Incipient Fault Feature Research Articles

Adaptive multiscale wavelet-guided periodic sparse representation for bearing incipient fault feature extraction

Adaptive multiscale wavelet-guided periodic sparse representation for bearing incipient fault feature extraction

Energy bubble entropy guided symplectic geometry mode decomposition for rotating machinery incipient fault feature extraction

Abstract Extracting incipient fault features is a critical aspect of monitoring the rotating machinery operation condition. However, existing methods based on symplectic geometry mode decomposition (SGMD) suffer from limited parameter adaptability and noise robustness. Therefore, this paper proposes an Energy bubble entropy guided SGMD method to extract incipient fault feature. Firstly, the SGMD method is employed to initially separate fault characteristic components from noisy signal. Furthermore, the energy bubble entropy(EbEn) is constructed to evaluate the attributes of incipient feature within the signal, which requires almost no parameter setting with good robustness and computational efficiency. Thirdly, the Empirical Bayes shrinkage method effectively mitigates irrelevant noise and enhances the significance of incipient fault feature. Simulated and experimental signals are employed to substantiate the efficacy of the EbEn guided SGMD method. The comparison analysis with relevant methods exhibits that this method has greater robustness and adaptivity.

Adaptive fast nonlinear blind deconvolution based on nonuniform particle swarm optimization for the rolling bearing fault diagnosis

Although superior fault extraction performance is possessed by fast nonlinear blind deconvolution (FNBD) through the introduction of penalty terms and nonlinear features, its wide application is limited by the complexity and diversity of parameter selection. The fault extraction performance of FNBD is impacted by the contingency of parameter selection. To reduce the prior experience dependence of FNBD and improve the performance of fault extraction, an end-to-end adaptive fast nonlinear blind deconvolution (AFNBD) is proposed. First, the proposed nonuniform particle swarm optimization (NPSO) is employed to assign parameters to be optimized for each particle to construct its Hankel matrix, nonlinear features and objective functions. Secondly, Gaussian fitting is applied to modify the filters. Then, NPSO is utilized to update relevant parameters and the corresponding filter of each particle to explore the optimum filter. Finally, simulations and experiments verify the effectiveness of AFNBD and the results indicate that AFNBD is a powerful tool for incipient fault feature extraction.

Helicopter Planet Gear Rim Crack Diagnosis and Trending Using Cepstrum Editing Enhanced with Deconvolution.

Detecting gear rim fatigue cracks using vibration signal analysis is often a challenging task, which typically requires a series of signal processing steps to detect and enhance fault features. This task becomes even harder in helicopter planetary gearboxes due to the complex interactions between different gear sets and the presence of vibration from sources other than the planetary gear set. In this paper, we propose an effectual processing algorithm to isolate and enhance rim crack features and to trend crack growth in planet gears. The algorithm is based on using cepstrum editing (or liftering) of the hunting-tooth synchronous averaged signals (angular domain) to extract harmonics and sidebands of the planet gears and low-pass filtering and minimum entropy deconvolution (MED) to enhance extracted fault features. The algorithm has been successfully applied to a vibration dataset collected from a planet gear rim crack propagation test undertaken in the Helicopter Transmission Test Facility (HTTF) at DSTG Melbourne. In this test, a seeded notch generated by an electric discharge machine (EDM) was used to initiate a fatigue crack that propagated through the gear rim body over 94 load cycles. The proposed algorithm demonstrated a successful isolation of incipient fault features and provided a reliable trending capability to monitor crack progression. Results of a comparative analysis showed that the proposed algorithm outperformed the traditional signal processing approach.

Adversarial domain adaptation network with MixMatch for incipient fault diagnosis of PMSM under multiple working conditions

The incipient faults diagnosis under multiple working conditions has been a challenging problem due to the small difference of incipient fault features, and different feature divergences and distributions under different working conditions. In this paper, an Adversarial Domain Adaptation Network with MixMatch (ADANM) is proposed for the incipient faults diagnosis of permanent magnet synchronous motor (PMSM) under multiple working conditions. In ADANM, through selecting multi-scale convolutional kernels, an Adaptive Multi-scale Convolutional Neural Network (AM-CNN) is designed to improve the adaptability of extracted features for the incipient faults of PMSM under different working conditions. Meanwhile, the MixMatch and confidence-thresholding embedded in the adversarial network are employed to reduce the conditional distribution discrepancy by reducing the number of false pseudo-labels. The proposed ADANM is evaluated by diagnosing 4 types of incipient faults in PMSM under multiple working conditions. Extensive experiments are conducted on the hardware-in-the-loop (HIL) real-time platform, and our method outperforms existing methods by an average accuracy increase of approximately 8.12% across three different datasets. This significant improvement validates the effectiveness and superiority of the proposed method.

