Transient Feature Extraction Research Articles

Estimation of abnormal states in shunt capacitor banks using transient disturbance feature extraction

Shunt capacitor banks are essential for reactive power compensation, ensuring voltage stability, and reducing system losses. These banks consist of multiple units with components in series and parallel. A few component failures do not immediately affect the safe operation of the capacitor bank, but component breakdown can lead to voltage redistribution. Under combined factors such as system overvoltage and equipment aging, and others can trigger an avalanche effect causing capacitor breakdown, resulting in significant safety accident risks. Practical operation experience shows that partial component breakdown generates many transient disturbance signals. Quantitative analysis of these signals can detect capacitor bank anomalies early. This paper proposes the quantitative extraction of transient disturbance characteristics using the Prony algorithm and estimates the phase and number of capacitors that break down to judge capacitor anomalies. The simulation part verifies the theoretical analysis and detection algorithm’s correctness through numerical simulations and PSCAD (Power Systems Computer Aided Design) electromagnetic transient simulations. The numerical simulations consider different signal lengths, noise levels, attenuation coefficients, and oscillation frequencies. In the PSCAD simulation environment, verification models are built under varying sampling frequencies, numbers of breakdown components, signal lengths, and signal-to-noise ratios. These simulation results verify the accuracy of the detection algorithm under different conditions.

Transient Feature Extraction of Encoder Signal Based on Envelope Demodulation for Fault Diagnosis of RV Reducer

Transient Feature Extraction of Encoder Signal Based on Envelope Demodulation for Fault Diagnosis of RV Reducer

Fault Diagnosis Method of Inter-Turn Short Circuit of Permanent Magnet Synchronous Motor Based on Third Harmonic

In order to realize the effective diagnosis of turn to turn short circuit fault of permanent magnet synchronous motor, a method of turn to turn short circuit fault diagnosis of permanent magnet synchronous motor based on third harmonic is proposed in this paper. Establish the system frequency oscillation analysis model of permanent magnet synchronous motor under the condition of turn to turn short circuit, collect the turn to turn harmonic of permanent magnet synchronous motor, then realize the third harmonic detection of permanent magnet synchronous motor under turn to turn short circuit fault, and detect the inertia of permanent magnet synchronous motor system. Based on the transient energy analysis and fault feature extraction, the turn to turn short circuit fault of permanent magnet synchronous motor is diagnosed. The test results show that this method has high reliability and good detection performance, and can improve the output stability and condition monitoring ability of permanent magnet synchronous motor.

Transient-extracting wavelet transform for impulsive-like signals and application to bearing fault detection

Being able to characterize impulsive-like signals and extract their transitory features is difficult due to the presence of noise and irrelevant signal components in real signals. To address these problems, a brand-new time-frequency (TF) analysis technique called the transient-extracting wavelet transform is developed. This method is put forth by first investigating which TF coefficients can represent the fundamental TF properties of impulsive signals, and then designing an extraction operator to get the most related TF coefficients while simultaneously removing the unrelated ones. The signal reconstruction of this method is also analyzed. Additionally, a transient feature extraction approach is suggested for pinpointing the impulse’s occurrence timing, which is essential for correctly identifying the fault type. The analysis shows that the suggested method is more able to analyze impulsive-like data and is an effective bearing defect detector.

Recognition of Oil Content for Oil Shale by Electronic Nose Based on Transient and Steady-State Fusion Feature Extraction Method

The evaluation of oil shale is significant to oil and gas exploration. Electronic nose (E-nose) has excellent odor recognition characteristics and can be used as a potential tool for oil content recognition. In this article, the odor samples of Huadian and Wangqing oil shale with different oil contents were obtained, and the recognition ability and feasibility of E-nose on oil content were investigated for the first time. More comprehensive information of oil shale was obtained by using transient and steady-state fusion feature (TAS) extraction method, which improves the recognition rate, and the influence of feature extraction methods and temperatures on oil content recognition were further discussed. The experimental results show that the E-nose combined with the proposed feature extraction method can realize the rapid in situ recognition of oil content at low temperatures. This work will help to propose more effective methods for the evaluation and development of oil shale.

