Resonance-based Sparse Signal Decomposition Research Articles

Research on a Fault Feature Extraction Method for an Electric Multiple Unit Axle-Box Bearing Based on a Resonance-Based Sparse Signal Decomposition and Variational Mode Decomposition Method Based on the Sparrow Search Algorithm

Research on a Fault Feature Extraction Method for an Electric Multiple Unit Axle-Box Bearing Based on a Resonance-Based Sparse Signal Decomposition and Variational Mode Decomposition Method Based on the Sparrow Search Algorithm

Fault detection of rotating machinery based on marine predator algorithm optimized resonance-based sparse signal decomposition and refined composite multiscale fluctuation dispersion entropy.

Feature extraction is the key to the fault detection of rotating machinery based on vibration signals, and the quality of the features influences the reliability of the detection. This paper develops a fault feature extraction method of rotating machinery based on optimized resonance-based sparse signal decomposition and refined composite multiscale fluctuation dispersion entropy. First, resonance-based sparse signal decomposition is used to decompose the vibration signals adaptively. In order to obtain the resonance-based sparse signal decomposition algorithm with optimal performance, the marine predator algorithm is used for the parameters optimization with correlation kurtosis as the fitness function. Subsequently, based on the refined composite coarse-grained process and fluctuation dispersion entropy, a refined composite multiscale fluctuation dispersion entropy is developed, enabling a more accurate and comprehensive measure of the complexity of time series. Then, all feature matrices are input to the support matrix machine for fault identification. Experiments are conducted using two typical rotating machinery datasets for the validity of the proposed method, and comparisons are made with other methods. The results show that the proposed scheme outperforms other comparative methods regarding classification accuracy and stability. In addition, the proposed scheme can obtain relatively reliable classification results even when the data volume is small and the background noise is significant, demonstrating the scheme's potential for application in practical engineering.

Fault Feature Extraction of Automobile Wheel Hub Bearing Based on RSK and RSSD

Abstract Wheel hub bearing not only bears axial load but also radial load, and then its running status directly affects the performance and safety of the automobile. In particular, complicated and variable driving conditions of the automobile are not only liable to aggravate the occurrence of mechanical fault such as localized wear, but also often cause powerful intermittent interference noise. It is difficult to extract the vibration characteristics of the transient impact and harmonic components of the wheel bearings in the event of a local fault. An extraction method of fault feature based on ration of smooth and kurtosis (RSK) index and resonance-based signal sparse de-composition (RSSD) is proposed for automobile wheel hub bearings, which aims at the minimum value of RSK index, sequentially optimizes the Q-factor of the resonance sparse decomposition, and obtains its optimal value adaptively to achieve the decomposition of low resonance components with transient impact components and high resonance components with harmonic components. The effect of the method has been verified by powerful intermittent interference simulation signals and experiment signals from local fault of wheel hub bearing with periodic dynamic radial loading.

An Integrated Model of Atom Search Optimization-Based Resonance Sparse Signal Decomposition and Cross-Validation SVM for Gearbox Fault Diagnosis

In the aspect of gearbox fault diagnosis, the periodic pulse signal containing fault characteristics is often overwhelmed with other strong interference components, which brings a great challenge for gearbox fault detection and status identification. To address these issues, this paper develops a novel resonance-based sparse signal decomposition (RSSD) fault diagnosis method combined with support vector machine (SVM). Based on the key of the decomposition parameters in the resonant sparse decomposition method, the influence of atom search optimization (ASO) on the quality factor in the resonance-based sparse signal decomposition method is primarily studied. The vibration signal of the gearbox was decomposed by resonance sparse decomposition with the optimal quality factor, from which the high and low-resonance components were obtained. Then, the power spectrum entropy, singular spectrum entropy, and time-domain energy entropy of the low-resonance signal were calculated. Finally, the pattern recognition of gearbox fault was completed with a designed cross-validation SVM pattern recognition model. The numerical simulation and gearbox fault experiments demonstrate that the presented method can achieve great recognition accuracies and effect capacities against interference components involved in the gearbox vibration signal.

Feature enhancement method of rolling bearing acoustic signal based on RLS-RSSD

The bearing acoustic signal is interfered by reflected sounds and background noises, resulting in a low signal-to-noise ratio (SNR). To address this problem, this paper proposes a feature enhancement method that combines recursive least squares (RLS) with resonance-based sparse signal decomposition (RSSD) into the RLS-RSSD method. First, the RLS method is used as the inverse filter to remove the reverberation as well as reduce the interference of the late reflected sound on the direct signal, then RSSD and wavelet denoising are used to eliminate aperiodic component in the low and high frequency bands. The signals are synthesized based on the amplitudes of different frequency signals, and finally, the bearing fault is determined by envelope spectrum analysis. The results of the simulation data, experimental data, and field application data analysis indicate that the frequency of bearing defects can be accurately extracted by the proposed method.

