well as the traditional decomposition methods due to the heavy noise. Therefore, how to extract the weak fault information from the complicated signal with low SNR is of significance. To overcome this issue, a period-enhanced feature mode decomposition (PEFMD) method is proposed in this paper. Firstly, the initialized filters used for the mode decomposition are adaptively designed according to the spectrum of the original vibration signal. Secondly, time synchronized averaging is used in the iterative process to excavate and identify accurately the weak period components and determine the period of the iterative signal. Finally, the period information can promote the proposed method to decompose the fault component into the hopeful modes by setting correlation kurtosis as the optimation objective and the mode selection. Relative to FMD, the proposed PEFMD achieves further improvement in extracting weak fault information. The practicability and superiority of the proposed PEFMD are verified by the simulated and experimented data. Compared with the feature mode decomposition method and variational mode decomposition, the proposed decomposition method shows an obvious performance advantage under low SNR situations.

A two-step bearing fault diagnosis strategy under variable speed based on symplectic geometry modal decomposition and practical fault feature extraction framework

Abstract Strong noise interference can lead to failure of bearing fault diagnosis techniques. This paper proposes a two-step fault diagnosis strategy to address the challenge of weak fault feature extraction in bearing fault diagnosis using acoustic or vibration data at varying speed. Firstly, the paper introduces a short-time symplectic modal decomposition (stSGMD) method that utilizes fractional Fourier transform. This method involves signal processing with short-time windowing to extract fault-sensitive components. The window is then expanded to obtain the complete component through fractional Fourier narrow-band filtering based on energy concentration in the fractional Fourier domain. A novel entropy index, standard deviation discrete entropy, is introduced to quantify the intensity of fault shocks in non-stationary signal and is used to select components in the stSGMD. Subsequently, a fault feature extraction framework called global objective deconvolution (GOD) is presented for extracting instantaneous fault features at varying speed. This method establishes a global objective matrix for the extraction process. The GOD is utilized to deconvolute the complete fault-sensitive component, followed by envelope order analysis for demodulating the fault feature order. Numerical simulations and experimental studies on acoustics and vibration are performed. The results demonstrate that stSGMD improves the demodulation capability of SGMD, while GOD effectively extracts fault features. It is expected that the presented method will be effectively utilized for fault feature extractions in bearings operating under linear variable speed conditions.

An extraction method for early fault features of rolling bearings based on resonance sparse signal decomposition and non-convex second-order total variation denoising

A primary challenge in the field of fault diagnosis is extracting weak fault characteristics of bearings under large background noise and non-stationary conditions. Due to significant noise interference in the signals of rolling bearings, resonance sparse signal decomposition (RSSD) cannot efficiently extract the transient impact components in the early failure stage and the total variation denoising (TVD) method distorts signal waveforms. In this study, a combined extraction method for early fault features of rolling bearings based on RSSD and non-convex second-order total variation denoising (NCSOTVD) is proposed. Firstly, a non-convex function is introduced to define the regularisation term in the second-order TVD method, and the regularisation parameter and the convexity parameter in the NCSOTVD method are respectively screened using the noise standard deviation and the permutation entropy value to enhance the impact characteristics of signals and induce signal sparsity. The NCSOTVD model is solved using the optimisation-minimisation algorithm, so as to achieve noise reduction and feature enhancement of the vibration signals. Then, the low-resonance components of RSSD are denoised using the NCSOTVD method to highlight periodic pulse signals and extract the fault features of the rolling bearings. The simulation results and experimental data show that the method largely suppresses the noise interference, highlights the fault characteristics and reduces the problems of waveform distortion and poor sparsity of the TVD method in the denoising process.

Graph constrained empirical wavelet transform and its application in bearing fault diagnosis

The signal decomposition based on frequency domain distribution is a fundamental methodology for mechanical component fault diagnosis. However, existing methods face challenges such as susceptibility to noise interference and limited adaptability. Therefore, this paper proposes the graph constrained empirical wavelet transform (GCEWT) method. This method introduces structured information, such as the interrelationships among different parts of the frequency domain distribution of vibration signals, into the boundary detection process of empirical wavelet transform. The high-dimensional connectivity among different parts of the time-frequency distribution is utilized to construct an adjacency matrix. By constructing an adjacent graph, the proposed method encodes the adjacency relationships among frequency bands to constrain the low-dimensional spatial relationships between them. In conjunction with spectral clustering algorithms, the GCEWT method determines the boundaries for empirical wavelet transformation in the frequency domain. This approach achieves structured and adaptive decomposition of vibration signals from components of critical equipment, facilitating the structured and adaptive extraction of fault features. The effectiveness of the proposed method is validated using vibration data from both wind turbine drivetrain systems and aircraft engines. The experimental results demonstrate that the proposed method yields more reasonable signal decomposition results compared to traditional algorithms. Additionally, the proposed method proves to be more effective in extracting weak fault features of bearings in the presence of noise.