A Physics-Based and Data-Driven Feature Extraction Model for Blades Icing Detection of Wind Turbines

Blades icing will seriously affect the performance of wind turbines with respect to power loss and dynamic load increase. Blades icing detection technique becomes necessary to advance de-icing maintenance. Extracting effective features from supervisory control and data acquisition (SCADA) data has become a challenging task during operating conditions under icing. Current research work lacks for the integration of physical information and insufficient analysis of icing dynamic evolution in the process of feature extraction. In order to eliminate these deficiencies, a multilevel feature extraction model combined with physical information and intelligent algorithm (MFT-PI) is proposed in this article. First, features that characterize the severity of icing are extracted by power characteristic curve technology and feature selection technology in the transient feature extraction level. These techniques are driven by the information of power loss and icing mass growth. Second, a fully connected neural network integrated with triplet loss function is designed for extracting fusion feature, which considers the similarity of data distribution between early-icing stage and non-icing stage. Finally, the proposed feature extraction model is evaluated in two icing wind turbines. Results show that fusion features extracted from the proposed model is more stable, reliable, and accurate by comparing the performance of other feature groups in different classifiers.

Fault transient feature extraction method of 66kV transmission line based on improved wavelet threshold and CEEMDAN-FME

In the 66kV transmission line, the compensation effect of the arc suppression coil, so the amplitude of fault zero sequence current is weak after the occurrence of single-phase grounding responsibility. The fault characteristics are not obvious, and there is environmental noise interference during fault signal acquisition, which further increases the difficulty of ground fault feature extraction. To solve the above problems, a fault transient feature extraction method of 66kV transmission line is proposed, based on improved wavelet threshold and Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN) - Fuzzy measure entropy (FME). The characteristics of single-phase grounding transient fault in small current grounding system are analyzed. The transient fault signal was preprocessed by an improved wavelet threshold method, and the transient fault characteristics were extracted accurately by CEEMDAN-FME processing. The proposed method has good anti-noise performance through the simulation comparison and verification of denoising processing and transient feature extraction by MATLAB. It can effectively extract fault features when the interference degree is significant.

Robust event detection for residential load disaggregation

Nonintrusive load monitoring (NILM) can facilate the transition to energy-efficient and low-carbon buildings. Event detection is the first and most critical step in event-based NILM and can improve the efficiency and performance of NILM by decreasing inference times to the number of events with transient features extracted from events. However, existing event detection methods with fixed parameters may fail to achieve high accuracy in the case of uncertain and complicated residential load changes such as high fluctuation, long transition, and near simultaneity in both power and time dimensions. Besides, it is difficult to transfer the fixed parameter to new households with different load profiles. Furthermore, most of these methods prove that they are able to detect events but not able to extract features for load disaggregation. This paper proposes a robust event detection method with adaptive parameters to deal with such issues. Specifically, a window with adaptive margins, multi-window screening, and adaptive threshold method is proposed to detect events in aggregated load data with high sampling rate (>1 Hz). The proposed method captures the transitions by adaptively tuning parameters including window width, margin width, and thresholds. It can also achieve good performance with blind parameter setting so that it is suitable for unknown households or datasets. Furthermore, it captures complete transitions that are indispensable for transient feature extraction. Case studies on a 20 Hz dataset, the 50 Hz LIFTED dataset, and the 60 Hz BLUED dataset show that the proposed method can robustly outperform other state-of-the-art event detection methods. The robust performance of the proposed method is also verified by a cross validation of parameters among different datasets. Lastly, the proposed event detection method is demonstrated to have the merits of improving the performance of load disaggregation.

An Improved Time-Varying Morphological Filtering and Its Application to Bearing Fault Diagnosis

Morphological filtering (MF) is a typical fault feature extraction technology, which has been widely used in rolling bearing fault diagnostics. The structural element (SE) length has an important influence on feature extraction and noise removal, and the filtering result obtained by the filtered signal under a single SE length or the weighted average of different filtered signals under partial SE lengths is limited and insufficient. To address this problem, a transient feature extraction method called improved time-varying morphological filtering (ITVMF) is proposed for bearing fault diagnosis, wherein the SE length is adaptively determined according to the inherent characteristics of vibration signals. Moreover, the autocorrelation envelop spectrum (AES) is employed to further eliminate fault-unrelated components. The main innovation of this method is to automatically extract bearing fault-related transient features using a novel time-varying SE (TVSE) based on autocorrelation while effectively eliminating the interference of random impulses and strong background noise on bearing fault features. The developed ITVMF-AES is investigated and validated on the simulation signals, accelerated bearing degradation data, and artificial experimental test data, and compared with three existing feature extraction methods. The results demonstrate that the developed methodology is more efficient in excavating transient impulse features and identifying bearing faults than the comparison methods, and has potential engineering application value.