A new method of health condition detection for hydraulic pump using enhanced whale optimization-resonance-based sparse signal decomposition and modified hierarchical amplitude-aware permutation entropy

The normal operation of the hydraulic pump is the significant premise for the stable and dependable working of hydraulic equipment. Consequently, this research comes up with a health condition detection method of hydraulic pump. First of all, this approach selects resonance-based sparse signal decomposition (RSDD) to adaptively disintegrate vibration signals. The biggest problem of the RSDD algorithm is the requirement to artificially set a large number of key parameters, such as quality factor Q, weight coefficient A, and Lagrange operator u. The improper parameter settings will seriously affect the decomposition performance. To overcome this shortcoming, an enhanced whale optimization algorithm is presented to search the best parameter combination of the RSDD. The algorithm takes the correlation kurtosis as the optimization objective function to adaptively disintegrate the signal into low and high resonance components. Moreover, on the basis of the modified analytic hierarchy process and the amplitude-aware permutation entropy, the modified hierarchical amplitude-aware permutation entropy is raised for measuring the complexity of the measured time series more accurately and comprehensively. After that, a health condition detection method for hydraulic pump based on enhanced whale optimization-resonance-based sparse signal decomposition and modified hierarchical amplitude-aware permutation entropy is raised. Finally, through the usage of the hydraulic pump vibration data, this method is compared with other approaches. According to the experimental results, the raised method can identify the fault type more effectively, which is capable of offering a feasible idea for the health condition detection of hydraulic equipment.

A Hybrid Approach for Parkinson’s Disease diagnosis with Resonance and Time-Frequency based features from Speech signals

Parkinson’s Disease (PD) is a progressive neurological disorder that affects the functioning of the brain. As PD progresses, there arises a problem of maintaining coordination between brain and different parts of the body. Subjects have the problem of performing daily activities. Its early diagnosis is important to improve the quality of life of Parkinson’s patients. Most of Parkinson’s patients are likely to have voice disorders in the early stages. Therefore, the analysis of voice recordings with machine learning-based models can improve the diagnosis process. Voice signals are non-linear, non-stationary signals which exhibit oscillatory behavior. In this paper, a hybrid approach is proposed to extract features from resonance-based and time-frequency based information. A combination of Resonance based Sparse Signal Decomposition (RSSD) + Time-Frequency (T-F) algorithm is proposed. Two datasets are collected for the study. The first dataset D1 is a public dataset consisting of 16 PD + 21 Healthy Controls (HC) and the second dataset D2 is collected from 20 HC to explore the effect of diversity in the diagnosis process. To explore the potential of deep learning techniques in diagnosing speech impairments in PD patients, the potential of Convolution Neural Network (CNN) is also explored. To evaluate the method, we conducted several experiments with state-of-the-art feature extraction and classification techniques. The proposed hybrid approach distinguished PD and HC with a validation accuracy of 99.37%. We have also explored the effect of diversity in the data collection process and it is observed that diversity in the socio-cultural group plays very important role to put the system in the clinical practice.

Signal Identification of Gear Vibration in Engine-Gearbox Systems Based on Auto-Regression and Optimized Resonance-Based Signal Sparse Decomposition

As an essential part of the transmission system, gearboxes are considered as a major source of vibration. Signal identification of gear vibration is necessary for online monitoring of the mechanical systems. However, in engine-gearbox systems, the ignition impact of the engine is strong, so that the gear vibration is generally submerged. To overcome this issue, the resonance-based signal sparse decomposition (RSSD) method is used in this paper based on different oscillatory behaviors of the gear meshing impact and the engine ignition impact. To improve the accuracy of RSSD under interferences, the meshing frequency energy ratio (MF–ER) index is introduced into RSSD to adaptively choose the decomposition parameters. Before applying the RSSD method, the auto-regression (AR) model is used as a pre-whitening step to eliminate the normal gear meshing vibration, which improves the decomposition performance of RSSD. The effectiveness of the proposed AR-ORSSD (AR-based optimized RSSD) algorithm is tested using both simulated signals and measured vibration signals from an engine-gearbox system in a forklift. Comparisons were made with the RSSD algorithm based on a genetic algorithm. Experimental results indicate that the AR-ORSSD algorithm is superior at identifying gear vibration signals especially when under strong interferences.