A novel adaptive blind deconvolution algorithm: application to feature extraction of weak faults in RV reducer gears

Abstract Aiming at the problem that the non-stationary and nonlinear weak fault signal of RV (rotate vector) reducers is hard to extract fault features due to the influence of noise and transmission paths, as well as the selection of parameters for maximum correlation kurtosis deconvolution (MCKD) relies heavily on manual experience, this article proposes a fault feature extraction method based on parameter adaptive MCKD for the gear faults of RV reducers. Firstly, the sparrow search algorithm combining sine-cosine and Cauchy mutation (SCSSA) is used to adaptively search for the input parameters of MCKD and obtain the signal after deconvolution with the optimal parameters. Secondly, the deconvoluted signal is subjected to ensemble empirical mode decomposition to obtain modal components on different frequency bands. Finally, calculate the multi-scale fuzzy entropy (MFE) of each component, constructing a MFE feature set vector, and input the feature vector into the support vector machine for fault classification and recognition. The experimental analysis and verification results both indicate that the proposed method can adaptively enhance the weak impact components in the gear signals of the RV reducer, effectively extracting weak fault features disturbed by noise. Compared with minimum entropy deconvolution, multipoint optimal minimum entropy deconvolution adjusted and MCKD, the proposed method has improved identification rate by 17.50%, 10.63% and 15.63%, respectively. In addition, in comparison to multiverse optimization and particle swarm optimizatio algorithms, the SCSSA exhibits superior performance when optimizing MCKD parameters, offering faster convergence speed, higher accuracy, and greater robustness.

Multi-rolling element faults diagnosis of rolling bearing based on time-frequency analysis and multi-curves extraction

Abstract In industrial production, rolling bearings are widely used as key mechanical components in all types of rotating machinery. Fault diagnosis is essential for predicting bearing damage in advance, avoiding sudden equipment downtime and reducing economic losses. However, rolling element fault diagnosis of rolling bearings continues to be a challenge, especially with multi-rolling element faults. In view of the characteristics of randomness, weakness, and coupling in the vibration signal generated by multi-rolling element faults in rolling bearings, a multi-rolling element fault detection method is proposed by combination time-frequency (TF) analysis (TFA) with multi-curves extraction methods. The pre-processing method combined autoregressive model with maximum correlated kurtosis deconvolution is employed to enhance the weak periodic fault impulses in the raw vibration signals of the rolling bearing. Then an improved dynamic path multi-curves extraction method is proposed to extract multiple TF curves from the TF spectrogram (TFS) constructed via short-time Fourier transform. According to the proposed classification criteria, the TF curves are classified as homologous faults. The TF masking (TFM) method is employed to keep TF information closely associated with the fault impulse. Finally, the fault signals are reconstructed sequentially based on the TFS processed by TFM, and precise identification of multi-rolling element faults is achieved by envelope analysis. Experimental results demonstrate the effectiveness of the proposed method in extracting the weak fault features of multi-rolling elements and accomplishing fault separation and diagnosis.

Rolling Bearing Fault Diagnosis Based on MResNet-LSTM

In response to the challenges of weak fault characteristics and complex and variable working conditions of roll-ing bearings in strong noise environments, a diagnostic methodology is proposed. This method integrates a multi-scale residual neural network (MResNet) and a long short-term memory (LSTM) neural network to achieve fault diagnosis of bearings under both constant and variable rotational speed working conditions. First, complete an ensemble empirical mode decomposition with adaptive noise (CEEMDAN) that is applied for vibration signal denoising. Secondly, a dropout layer is introduced between multi-scale residual blocks to prevent net-work overfitting, and the powerful time series information capture ability of LSTM is combined to improve di-agnostic accuracy. Finally, experimental verification is conducted using constant and variable speed bearing data sets. The results show that the proposed approach maintains strong diagnostic capabilities when the rotat-ing speed conditions change, and the signal is heavily polluted by noise.