Incipient fault detection enhancement based on spatial-temporal multi-mode siamese feature contrast learning for industrial dynamic process

Incipient faults are characterized by low-amplitude, unclear fault features, which are susceptible to unknown disturbances, leading to unsatisfactory detection performance. In this paper, an incipient fault detection enhancement method based on siamese spatial-temporal multi-mode feature contrast learning method is proposed. Firstly, we design a novel siamese spatial-temporal multi-mode convolutional neural network model consisting of two weight-shared spatial-temporal multi-mode convolutional neural networks and one feature discrimination measure operator, which are then used to extract the spatial-temporal multi-mode features of two datasets and to measure the distance between them. Then, an incipient fault feature discrimination intensification training strategy is developed to enhance the incipient fault detection performance. Specifically, this strategy intends to maximize the feature distance between the normal data and the incipient fault data, as well as that between different incipient faults, while minimizing the feature distance between the normal data and between the same incipient faults. Moreover, due to the long-term slow change characteristic of the incipient fault, the multi-head self-attention Long Short-Term Memory is presented as a dynamic feature learning model to further lopsidedly learn the incipient fault temporal long-term dependency according to attention weights utilizing the scaled dot-product multi-head self-attention mechanism. Finally, the performance of the proposed method is demonstrated on two industrial cases.

Incipient fault feature extraction of rolling element bearings based on SOSO boosting technique and improved energy operator

The incipient fault features of rolling element bearings (REBs) are easily overwhelmed by environmental noise and vibration interference. Therefore, this paper proposes a novel fault feature extraction method for REBs based on a SOSO (Strengthen-Operate denoising-Subtract-Strengthen) boosting technique. Firstly, an improved fast non-local mean filtering (IFNLM) algorithm is proposed by improving the similarity measure and kernel function while reducing the amount of weight calculation based on distance symmetry. Secondly, a SOSO_IFNLM boosting filtering structure is constructed to reduce the noise of the original vibration signal and enhance the early faint fault pulse. Finally, a k-value improved symmetric higher-order frequency-weighted energy operator (k-SHFWEO) is proposed to detect the bearing fault features from denoised signals. The effectiveness and feasibility of the proposed SOSO_IFNLM-k-SHFWEO method are numerically and experimentally investigated. The results demonstrate that the proposed method has better fault feature extraction capability for early weak faults of REBs and higher efficiency compared to other popular methods.

Incipient Fault Feature Extraction of Rolling Bearing Based on Signal Reconstruction

In the incipient fault vibration signals of rolling bearings, weak fault features are easily submerged in strong background noise and difficult to be extracted. The sparse decomposition method can perform well in the extraction of weak fault features, but the low signal-to-noise ratio (SNR) would cause excessive decomposition. To enhance the fault features and maintain the time–frequency structure of fault impulses, a novel incipient fault feature extraction of rolling bearing based on signal reconstruction is proposed. Firstly, the Teager energy operator (TEO) is used to obtain the envelope of the impulse components in the vibration signal, which is also sensitive to noise and would be seriously affected by strong noise. Secondly, a Savitzky–Golay (S–Golay) filter based on the particle swarm optimization (PSO) algorithm is adopted to suppress the noise in the TEO envelope and generate a smooth envelope signal. Finally, the fault signal is reconstructed by the multiplication of the filtered TEO envelope signal and the original signal. The reconstructed signal can maintain the structural characteristics of the original fault impact signal and can provide reliable feature enhancement signals for further sparse decomposition, multi-source vibration separation, and other operations. Simulation signals and experiments verify the effectiveness of this method in extracting early fault features under low SNRs.

