Adaptive Local Iterative Filtering Research Articles

Transformer partial discharge fault diagnosis based on improved adaptive local iterative filtering‐bidirectional long short‐term memory

AbstractInsulation deterioration, which is mainly caused by partial discharge (PD) occurring inside power transformers, is one of the prime reasons to cause transformer faults. Therefore, an effective diagnosis of PD is crucial to ensure the safe and stable operation of transformers. To extract more effective features that characterise transformers PD signals and enhance the recognition accuracy, a novel transformer PD fault diagnosis model based on improved adaptive local iterative filtering (ALIF) and bidirectional long short‐term memory (BILSTM) neural network is proposed. Addressing the issue of predetermined decomposition levels and accuracy in ALIF decomposition, the golden jackal optimisation (GJO) algorithm is introduced to optimise the parameters. The proposed fault diagnostic model extracts dominant PD features employing the improved ALIF and Refined Composite Multi‐Scale Dispersion Entropy and improves the diagnostic accuracy with the optimised BILSTM by introducing GJO. Experimental data evaluates the performance of support vector machine, long short‐term memory and BILSTM. The results verify the effectiveness and superiority of the proposed model.

Improved ALIF and its application to rolling bearing fault diagnosis

Adaptive Local Iterative Filtering (ALIF) is a commonly used signal processing method. However, when applied to rolling bearing fault diagnosis, ALIF has been found to have problems such as abnormal interruption, serious mode aliasing, and inconvenient parameter setting. To address these issues, this study proposed an improved Adaptive Local Iterative Filtering (IALIF) method. Based on another assumption, IALIF not only effectively guarantees the stability of the decomposition and the reliability of the results by adaptively dividing the distance and de-oscillation by extreme envelope form, but also provides more convenient parameter setting and effectively suppresses the problem of modal aliasing. In addition, IALIF adds a stop criterion, which greatly improves the efficiency of the algorithm. Finally, the analysis results of simulation signals and experimental signals show that IALIF has good decomposition performance and is practical for rolling bearing fault diagnosis.

Envelope extraction algorithm for magnetic resonance sounding signals based on adaptive local iterative filtering

Magnetic resonance sounding (MRS) is a geophysical method that can determine groundwater content directly and quantitatively. However, as MRS uses the Earth’s magnetic field as the background field, MRS signals are weak and cannot be shielded. Reliably extracting MRS signals in a strong noise environment is difficult. In this study, a data processing scheme using the adaptive local iterative filtering (ALIF) algorithm is proposed to extract MRS signal envelopes accurately. Based on the uncertainty of the initial amplitude and relaxation time, the decomposition order and mask coefficient of the ALIF algorithm are selected via traversal. Simulation results show that in the case of Gaussian noise and power frequency harmonic noise, the ALIF algorithm can reliably extract the MRS signal envelopes, and the correlation coefficient between the extracted and noiseless envelopes is 0.97. Under various noise types, amplitudes, and relaxation times, the average SNR increases by 30 dB∼42 dB. The ALIF algorithm is also suitable for extracting multi-exponential MRS signal envelopes. A comparative analysis between harmonic modeling cancellation and ensemble empirical mode decomposition shows the superiority of the ALIF algorithm, and the processing of the field data further verifies the effectiveness and practicability of the algorithm.

On ALIF-WT-GDA-based Terahertz spectral method for identification of transgenic cotton seeds

Terahertz (THz) time-domain spectroscopy can interact with organic molecules to produce “resonance” absorption, and it is suitable for qualitative and quantitative analysis on the basis of spectral absorption mechanism. However, there are some problems with it, such as un-obviousness signal peaks and positions, signal oscillations and low recognition accuracy. Therefore, this paper has proposed a method named ALIF-WT-GDA-based THz spectrum for identifying transgenic cotton seeds, which can strengthen and accurately identify features of the THz spectral data. First, this paper has put forward a pretreatment method of THz spectral data based on Adaptive Local Iterative Filtering (ALIF) and Wavelet Transform (WT). Specifically speaking, ALIF is used to decompose THz time-domain spectral signals and extract useful mode components for signal reconstruction in order to improve the signal-to-noise ratio (SNR). Also, WT is employed to perform smoothing filtering of THz signals, which makes THz absorbance curves much smoother and absorption peak positions much more obvious. Then, thanks to the advantages of kernel methods, Generalized Discriminant Analysis (GDA) method of Kernel Fisher Discriminant Analysis (KFDA) is used for dimension reduction and clustering analysis. Finally, the proposed ALIF-WT-GDA method is adopted for pretreatment and clustering of the THz spectral data from four different types of transgenic cotton seeds, with its identification accuracy of 98.33%. The above result has proved that the proposed ALIF-WT-GDA method is effective.