Rolling bearing fault feature extraction using Adaptive Resonance-based Sparse Signal Decomposition

The existence of periodic impacts in collected vibration signal is the representative symptom of rolling bearing localized defect. Due to the complicacy of the working condition, the fault-related impacts are usually submerged in other ingredients. This article proposes an adaptive Resonance-based Sparse Signal Decomposition (RSSD) for extracting the fault features of rolling bearings. Adaptive RSSD is constructed to fetch the impacts from collected vibration signal, by making RSSD decomposed signal kurtosis value maximum using Lion Swarm Algorithm (LSA). Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is further performed to enhance the amplitude and periodicity of impacts contained in RSSD decomposed signal, so that fault feature is highlighted. The superiority and availability of proposed strategy are validated by applying to single fault feature extraction of an experimental dataset and compound faults feature extraction of a locomotive rolling bearing.

Compound fault diagnosis of rolling element bearings using multipoint sparsity–multipoint optimal minimum entropy deconvolution adjustment and adaptive resonance-based signal sparse decomposition

The rolling element bearings used in rotating machinery generally include multiple coexisting defects. However, individual defect–induced signals of bearings simultaneously arising from multiple defects are difficult to extract from measured vibration signals because the impulse-like fault signals are very weak, and the vibration signal is commonly affected by the transmission path and various sources of interference. This issue is addressed in this study by proposing a new compound fault feature extraction scheme. Vibration signals are first preprocessed using resonance-based signal sparse decomposition to obtain the low-resonance component of the signal, which contains the information related to the transient fault–induced impulse signals, and reduce the interference of discrete harmonic signal components and noise. The objective used for adaptively selecting the optimal resonance-based signal sparse decomposition parameters adopts the ratio of permutation entropy to the frequency domain kurtosis, as a new comprehensive index, and the optimization is conducted using the cuckoo search algorithm. Subsequently, we apply multipoint sparsity to the low-resonance component to automatically determine the possible number of impulse signals and their periods according to the peak multipoint sparsity values. This enables the targeted extraction and isolation of fault-induced impulse signal features by multipoint optimal minimum entropy deconvolution adjustment. Finally, the envelope spectrum of the filtered signal is used to identify the individual faults. The effectiveness of the proposed scheme is verified by its application to both simulated and experimental compound bearing fault vibration signals with strong interference, and its advantages are confirmed by comparisons of the results with those of an existing state-of-the-art method.

Identification of planetary gearbox weak compound fault based on parallel dual-parameter optimized resonance sparse decomposition and improved MOMEDA

Abstract Due to the complexity and harsh operating environment of planetary gear transmission system, compound fault may co-exist in the planetary gearbox, which results in different type faults coupling together and the weak fault impulse features being completely submerged by strong ambient noise, thus making it a great challenge to extract fault-related information from planetary gearbox. To address these issues, a novel compound fault features extraction technique based on parallel dual-parameter optimized resonance-based sparse signal decomposition (RSSD) and improved multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is proposed in this contribution. Firstly, according to the oscillation properties of different type faults, the parallel dual-parameter optimized RSSD constructs wavelet basis functions matching with fault features to adaptively decouple compound fault signal into high and low resonance components. Then, the improved MOMEDA is applied to deconvolute the resonance components to eliminate the interference of complex transmission path and strong ambient noise, thereby enhancing the weak periodic fault impulses. Finally, envelope demodulation processing for the enhanced signals is carried out to extract fault characteristic frequencies and identify different type faults. The effectiveness and feasibility of the proposed method are validated using both the numerical simulated signals and practical experimental dates from two different types of planetary gearbox compound fault. Moreover, comparisons with some existing methods illustrate the superiority of the proposed method to identify weak compound fault under strong ambient noise.

A novel short-frequency slip fault energy distribution-based demodulation technique for gear diagnosis and prognosis

To conduct diagnosis and prognosis of gears, this paper introduces a novel short-frequency slip fault energy distribution-based demodulation method. As an essential step of the method, the resonance-based sparse signal decomposition algorithm is firstly employed to obtain the high-resonance part from the raw gear fault signal. To deal with the difficulty in determining the resonance frequency band, we establish a multi-input signal-output model to describe the signal components acquired from a faulty gear. Based on it, the short-frequency slip fault energy distribution graph is defined to locate the center frequency. Besides, the maximum amplitude in the short-frequency slip fault energy distribution graph can be used as a health indicator for prognosis, which is named as fault-induced resonance energy ratio. The effectiveness of the proposed method is validated with both simulated signal and test data. The positive results achieved in both experiments show the perfect property of the methodology for gear fault detection with high noise, especially when the fault is incipient. In addition, by comparing the fault-induced resonance energy ratio values of faulty gears with different severity, it is proved to be a reliable health indicator for gear prognostic.