An enhanced cyclostationary method and its application on the incipient fault diagnosis of induction motors

The cyclostationary analysis techniques have been extensively explored for the purpose of fault detection in rotating machinery. However, there are still huge challenges because of both limited detection frequency range and low fault identification accuracy. This paper proposes an improved cyclostationary method to enhance incipient fault features. Firstly, the continuous wavelet transform is used to accurately locate important frequency bands, and the fault modulation mechanism or fast kurtogram can be adopted to design the optimal wavelet transform scale factor. Secondly, the Teager-Kaiser energy operator is improved to be used in the frequency domain for the weak fault feature enhancement. Finally, fault features are presented in the cyclic frequency domain through spectral coherence and enhanced envelope spectrum. The proposed method is verified through both numerical simulation and experiments, including incipient half-broken rotor bar, and rolling bearing outer race faults in induction motors.

Rolling bearing incipient fault feature extraction using impulse-enhanced sparse time-frequency representation

Incipient faults features are often extremely weak and susceptible to heavy noise, making it challenging to obtain the concentrated faulty energy ridges in the time-frequency domain. Thus, a novel impulse-enhanced sparse time-frequency representation (IESTFR) method is proposed in this paper. First, the time-rearranged multisynchrosqueezing transform is utilized to produce a time-frequency representation (TFR) with a high energy concentration for faulty impulses. Next, a new non-convex penalty function is constructed by the hyperbolic tangent function, which can enhance the periodic impulsivity of sparse TFR for more obvious fault characteristic frequency. Moreover, the time-frequency transform is evaluated and compared by simulated signals and a selection strategy for the regularization parameter is designed. Simulated signals and two experimental signals are applied to verify the effectiveness of IESTFR, and the results show that IESTFR is effective and superior in bearing incipient fault feature extraction.

Incipient fault detection of planetary gearbox under steady and varying condition

As an important component in rotating machines, gearbox failure will lead to costly economic losses. Generally, incipient fault features of gearbox are weak and concealed in a set of time-varying vibration signals that are challenging to identify effectively. Based on that, a new method is proposed for incipient fault detection(FD) under steady and variable conditions of gearboxes based on improved octave convolution in this paper. First, the vibration signals are transferred into images via the symmetrized dot pattern(SDP) method. Then, the proposed method enhances image detail learning by adding convolution kernels and introducing residual connections in the high-frequency component of the octave convolution. Meanwhile, self-attention units are introduced in the information interaction branches. After that, the improved octave convolution is applied to the ResNet50 backbone network(IOC-ResNet50) to mine deep fault features. Compared with other published methods, the results indicate that the proposed performs superiorly in both time-varying conditions and steady state.

A Refined Multiscale Symbolic Diverse Entropy Technique for Detecting Weak Faults in Rotating Machinery

In rotating machinery components, it is difficult to extract incipient weak fault features directly from vibration signals. A fault feature extraction method based on refined multiscale symbolic dispersion entropy (RMSDE) is proposed. This method mainly combines symbolic coding and dynamic complexity evaluation to achieve multi-dimensional feature extraction. By constructing multiple sets of simulation signals to evaluate the correlation performance of the proposed method, the superior performance in complexity estimation, anti-noise interference and computational efficiency is mainly demonstrated. When RMSDE is combined with a self-organizing fuzzy logic classifier (SOF), the results show that the proposed RMSDE-SOF method can achieve high-precision fault diagnosis of rotating machinery. The effectiveness and robustness of the proposed method in fault feature extraction and diagnosis are verified by using the rolling bearing dataset and gear dataset, which contain multiple fault types, and the comprehensive performance of the proposed method is better than that of similar feature extraction methods. The final results show that the proposed method is superior to the other methods in rotating machinery fault diagnosis.