Rotating machinery weak fault features enhancement via line-defect phononic crystal sensing

The fault features of rotating machinery at the early degradation stage are always weak as a result of interference from strong noise and irrelevant harmonics. Although traditional acoustic diagnosis techniques have attracted much attention with the merits of rich information and non-contact, extracting meaningful features related to faults in extremely low signal-to-noise ratios (SNRs) has always been a challenging problem. Considering the extraordinary physical properties of phononic crystals (PnCs) and acoustic metamaterials, this study proposes a structural enhancement method for rotating machinery fault diagnosis via line-defect PnC sensing. In contrast to conventional acoustic filtering and enhancement methods, this method can directly filter out noise and enhance fault features in the pre-processing stage of acoustic perception without the need for complex post-processing algorithms. Consequently, the original information of the fault features is preserved intact, thus further increasing the detection limit of current acoustic sensing. Specially, the designed line-defect PnC is parametrically tunable. Combined with the prior knowledge of rotating machinery fault features, such as gears and bearings, it is possible to design structures suitable for enhancing their fault features, which has great potential for practical engineering applications. The enhancement mechanism of line-defect PnC is theoretically described, and numerical simulations are also conducted to verify its ability to detect weak faults in rotating machinery. The experimental results show that by comparison with the variational modal decomposition (VMD)-based method, the proposed method exhibits superior fault feature enhancement performance under low SNR conditions. By systematically combining fault detection methods and acoustic metamaterial sensing, it can be foreseen that the proposed method shows great potential in mechanical equipment fault diagnosis.

Periodic group-sparse method via generalized minimax-concave penalty for machinery fault diagnosis

Abstract Diagnosing faults in large mechanical equipment poses challenges due to strong background noise interference, wherein extracting weak fault features with periodic group-sparse property is the most critical step for machinery intelligent maintenance. To address this problem, a periodic group-sparse method based on a generalized minimax-concave penalty function is proposed in this paper. This method uses periodic group sparse techniques to capture the periodic clustering trends of fault impact signals. To further enhance the sparsity of the results and preserve the high amplitude of the impact signals, non-convex optimization techniques are integrated. The overall convexity of the optimization problem is maintained through the introduction of a non-convex controllable parameter, and an appropriate optimization algorithm is derived. The effectiveness of this method has been demonstrated through experiments with simulated signals and mechanical fault signals.

Adaptive Low-Rank Tensor Estimation Model Based Multichannel Weak Fault Detection for Bearings.

Multichannel signals contain an abundance of fault characteristic information on equipment and show greater potential for weak fault characteristics extraction and early fault detection. However, how to effectively utilize the advantages of multichannel signals with their information richness while eliminating interference components caused by strong background noise and information redundancy to achieve accurate extraction of fault characteristics is still challenging for mechanical fault diagnosis based on multichannel signals. To address this issue, an effective weak fault detection framework for multichannel signals is proposed in this paper. Firstly, the advantages of a tensor on characterizing fault information were displayed, and the low-rank property of multichannel fault signals in a tensor domain is revealed through tensor singular value decomposition. Secondly, to tackle weak fault characteristics extraction from multichannel signals under strong background noise, an adaptive threshold function is introduced, and an adaptive low-rank tensor estimation model is constructed. Thirdly, to further improve the accurate estimation of weak fault characteristics from multichannel signals, a new sparsity metric-oriented parameter optimization strategy is provided for the adaptive low-rank tensor estimation model. Finally, an effective multichannel weak fault detection framework is formed for rolling bearings. Multichannel data from the repeatable simulation, the publicly available XJTU-SY whole lifetime datasets and an accelerated fatigue test of rolling bearings are used to validate the effectiveness and practicality of the proposed method. Excellent results are obtained in multichannel weak fault detection with strong background noise, especially for early fault detection.

Bearing fault detection technology for automated machinery based on acoustic analysis

Traditional fault detection methods in acoustic signal feature extraction of rolling bearings often make the signal denoising process complex due to low signal-to-noise ratio and weak fault features, making this method difficult to meet real-time requirements. Therefore, a fault detection model based on Fast-Renoriented SIFT feature extraction is proposed, which can quickly extract a large number of features from the original signal without the need for noise reduction processing and can effectively improve the efficiency and accuracy of fault detection. At the same time, to adapt to the fault detection of rolling bearings under multiple working conditions, this study also proposes an adaptive extended word bag model that combines local kurtosis and local 2-dimensional information entropy features, improving the adaptability and flexibility of the new model. It obtained a 100% overall recognition rate and a fault detection time of no more than 0.5 seconds in a 5-fold cross-validation experiment, verifying the excellent recognition accuracy, stability, and operational efficiency of the detection model. Its recognition accuracy in the multi-working condition rolling bearing fault detection experiment was above 97%, which was improved by about 21.25% compared to the traditional word bag model and had significant advantages in fault recognition accuracy and computational efficiency.