Application of adaptive local iterative filtering in axis trace purification of turbine generator rotor

Application of adaptive local iterative filtering in axis trace purification of turbine generator rotor

Conjectures on spectral properties of ALIF algorithm

A new decomposition method for nonstationary signals, named Adaptive Local Iterative Filtering (ALIF), has been recently proposed in the literature. Given its similarity with the Empirical Mode Decomposition (EMD) and its more rigorous mathematical structure, which makes feasible to study its convergence compared to EMD, ALIF has really good potentiality to become a reference method in the analysis of signals containing strong nonstationary components, like chirps, multipaths and whistles, in many applications, like Physics, Engineering, Medicine and Finance, to name a few.In [9], the authors analyzed the spectral properties of the matrices produced by the ALIF method, in order to study its stability. Various results are achieved in that work through the use of Generalized Locally Toeplitz (GLT) sequences theory, a powerful tool originally designed to extract information on the asymptotic behavior of the spectra for PDE discretization matrices. In this manuscript we focus on answering some of the open questions contained in [9], and in doing so, we also develop new theory and results for the GLT sequences.

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.

A Hybrid Method to Diagnose 3D Rotor Eccentricity Faults in Synchronous Generators Based on ALIF_PE and KFCM

This paper proposed a new hybrid diagnosis method for the generator’s 3D static eccentricity faults which include the axial eccentricity, the radial eccentricity, and the mixed eccentricity composed of the former two. Firstly, adaptive local iterative filtering (ALIF) method was used to decompose the vibration signals of the generator under eccentricity faults. Then, in order to figure out the intrinsic mode function (IMF) components with the upmost feature information, the correlation coefficient was calculated. Finally, the components’ permutation entropy (PE) is extracted to construct the eigenvector matrix which can be used to input the kernel fuzzy C-means (KFCM) algorithm to obtain the result of clustering. The result indicates that the classification coefficient based on ALIF and KFCM behaves closer to 1, while the average fuzzy entropy (FE) is closer to 0, showing that this method is able to detect different eccentricity faults more accurately.

Time–frequency analysis via complementary ensemble adaptive local iterative filtering and enhanced maximum correlation kurtosis deconvolution for wind turbine fault diagnosis

A complementary ensemble adaptive local iterative filtering (CEALIF) and enhanced maximum correlation kurtosis deconvolution (EMCKD) approach is proposed for weak fault signals in wind turbine bearings, which are easily concealed by strong background noise and susceptible to intermittent interference. The adaptive local iterative filtering (ALIF), as a novel nonstationary signal processing technique, can perform adaptive filtering based on the signal itself characteristics. However, its mode mixing is an annoying problem. To relieve this problem, the noise-assisted CEALIF-based filtering is proposed. Nonetheless, the ambient noise present in the original signal is retained in the component of interest. Maximum correlation kurtosis deconvolution (MCKD) is an effective tool for enhancing periodic pulses. However, its deconvolution parameters need to be set manually, and are more demanding when the bearing failure is weak. To address this circumstance, this paper introduces the particle swarm algorithm (PSO) to solve the optimal deconvolution parameters and proposes EMCKD. Firstly, by employing CEALIF, the original signal is adaptively filtered into a sequence of IMFs. Then, the IMF that best characterizes the fault information is selected based on the weighted kurtosis index (WKI). Finally, the shock components of the selected IMF are enhanced to extract the periodic shock based on EMCKD. The proposed approach can accurately extract fault characteristics by analyzing the whole life cycle signals of bearings and fault signals of a 1.5 MW direct-drive wind turbine within strong background noise. Further, the proposed approach is implemented for the compound fault extraction of bearings, and the compound fault information of the inner race as well as the outer race of the bearings is successfully extracted.