Local lowest-rank dynamic mode decomposition for transient feature extraction of rolling bearings

The fault diagnosis mainly relies on the detection of periodic pulse components caused by the local damage. However, the impulsive component in bearing vibration signals is usually disturbed by noise and some harmonics due to the harsh working environment, which makes great challenge to fault diagnosis. To enhance the performance in fault diagnosis of rolling bearing, a local lowest-rank dynamic mode decomposition (LLRDMD) is proposed in this paper. Firstly, an improved projection operator is proposed to reduce the noise of augmented snapshot matrix and thus eliminating the bias of high-dimension dynamic system matrix. Secondly, by solving a local lowest-rank optimization problem, the impulsive component and interference components in the high-dimension dynamic system matrix are separated. Compared with DMD, the proposed method achieves better performance in the extraction of transient pulse component while avoiding the problems of parameter selection and mode selection. Lastly, the proposed method is applied to three sets of real datasets. Compared with existing methods, LLRDMD achieves a higher accuracy in fault diagnosis.

A Novel Based on the Log-Squared Envelope Harmonic Noise Ratio Fault Transient Feature Extraction Method

To counter the sensitivity of kurtosis for the random pulse, a novel periodic detection index based on the log-squared envelope harmonic noise ratio (LSEHNR) is constructed, and then, a novel LSEHNRgram feature extraction method is proposed by introducing the maximal overlap discrete wavelet packet transform (MODWPT). Firstly, MODWPT is used to decompose the frequency band of the signal and a series of subsignals are obtained from each stage of decomposition, which are called nodes. Secondly, autocorrelation of the log-squared envelope for each node at each level is computed; calculate the LSEHNR, simultaneously. Then, the LSEHNR values of all nodes are calculated and the node with the maximal of the LSEHNR value is selected for spectrum analysis. Finally, the square envelope spectrum (SES) analysis is performed on the in-band filtered signal to extract the transient characteristics of the rolling bearing fault. The experimental results indicate that the LSEHNRgram method can accurately detect the demodulation frequency band and enhance the fault features under the condition of the low noise-to-signal ratio (SNR). The effectiveness of the method is verified on numerical and experimental datasets by comparison with autogram, infogram, and fast kurtgram methods.

Microheater Integrated Nanotube Array Gas Sensor for Parts-Per-Trillion Level Gas Detection and Single Sensor-Based Gas Discrimination.

Real-time monitoring of health threatening gases for chemical safety and human health protection requires detection and discrimination of trace gases with proper gas sensors. In many applications, costly, bulky, and power-hungry devices, normally employing optical gas sensors and electrochemical gas sensors, are used for this purpose. Using a single miniature low-power semiconductor gas sensor to achieve this goal is hardly possible, mostly due to its selectivity issue. Herein, we report a dual-mode microheater integrated nanotube array gas sensor (MINA sensor). The MINA sensor can detect hydrogen, acetone, toluene, and formaldehyde with the lowest measured limits of detection (LODs) as 40 parts-per-trillion (ppt) and the theoretical LODs of ∼7 ppt, under the continuous heating (CH) mode, owing to the nanotubular architecture with large sensing area and excellent surface catalytic activity. Intriguingly, unlike the conventional electronic noses that use arrays of gas sensors for gas discrimination, we discovered that when driven by the pulse heating (PH) mode, a single MINA sensor possesses discrimination capability of multiple gases through a transient feature extraction method. These above features of our MINA sensors make them highly attractive for distributed low-power sensor networks and battery-powered mobile sensing systems for chemical/environmental safety and healthcare applications.