Periodic impulse signal separation based on resonance-based sparse signal decomposition and its application to the fault detection of rolling bearing

The main purpose of the paper is to propose a new method to achieve separating periodic impulse signal among multi-component mixture signal and its application to the fault detection of rolling bearing. In general, as local defects occur in a rotating machinery, the vibration signal always consists of periodic impulse components along with other components such as harmonic component and noise; impulse component reflects the condition of rolling bearing. However, different components of multi-component mixture signal may approximately have same center frequency and bandwidth coincides with each other that is difficult to disentangle by linear frequency-based filtering. In order to solve this problem, the author introduces a proposed method based on resonance-based sparse signal decomposition integrated with empirical mode decomposition and demodulation that can separate the impulse component from the signal, according to the different Q-factors of impulse component and harmonic component. Simulation and application examples have proved the effectiveness of the method to achieve fault detection of rolling bearing and signal preprocessing.

Fault diagnosis of rolling element bearing weak fault based on sparse decomposition and broad learning network

Rolling element bearings are widely used in rotating machinery and, at the same time, they are easily damaged due to harsh operating environments and conditions. As a result, rolling element bearings are critical to the safe operation of the mechanical devices. The incipient fault information extraction of rolling bearings mainly faces the following difficulties: (1) The fault signal is too weak. (2) The fault mechanism and the dynamic model of the rolling bearing system are complex. (3) The oscillations caused by the fault shocks are overlapped due to the smaller impact between two adjacent faults. (4) The impact interval of the fault will change randomly. To overcome the aforementioned difficulties, a connection network constructed by resonance-based sparse signal decomposition (RSSD) and broad learning system (BLS) without the need for deep architecture, namely RSSD-BLS, is proposed for intelligent fault diagnosis. We construct RSSD-BLS by input layer, RSSD decomposition layer, feature layer and output layer. So, when the observed vibration signals are the input layer, the network first uses RSSD to decompose the raw vibration signal into high resonance components and low resonance components. Then, the network obtains energy spectrum features of high resonance components which decomposed by RSSD to extract the unique features in the feature. Finally, the network recognizes different fault conditions in the output layer. Through comparing with commonly used intelligent network diagnosis method, the superiority of the proposed RSSD-BLS is verified.

Fault diagnosis of rolling bearing based on resonance-based sparse signal decomposition with optimal Q-factor

When a localized defect is induced, the vibration signal of rolling bearing always consists periodic impulse component accompanying with other components such as harmonic interference and noise. However, the incipient impulse component is often submerged under harmonic interference and background noise. To address the aforementioned issue, an improved method based on resonance-based sparse signal decomposition with optimal quality factor ( Q-factor) is proposed in this paper. In this method, the optimal Q-factor is obtained first by genetic algorithm aiming at maximizing kurtosis value of low-resonance component of vibration signal. Then, the vibration signal is decomposed based on resonance-based sparse signal decomposition with optimal Q-factor. Finally, the low-resonance component is analyzed by empirical model decomposition combination with energy operator demodulating; the fault frequency can be achieved evidently. Simulation and application examples show that the proposed method is effective on extracting periodic impulse component from multi-component mixture vibration signal.

Weak Crack Detection for Gearbox Using Sparse Denoising and Decomposition Method

The localized faults of rotating machinery can be diagnosed by the extraction of the periodic transient impulses (PTIs); however, the PTI is generally weak and may get submerged in strong noise. To address this issue, a novel approach based on nonconvex sparse regularization denoising and adaptive sparse decomposition is proposed. The main work can be divided into two areas: 1) raw signal denoising and 2) repetitive impulses isolation. Specifically, for the raw signal denoising, the augmented Huber function is proposed as penalty function, and the convexity of the objective cost function (OCF) can be maintained; meanwhile, the solution of the proposed OCF can be solved using forward–backward splitting algorithm. For the repetitive impulses isolation, due to the commonly used resonance-based signal sparse decomposition (RSSD) method lacks adaptability and flexibility in practical application, the genetic algorithm (GA) is introduced to optimize the decomposition parameters of the RSSD that are selected adaptively in the desirable range according to the denoised signal in terms of the global optimization characteristic of GA. As an example, a pinion gear with weak root-crack failure is investigated based on the proposed approach. Compared to some state-of-the-art methods such as L1-norm fused lasso optimization (LFLO) and maximum correlated kurtosis deconvolution method, the results demonstrate that the proposed approach can effectively extract the weak fault frequency and its harmonics, and the shortcoming of the systematic underestimation of LFLO method has also been greatly improved.