Improved DBO-VMD and optimized DBN-ELM based fault diagnosis for control valve

Control valves play a vital role in process production. In practical applications, control valves are prone to blockage and leakage faults. At the small control valve openings, the vibration signals exhibit the drawbacks of significant interference and weak fault characteristics, which causes subpar fault diagnosis performance. To address the issue, a diagnostic model based on optimized variational mode decomposition (VMD) and improved deep belief network-extreme learning machine (DBN-ELM) is proposed. Firstly, good point set population initialization, nonlinear convergence factor, and adaptive Gaussian–Cauchy mutation strategies are applied in the dung beetle optimization algorithm (DBO) to escape local optima. Then, the improved DBO (IDBO) is used to optimize VMD parameters to obtain a series of modal components. Next, the generalized dispersion entropy (GDE) is formed by the combination of generalized Gaussian distribution and refined composite multiscale fluctuation-based dispersion entropy. The maximum correlation coefficient modal components are applied to extract GDE. Finally, the IDBO is applied to optimize the parameters of the DBN-ELM network to improve the classification performance of control valve faults. The comparative experiment results demonstrate that the proposed model can extract effective features and the diagnostic accuracy reaches 99.87%.

Two-stage benefits of internal and external noise to enhance early fault detection of machinery by exciting fractional SR

For early mechanical fault diagnosis, stochastic resonance (SR) breaks the previous perception that “noise is useless” and uses internal noise or external noise to enhance weak fault characteristics. Moreover, the fractional-order derivative can reinforce noise-enhanced weak fault detection. However, in the face of extremely low signal-to-noise ratio conditions, the enhancement effect of fractional SR induced by a single excitation of internal noise or external noise is unsatisfactory. To solve the above drawbacks, this paper investigates two-stage benefits of both internal and external noise to enhance early fault detection of machinery by exciting parallel array of fractional SR, and using the signal-to-noise ratio (SNR) to adjust these parameters of fractional SR. Then, two experiments including rolling element bearings and gearboxes were performed to validate it. Experimental results show that the proposed method takes on obvious detection ability for low SNR signals, where the amplitude at the fault characteristic frequency is amplified by beyond 300 times in bearing and gearbox fault experiments than one-stage those. In addition, it is also found that as the noise intensity and the number of iterations increase, the amplitude at the fault characteristic frequency tends to the peak value and then falls into a saturation value. Finally, compared with empirical mode decomposition (EMD), the proposed method can amplify the amplitude at the fault characteristic frequency beyond 1000 times in bearing and gearbox fault experiments. It was concluded that the proposed method has obvious advantages in extracting weak fault characteristics of machinery submerged by strong background noise.

A synchronization-induced cross-modal contrastive learning strategy for fault diagnosis of electromechanical systems under semi-supervised learning with current signal

Electromechanical systems is widely employed in the manufacturing industry, with fault diagnosis being critical for ensuring the reliable operation of them. Vibration signals exhibit distinct fault features, but their acquisition is subject to various limitations. Conversely, current signals, while easily measurable, typically manifest weak fault features. Therefore, selecting a signal for fault diagnosis necessitates a trade-off among cost, performance, and feasibility. To overpass these obstacles and enable flexible fault diagnosis in complex environments, this paper presents a novel contrastive vibration-current (CVC) framework that leverages synchronization information from multiple modalities to enhance the performance of single-modality models, primarily the current model. This allows the choice of any signal for monitoring during the deployment phase. Specifically, we first preprocess current signals using Clarke transformation to highlight their fault information. Subsequently, the vibration model employs semi-supervised learning to make full use of labeled and unlabeled samples. Additionally, a noise-resistant augment Mean Teacher is incorporated to enhance the robustness of the vibration model. Then, using synchronization-induced cross-modal contrastive learning (SICMCL), vibration and current features are aligned. And at the decision level, the current model leverages pseudo-labels derived from vibration. The results of experiments demonstrate that CVC excels when relying solely on single-modal signals, owing to the effectiveness of SICMCL. Moreover, for the current model, SICMCL is more beneficial in improving performance than true labels.

Accuracy-Improved Fault Diagnosis Method for Rolling Bearing Based on Enhanced ESGMD-CC and BA-ELM Model

The current methods for early fault diagnosis of rolling bearing have some flaws, such as poor fault feature information and insufficient fault feature extraction capability, which makes it challenging to guarantee fault diagnosis accuracy. In order to increase the accuracy of fault diagnosis, it proposes a new fault diagnosis method based on enhanced Symplectic geometry mode decomposition with cosine difference factor and calculus operator (ESGMD-CC) and bat algorithm (BA) optimized extreme learning machine (ELM). The vibration signal is first decomposed into a number of Symplectic geometry components (SGCs) by SGMD. The number of iterations is reduced by the cosine difference factor, which also successfully separates the noise components from the effective components. The calculus operator is adopted to strengthen the weak fault features, making it simple to extract. The fault feature vectors are calculated by the power spectrum entropy-weighted singular values. Finally, the ELM model optimized by BA iteratively is performed as the final classifier for fault classification. The simulation and experiments demonstrate that the proposed method has a better degree of fault diagnostic accuracy and is effective at extracting the rich fault information from vibration signals.