Feature Extraction of Weak-Bearing Faults Based on Laplace Wavelet and Orthogonal Matching Pursuit

The transient impact component of early bearing faults is not obvious, and the traditional basis function expansion method is poor in feature extraction under strong noise conditions. In this paper, a transient feature extraction technique is proposed based on Laplace wavelet and orthogonal matching pursuit algorithm and combined with sparse representation theory. First, the overcomplete and redundant Laplace wavelet dictionary is adopted to represent vibration signals in a sparse way. Then the Hilbert resonance demodulation method is employed to obtain the envelope spectrum of sparse representation signal. Finally, the coefficient calculation problem of sparse representation is solved by orthogonal matching pursuit (OMP) algorithm. Simulation examples and experimental example are used to examine the performance of the proposed method. The results show that the weak-bearing faults feature extraction can be effectively realized since the transient shock component can be identified. Furthermore, the effectiveness of the proposed method is verified by the cyclic multishock simulated signals as well as the practical rolling-bearing vibration signals. Moreover, the comparison studies are also carried out to show that the proposed method outperforms the traditional basis function expansion methods in weak fault feature extraction of bearing.

Transient feature extraction method based on adaptive TQWT sparse optimization

Aiming at the problem of strong impact, short response period and wide resonance frequency bandwidth of transient vibration signals, a transient feature extraction method based on adaptive tunable Q-factor wavelet transform (TQWT) was proposed. Firstly, the characteristic frequency band of the vibration signal was selected according to the time–frequency distribution. Based on the characteristic frequency band, the sub-band average energy weighted wavelet Shannon entropy was used to optimize the number of decomposition layers, quality factor and redundancy of TQWT, so as to achieve the adaptive optimal matching of the impact characteristic components in the vibration signal. Then, according to the characteristics of the transient impact of the telemetry vibration signal, the TQWT decomposition coefficients were sparse reconstructed to obtain more sparse impact characteristics, and the weighted power spectrum kurtosis was used as the impact characteristic index to select the optimal sub-band, Finally, the inverse transform of TQWT was used to reconstruct the optimal sub-band to enhance its weak impact features. The simulation and measured signal processing results verify the effectiveness of the algorithm.

Multiple Transient Extraction Algorithm and Its Application in Bearing Fault Diagnosis

Transient impulsive signal is usually related with the bearing or gear local defect. It is very difficult to extract those multi-transient features due to the non-stationary of the corresponding vibration signals of rotating machinery. Time-frequency analysis is a suitable tool for analyzing non-stationary signals. A multiple transient extracting transform has been proposed in this work, which can not only effectively detect the multiple transient information in the signal, but also achieve a more concentrated time-frequency representation. The results of numerical simulation show the effectiveness of this proposed method. The proposed multi-transient extracting transform can better locate the transient features and has a lower time-consuming and better noise robustness, compared with the traditional time-frequency analysis methods. Finally, multi-transient extraction algorithm is utilized to analyze practical bearing vibration signals. It has been well demonstrated that the proposed method is more effective than other advanced time-frequency methods in the field of transient feature extraction.

Investigation of Resonance and Ferroresonance Overvoltages due to Cable-Transformer Interactions

In this paper, the resonance and ferroresonance phenomena are deeply investigated considering two different cable-transformer power systems. The internal transformer resonance overvoltage case is initiated due to a single-phase earth fault, while the studied ferroresonance case is due to a single-phase switching in the source feeding an unloaded transformer via a power cable. The resulted secondary overvoltage and the core flux signatures are used as a classifier, where the transient features are extracted using three algorithms. Although the voltage frequency spectrum and phase-plane diagrams have been previously utilized for the ferroresonance mode evaluation, they are exploited in this paper for the comparison study and accordingly for the detection of resonance and ferroresonance phenomena. Salient features found in the transients extracted using the well-known DWT are used as inputs of the third algorithm. A new detection approach is attained based on the DWT-based transient feature extraction of resonance and ferroresonance overvoltages.

Transient Feature Extraction Based on Time–Frequency Manifold Image Synthesis for Machinery Fault Diagnosis

Fault diagnosis of rotating machinery is crucial to the safety management of the equipment. However, the weaker intrinsic features are generally submerged in the strong noise interference and modulated to multiple frequency scales, which will weaken the extraction and identification of the transient features. To enhance the transient features, a method called time–frequency manifold image synthesis (TFMIS) is proposed in this paper. By inheriting and promoting the merits of time–frequency manifold (TFM) in feature enhancement and in-band noise suppression, the proposed method contributes on the natural compression and enhancement of the time–frequency transient features in the view of the image processing. First, the raw time–frequency image (TFI) is compressed by the 2-D discrete wavelet transform with the principal structure remained. These approximation sub-TFIs are later used to achieve a fast TFM learning process. Then, a relationship between the global TFI and the local TFM can be adaptively built by using the histogram concept with the probability distribution property demarcated. Thereupon, the proposed method can enhance a global manifold transient structure with a matching rule built from the local TFM. Consequently, by a series of inverse transformations, a TFMIS scheme is constructed for the transient feature extraction in a self-learning process. Two case studies, including bearing and gear transient feature extraction, confirm the performance of the proposed method in achieving rather high-contrast results for the natural transients, and more precise results for the fault frequency identification in detection of periodic transient signals.