Mechanical Incipient Fault Detection and Performance Analysis Using Adaptive Teager-VMD Method

For large rotating machinery with low speed and heavy load, the incipient fault characteristics of rolling bearings are particularly weak, making it difficult to identify them effectively by direct signal processing methods. To resolve this issue, we propose a novel approach to detecting incipient fault features that combines signal energy enhancement and signal decomposition. First, the structure of a conventional Teager algorithm is modified to further increase the energy of the micro-impact component and hence the impact amplitude. Then, a kind of composite chaotic mapping is constructed to extend the original fruit fly optimization algorithm (FOA) framework, improving the FOA’s randomness and search power. The effective intrinsic mode functions (IMFs) are determined by searching for the optimal combination values of the key parameters of the variational mode decomposition (VMD) with the improved chaotic FOA (ICFOA). The kurtosis index is then used to select the IMFs that are most relevant to the fault characteristics information. Finally, the sensitive components are analyzed to identify multiple early fault characteristics and determine detailed information about the faults. Moreover, the approach is evaluated by a simulation signal and a measured signal. The comprehensive evaluation indicates that the approach has clear advantages over other excellent methods in extracting the incipient fault feature information of the equipment and has great potential for application in engineering.

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

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

Optimal periodicity-enhanced group sparse for bearing incipient fault feature extraction

Efficient and automatic fault feature extraction of rotating machinery, especially for incipient faults is a challenging task of great significance. In this article, an optimal periodicity-enhanced group sparse method is proposed. Firstly, a period sequence determination method without any prior information is proposed, and the amplitude is calculated by the numerical characteristics of the vibration signal to obtain period square waves. Secondly, the periodic square waves are embedded into the group sparse algorithm, to eliminate the influence of random impulses, and intensify the periodicity of the acquisition signal. Thirdly, a fault feature indicator reflecting both signal periodicity and sparsity within and across groups is proposed as the fitness of the marine predator algorithm for parameter automatic selection. In addition, the method proposed is evaluated and compared by simulation and experiment. The results show that it can effectively extract incipient fault features and outperforms traditional overlapping group shrinkage and Fast Kurtogram.

Incipient detection of bearing fault using impulse feature enhanced weighted sparse representation

The bearing fault impact impulses induced by the contact between components with drawback, is difficult to be detected at sprouting stage due to the interference of background noise, harmonics, random shocks, etc. In this paper, an impulse feature enhanced weighted sparse representation (IFEWSR) algorithm is proposed to accurately detect the weak bearing fault impact feature from incipient stage condition monitoring (CM) signal. Firstly, a modified fault period estimation method is presented to improve the robustness and reduce the computational complexity of recently proposed algorithms. Secondly, a novel weighting strategy on wavelet coefficients, indicated by the period-assisted corelated kurtosis of envelope spectrum (CKSES), is presented to denote the contribution of subband signals for sparse representation calculation framework. In addition, the mean normalized energy-weight deviation (MNEWD) rule is proposed to evaluate the performance of the weighting algorithm on subband signals which is blank at present. Thirdly, a novel fault feature enhancement technique is developed to better capture the bearing fault feature information. The effectiveness and superiority of the proposed method are proved by simulation and experiments. Results show that the proposed IFEWSR method provides higher accuracy for incipient fault feature extraction and outperforms other state-of-the-art methods.

Extraction of incipient fault features of rolling bearings based on CWSSMD and 1.5D-EDEO demodulation

The fault feature of a rolling bearing is weak in the incipient fault stage, with severe environmental noise interference, which makes it difficult to extract the fault feature information from the vibration signal. In this paper, an adaptive method based on component-weighted symplectic singular mode decomposition and 1.5-dimensional envelope derivative energy operator (1.5D-EDEO) demodulation is proposed to extract the incipient fault features of a bearing and it does not require manual parameter setting. The method begins with the original vibration signal decomposed by symplectic singular mode decomposition to obtain multiple initial symplectic singular components (ISSCs). Then, the fault information amount of the ISSCs is measured by fault impulse sparsity (FIS) constructed by the Gini index of the square envelope which has a powerful sparsity measurement capability. After this, the ISSCs are reconstructed based on the weights obtained from the FIS to obtain the final denoised symplectic singular component (DSSC). Finally, the DSSC is demodulated by 1.5D-EDEO to further highlight the fault features of the bearing and reduce noise interference. The effectiveness of the proposed method is verified through simulation and experimental analysis. The experimental results show that the proposed method is more effective in enhancing incipient bearing fault features compared to other bearing fault diagnosis methods.