Research on Noise Reduction Method of Pressure Pulsation Signal of Draft Tube of Hydropower Unit Based on ALIF-SVD

Aiming at the problem that it is difficult to extract the characteristics of the draft tube pressure fluctuation signal under the background of strong noise, this study proposes a dual noise reduction method based on adaptive local iterative filtering (ALIF) and singular value decomposition (SVD). First, perform ALIF decomposition of the signal to be decomposed to obtain a series of IMF components, calculate the sample entropy of each component, select some IMF components to reconstruct according to the set sample entropy threshold, and then perform SVD decomposition on the reconstructed signal, and according to the location of the singular value difference spectrum mutation point, the appropriate number of reconstructions is selected for reconstruction, so as to achieve the double noise reduction effect. The ALIF-SVD dual noise reduction method proposed in this study is compared with the single ALIF, EMD, and EMD-SVD dual noise reduction method through simulation, and the correlation coefficient, signal-to-noise ratio, and mean square error are used to evaluate the noise reduction. It is found that the ALIF-SVD dual noise reduction method avoids the phenomenon of modal aliasing in the decomposition process, effectively removes the noise, and can retain the useful information of the original signal, and the noise reduction effect is better. A unit of a hydropower station in China is further selected as the research object, and its draft tube pressure fluctuation data were analyzed for noise reduction. It was found that this method can accurately extract the signal characteristics under strong noise background, so as to determine the type of pressure fluctuation of the unit, which is helpful to improve the fault recognition rate of hydraulic turbines. And it provides some technical support for the safe and stable operation of hydropower units and the promotion of condition-based maintenance strategy and improves the intelligent level of hydropower station operation management.

A Rolling Bearing Fault Classification Scheme Based on k-Optimized Adaptive Local Iterative Filtering and Improved Multiscale Permutation Entropy

The health condition of the rolling bearing seriously affects the operation of the whole mechanical system. When the rolling bearing parts fail, the time series collected in the field generally shows strong nonlinearity and non-stationarity. To obtain the faulty characteristics of mechanical equipment accurately, a rolling bearing fault detection technique based on k-optimized adaptive local iterative filtering (ALIF), improved multiscale permutation entropy (improved MPE), and BP neural network was proposed. In the ALIF algorithm, a k-optimized ALIF method based on permutation entropy (PE) is presented to select the number of ALIF decomposition layers adaptively. The completely average coarse-graining method was proposed to excavate more hidden information. The performance analysis of the simulation signal shows that the improved MPE can more accurately dig out the depth information of the time series, and the entropy value obtained is more consistent and stable. In the research application, rolling bearing time series are decomposed by k-optimized ALIF to obtain a certain number of intrinsic mode functions (IMFs). Then the improved MPE value of effective IMF is calculated and input into backpropagation (BP) neural network as the feature vector for automatic fault identification. The comparative analysis of simulation signals shows that this method can extract fault information effectively. At the same time, the experimental part shows that this scheme not only effectively extracts the fault features, but also realizes the classification and identification of different fault modes and faults of different degrees, which has a certain application prospect in the research and application direction of rolling bearing fault identification.

Complementary Ensemble Adaptive Local Iterative Filtering and Its Application to Rolling Bearing Fault Diagnosis

A fault diagnosis model for rolling bearing based on complementary ensemble adaptive local iterative filtering (CEALIF), Laplacian score (LS) feature selection, and genetic algorithm-based backpropagation neural network (GA-BPNN) is proposed in this article. When the rolling bearing fails, the field-measured waveforms usually shows strong nonlinearity and non-stationary characteristics. Adaptive Local Iterative Filtering (ALIF) is an alternative novel approach to empirical mode decomposition to decompose complex signals into multiple intrinsic mode functions (IMFs), but modal aliasing will occur in actual processing. Aiming at the phenomenon of modal aliasing, a noise-assisted analysis methodology, namely complementary ensemble adaptive local iterative filtering, which could overcome the modal aliasing problem of ALIF. This article applies it to the pre-processing of rolling bearing time series. Then, the time domain (TD) statistical features of the IMFs, their Fourier frequency domain (FD) features, and the time frequency domain (TFD) energy features are extracted to capture the fault information. Meanwhile, to avoid feature redundancy and enhance the diagnostic performance, the LS is adopted to rank the features to improve the fault characteristics. Subsequently, the optimized feature vectors are entered into the GA-BPNN to automatically achieve the fault type recognition. The experimental data analysis results of rolling bearings indicate that the model can effectively diagnose the degree and type of failure.