Transient feature extraction of encoder signal for condition assessment of planetary gearboxes with variable rotational speed

This paper proposes a transient feature extraction algorithm of encoder signal for the condition assessment of planetary gearboxes under variable speed condition. In the proposed method, local polynomial fitting and sparsity based algorithm (LSA) is first constructed to extract time domain transient features from raw encoder signal and then order tracking (OT) technique is applied to convert non-stationary transient features in time domain into stationary ones in angular domain. Meanwhile, to solve the formulated local polynomial fitting and sparsity based optimization problem, a fast convergence algorithm is derived based on alternating direction method of multipliers and majorization-minimization. Additionally, an adaptive parameter selection strategy is introduced for automatically selecting appropriate parameters of the proposed method LSA-OT. With the proposed method LSA-OT, the transient features can be effectively extracted and the condition assessment of planetary gearboxes can be easily achieved. The effectiveness of LSA-OT is confirmed by simulation evaluation and case study.

Fast time-frequency manifold learning and its reconstruction for transient feature extraction in rotating machinery fault diagnosis

The transient features caused by local fault are of vital importance for rotating machinery fault diagnosis, while they are always submerged and distorted by a large amount of noise interference and macro-structural vibrations. Time-frequency manifold (TFM) has been developed to extract these transient features in time-frequency domain, and the corresponding TFM-based data denoising has been further used to recover the time-domain transient signal. However, due to its time-frequency analysis and nonlinear manifold learning properties, there will be not only high computational cost for TFM learning but also a challenge for reliable transient feature recovery, which has further limited this technique to application in practical and on-line rotating machinery fault diagnosis. To overcome these problems, an improved fast TFM (FTFM) method is first developed to effectively but efficiently extract the transient characteristics. In the process of FTFM learning, a new time-frequency analysis technique called short-frequency Fourier transform (SFFT) is introduced to efficiently describe time-frequency distribution as a time-frequency image (TFI), and two-dimensional discrete wavelet transform (2-D DWT) is used to further compress the inherent time-frequency structure for nonlinear manifold learning. Subsequently, different from the conventional model-based sparse expression, by means of sparse latent components of FTFM basis, the corresponding signal reconstruction is later proposed in a series of inverse transformations, including the inverse SFFT developed and inverse 2-D DWT employed in this paper. The proposed FTFM-based reconstruction method indicates attractive prospects in the following two aspects: effective but efficient TFM learning for practical and on-line application, sound and adaptive signal reconstruction with the data-driven FTFM basis. Theoretical analysis and experimental verification using simulation and testing data indicated the computational efficiency and effectiveness of the proposed FTFM-based method for rotating machinery fault diagnosis.

Transient Feature Extraction by the Improved Orthogonal Matching Pursuit and K-SVD Algorithm With Adaptive Transient Dictionary

To detect the incipient faults of rotating parts used in electromechanical systems widely, a novel transient feature extraction method based on the improved orthogonal matching pursuit (OMP) and one-dimensional K-SVD algorithm is explored in this paper. First, the stopping criterion of adaptive spark is developed, and then the corresponding OMP algorithm is used to remove the modulated and harmonic signals adaptively. Second, the residual signal is reformulated as a signal matrix by period segmentation and circulating shift, and the initial transient dictionary is constructed via the time-domain average technique. Subsequently, a novel K-SVD algorithm is proposed to get the optimized transient dictionary for the one-dimensional signal. Finally, the repetitive transient signal is recovered by the optimized dictionary. The simulated and experimental results show that the proposed method can not only much faster extract the fault characteristics than the traditional K-SVD method, but also more accurately detect the repetitive transients than the infogram method and the traditional K-SVD method.