Detection of an Incipient Fault for Dual Three-Phase PMSMs Using a Modified Autoencoder

For the detection of incipient interturn short-circuit (IITSC) faults of machines without shutting them down, there are still shortcomings of insufficient incipient fault features and a high false alarm rate. This is especially the case for dual three-phase permanent magnet synchronous motors (PMSMs) with complex winding structures, and this kind of incipient fault detection is more complicated. To solve this detection difficulty, an IITSC detection method for dual three-phase PMSMs is proposed based on a modified deep autoencoder (MDAE). This autoencoder (AE) adopts an improved distribution metric combined with the maximum mean discrepancy (MMD) and the maximum covariance discrepancy (MCD) to extract the fault feature from the common features, which can improve the feature difference between the normal state and the incipient fault state. Then, the permutation entropy of the extracted features is calculated to detect the IITSC faults. The results illustrate that this method can not only detect IITSC faults online effectively and robustly, but also reduce the false alarm rate of the fault detection for dual three-phase PMSMs.

Integrated early fault diagnosis method based on direct fast iterative filtering decomposition and effective weighted sparseness kurtosis to rolling bearings

The fault vibration signal of rolling bearing is typical non-stationary and non-linear signals. It usually exhibits the characteristics of amplitude modulation and frequency modulation (AM-FM) due to the interaction between the various components within the bearing and modulation of the rotor speed. In addition, the incipient weak fault feature information is inevitably masked by the strong background noise and other vibration interference because of the influence of the working environment of mechanical equipment. Therefore, new high efficiency and effective processing methods are indispensable for feature extraction of bearing fault diagnosis signals. Direct fast iterative filtering (dFIF) is a new kind of adaptive mode decomposition method proposed by Antonio Cicone based on iterative filtering (IF) and fast iterative filtering (FIF). dFIF is used to quickly decompose multi-component signals into a set of intrinsic mode functions (IMFs) by means of fast Fourier transform (FFT). In order to study the decomposition performance and examine the feature extraction ability of the dFIF method, a group of harmonic signals with simple linear superposition, multi-components AM-FM signals with different data length and different signal-to-noise ratio (SNR) are simulated and analyzed. For comparison, other mode decomposition methods were also applied to the simulated signals, such as adaptive local iterative filtering (ALIF), local mean decomposition (LMD), and empirical mode decomposition (EMD). The results show that dFIF decomposition methods have faster decomposition speed and better accuracy than other methods. Furthermore, the results also demonstrate excellent performance in AM-FM feature extraction and anti-mode mixing. On the other hand, the effective weighted sparseness kurtosis (EWSK) index integrates the periodicity and intensity of impact in each mode, which can effectively identify the effective mode. Therefore, considering the above merits, EWSK is integrated on the dFIF to effectively accomplish the fault diagnosis for rolling bearing under strong background noise. Firstly, the raw vibration signals of rolling bearing fault are decomposed into a set of IMFs by using dFIF. The EWSK indicator is then utilized to select IMFs with rich bearing fault impulse information for reconstruction. Finally, the Hilbert envelope demodulation analysis is used to analyze the reconstructed signal, extract the bearing fault feature and then judge the fault type. This proposed method is applied to analyze the simulated and field measured vibration signals of rolling bearing faults. Simultaneously, the other above-mentioned adaptive TFA methods are also adopted to analyze the signals. The results compared show that the proposed method has excellent performance in the aspects of decomposition speed and accuracy under the influence of strong background noise. It demonstrates the effectiveness and applicability of the proposed method for signal processing and fault pattern recognition of rolling bearings.

Bearing incipient fault feature extraction using adaptive period matching enhanced sparse representation

Bearing incipient fault feature extraction is crucial and challenging throughout its life cycle. In this paper, an adaptive period matching enhanced sparse representation (APMESR) algorithm is developed to address this issue. First, a novel methodology for estimating the period of faulty impulses is proposed from the perspective of mining the periodicity-related numerical patterns. Second, the period estimation methodology is embedded in a sparse representation model to implement adaptive period matching to form APMESR, which is capable of achieving periodic sparsity. Third, maximal overlap discrete wavelet packet transform is selected as the linear transformation of APMESR for improving its ability to reduce noise and highlight periodic impulse signatures. Furthermore, evaluations and comparisons are conducted using simulations to demonstrate the validity and performance of the proposed period estimation methodology, linear transformation, and APMESR. Experimental results indicate that APMESR can effectively extract incipient bearing fault features and outperforms other well-advanced methods.