Permanent magnet synchronous generator stator current AM-FM model and joint signature analysis for planetary gearbox fault diagnosis

Wind energy is renewable and wind power generation facilities have been growing rapidly around the world. Permanent magnet synchronous generators (PMSGs) and planetary gearboxes are commonly used in wind turbines (WTs). For planetary gearbox fault diagnosis, PMSG stator current signal analysis provides a potential alternative approach because stator current signals contain fault signatures with easier accessibility comparing to conventional vibration signals. Planetary gearbox faults result in PMSG input torque oscillations, leading to both amplitude modulation and frequency modulation (AM-FM) effects on stator current signals. To better reveal gear fault features in PMSG stator current signals, this paper proposes to diagnose planetary gearbox faults from three complementary analysis results of the PMSG stator current, i.e., Fourier spectrum, amplitude demodulated spectrum and frequency demodulated spectrum. For this purpose, an AM-FM current signal model is derived through mechanical-magnetic-electric interaction analysis. Explicit equation of Fourier spectrum is derived with its sideband characteristics investigated. Both amplitude and frequency demodulation analyses are proposed to compensate for or mitigate the difficulty in the intricate sideband analysis of Fourier spectrum. Due to its capability in signal mono-component decomposition for accurate estimation of instantaneous frequency, adaptive local iterative filtering is utilized in frequency demodulation analysis. Explicit equations of corresponding demodulated spectra are derived, and gear fault signatures in PMSG stator current signals are revealed. The theoretical derivations and proposed methods are validated through lab experiments by diagnosing localized faults on the sun, planet and ring gears.

A Feature Extraction Method of Wheelset-Bearing Fault Based on Wavelet Sparse Representation with Adaptive Local Iterative Filtering

The feature extraction of wheelset-bearing fault is important for the safety service of high-speed train. In recent years, sparse representation is gradually applied to the fault diagnosis of wheelset-bearing. However, it is difficult for traditional sparse representation to extract fault features ideally when some strong interference components are imposed on the signal. Therefore, this paper proposes a novel feature extraction method of wheelset-bearing fault based on the wavelet sparse representation with adaptive local iterative filtering. In this method, the adaptive local iterative filtering reduces the impact of interference components effectively and contributes to the extraction of sparse impulses. The wavelet sparse representation, which adopts L1-regularized optimization for a globally optimal solution in sparse coding, extracts intrinsic features of fault in the wavelet domain. To validate the effectiveness of this proposed method, both simulated signals and experimental signals are analyzed. The results show that the fault features of wheelset-bearing are sufficiently extracted by the proposed method.

Rolling element bearing weak fault diagnosis based on spatial correlation and ALIFD

Vibration signals of rolling element bearings during operation are always very complex, random strongly and broadband. Adaptive Local Iterative Filtering Decomposition (ALIFD) can overcome the smoothness and adaptive flaws of Iterative Filtering Decomposition (IFD), but it is so susceptible to random noise that it’s less effective. Here, spatial correlation was proposed. Firstly, the signal was denoised by spatial correlation and decomposed into several modes by ALIFD. Finally, the envelope demodulation was analyzed to extract fault feature. The simulating signal analysis and bearing fault simulator show that this method can be available for separating different frequencies of bearing fault vibration signals.

An improved higher-order analytical energy operator with adaptive local iterative filtering for early fault diagnosis of bearings

Early fault diagnosis in rolling bearings is crucial to maintenance and safety in industry. To highlight the weak fault features from complex signals combined with multiple interferences and heavy background noise, a novel approach for bearing fault diagnosis based on higher-order analytic energy operator (HO-AEO) and adaptive local iterative filtering (ALIF) is put forward. HO-AEO has better effect in dealing with heavy noise. However, it is subjected to the limitation of mono-components. To solve this limitation, ALIF is adopted firstly to decompose the nonlinear, non-stationary signals into multiple mono-components adaptively. In the next, the resonance frequency band as the optimal intrinsic mode function (IMF) is selected according to the maximum kurtosis. In the following, HO-AEO is utilized to highlight weak fault characteristics of the selected IMF. Finally, the early bearing fault is diagnosed by the energy operator spectrum based on fast Fourier transform (FFT). Comparisons in the simulation indicate that the fourth order HO-AEO shows the best performance in fault diagnosis compared with Teager energy operator (TEO), analytic energy operator (AEO), the second and the third order HO-AEO. The simulated test and experimental results demonstrate that the proposed approach could effectively extract weak fault characteristics from contaminated vibration signals.

Generalized adaptive mode decomposition for nonstationary signal analysis of rotating machinery: Principle and applications

Abstract Rotating machinery signals are usually intricate and often nonstationary. The analysis of such signals is further complicated because of the spectral overlaps between constituent frequency components under time-varying running conditions. Most conventional signal processing methods are unable to effectively extract the embedded meaningful information. Adaptive mode decomposition (AMD) methods are flexible to describe complex signals, free from the limitations inherent in conventional basis expansion and thus more suited to unveiling the underlying physical nature. However, as those methods work in a way similar to filter banks, i.e. via rectangular partition of time-frequency plane, they are effective only for signals with constituent frequency components that are almost constant and parallel to each other. As such, it would be inappropriate to apply such methods when signal constituent frequency components vary considerably over time and particularly overlap in frequency domain. To address this issue, this paper proposes a general framework by exploiting the unique capability of generalized demodulation to transform an arbitrary time-varying instantaneous frequency into a constant frequency. In doing so, many state-of-the-art AMD methods (including empirical mode composition, local mean decomposition, intrinsic time-scale decomposition, local characteristic scale decomposition, Hilbert vibration decomposition, empirical wavelet transform, variational mode decomposition, and adaptive local iterative filtering) can be extended to analyze highly nonstationary complex signals with spectral overlaps. The principle and advantage of generalized adaptive mode decomposition (GAMD) are illustrated through numerical simulation. The GAMD’s performance in monitoring different rotating machinery components (including a planetary gearbox, an aircraft engine rolling bearing and a hydraulic turbine rotor) is demonstrated and validated under time-varying conditions.

Spectral and convergence analysis of the Discrete ALIF method

The Adaptive Local Iterative Filtering (ALIF) method is a recently proposed iterative procedure to decompose a signal into a finite number of “simple components” called intrinsic mode functions. It is an alternative to the well-known and widely used empirical mode decomposition method, and it has proved to be a powerful and promising algorithm. However, so far, no convergence analysis is available for the ALIF algorithm both in the continuous and discrete settings. In the present paper we focus on the discrete version of the ALIF method and we tackle the problem of studying its convergence properties. Using recent results about sampling matrices and the theory of generalized locally Toeplitz sequences — which we extend in this paper — we perform a spectral analysis of the ALIF iteration matrices, with a special attention to the eigenvalue clustering and the eigenvalue distribution. Based on the eigenvalue distribution, we formulate a necessary condition for the convergence of the Discrete ALIF method. Moreover, we provide a simple criterion to construct appropriate filters satisfying this condition. We also present several numerical examples in support of the theoretical study. Our contribution represents a first significant step toward a complete convergence analysis of the ALIF method, an analysis which appears to be rather difficult from a mathematical viewpoint as the ALIF iteration matrices possess a peculiar structure that, to the best of the authors' knowledge, has never been investigated in the literature.

Bearing Fault Diagnosis with Kernel Sparse Representation Classification Based on Adaptive Local Iterative Filtering-Enhanced Multiscale Entropy Features

To improve the bearings diagnosis accuracy considering multiple fault types with small samples, a new approach that combined adaptive local iterative filtering (ALIF), multiscale entropy features, and kernel sparse representation classification (KSRC) is put forward in this paper. ALIF is used to adaptively decompose the nonlinear, nonstationary vibration signals into a sum of intrinsic mode functions (IMFs). Multiple entropy features such as sample entropy, fuzzy entropy, and permutation entropy with multiscale are computed from the first three IMFs and a total of one hundred and eighty features are obtained. After normalization, the features are employed to train and test the classifier KSRC, respectively. Finally, the proposed approach is evaluated with two experimental tests. One is concerned with different types of bearing faults from the centrifugal pump; and the other is from Case Western Reserve University (CWRU) considering 12 bearing fault states. Experimental results have proved that the proposed approach is efficient for bearing fault diagnosis, and high accuracy will be obtained with high dimensional features through small samples.

A novel deep learning diagnosis scheme for rotating machinery using adaptive local iterative filtering and ensemble hierarchical extreme learning machine

There are many hyper-parameters to be tuned in both machine learning model and deep learning model, and the structure of the deep learning model is large and complicated, making it extremely diffic...