Fault diagnosis method based on integration of RSSD and wavelet transform to rolling bearing

To solve the problem of early fault diagnosis of rolling bearing under strong background noise, a fault diagnosis method based on integration of Resonance-based Sparse Signal Decomposition (RSSD) and Wavelet Transform (WT) is proposed in this paper. The RSSD method is combined with quality factor optimization using genetic algorithm and sub-band reconstruction. Firstly, the early fault vibration signal of the rolling bearing is decomposed by RSSD. The kurtosis value of the low resonance component is taken as the objective function to optimize the combination of high and low quality factors with genetic algorithm. Then, the master sub-band is selected out to reconstruct the low resonance component based on the principle of energy dominant distribution. It can reduce the noise interference and enhance the impulse characteristic of the fault signal. Finally, characteristics of local optimization and multi-resolution of wavelet analysis considered, the multi-scale wavelet decomposition is applied to the reconstructed low resonance component to extract the fault features of the bearing failure deeply. The effectiveness and application value of the method are proved by two different diagnosis cases of rolling bearing faults. By comparisons, the fault feature extraction ability of the proposed method is prior to WPD method and similar or prior to EMD method for different bearing fault signals.

Gear Fault Diagnosis Based on Dual Parameter Optimized Resonance-Based Sparse Signal Decomposition of Motor Current

Motor current signature analysis (MCSA) provides a nondestructive and remote approach for a gear fault diagnosis. However, in addition to the fault-related components, motor current in the faulty gear system also contains the eccentricity-related components and gear meshing-related components, which contaminate the fault features and increase the difficulty of fault diagnosis. To extract fault features from these interferences, this paper proposes the dual parameters optimized resonance-based sparse signal decomposition (RSSD) method, which can decompose a complex signal into a high- and low-resonance component with two sets of overcomplete wavelet bases. After the decomposition, the fault-related components, which have short duration, will exist in low-resonance component. The novelty is that the wavelet bases related parameters, Q-factors, and decomposition levels are chosen automatically based on artificial bee colony algorithm to obtain the optimal decomposition results instead of chosen subjectively. Kurtosis of the low-resonance component is employed as optimization index. The proposed method is then verified on the gear fault-diagnosis platform, which consists of two permanent magnet synchronous motors and a pair of gears with transmission ratio of 3:2, and its effectiveness over some existing methods under different operating conditions is also validated.

Adaptive fault feature extraction from wayside acoustic signals from train bearings

Wayside acoustic detection of train bearing faults plays a significant role in maintaining safety in the railway transport system. However, the bearing fault information is normally masked by strong background noises and harmonic interferences generated by other components (e.g. axles and gears). In order to extract the bearing fault feature information effectively, a novel method called improved singular value decomposition (ISVD) with resonance-based signal sparse decomposition (RSSD), namely the ISVD-RSSD method, is proposed in this paper. A Savitzky-Golay (S-G) smoothing filter is used to filter singular vectors (SVs) in the ISVD method as an extension of the singular value decomposition (SVD) theorem. Hilbert spectrum entropy and a stepwise optimisation strategy are used to optimize the S-G filter's parameters. The RSSD method is able to nonlinearly decompose the wayside acoustic signal of a faulty train bearing into high and low resonance components, the latter of which contains bearing fault information. However, the high level of noise usually results in poor decomposition results from the RSSD method. Hence, the collected wayside acoustic signal must first be de-noised using the ISVD component of the ISVD-RSSD method. Next, the de-noised signal is decomposed by using the RSSD method. The obtained low resonance component is then demodulated with a Hilbert transform such that the bearing fault can be detected by observing Hilbert envelope spectra. The effectiveness of the ISVD-RSSD method is verified through both laboratory field-based experiments as described in the paper. The results indicate that the proposed method is superior to conventional spectrum analysis and ensemble empirical mode decomposition methods.

Resonance-Based Time-Frequency Manifold for Feature Extraction of Ship-Radiated Noise.

In this paper, a novel time-frequency signature using resonance-based sparse signal decomposition (RSSD), phase space reconstruction (PSR), time-frequency distribution (TFD) and manifold learning is proposed for feature extraction of ship-radiated noise, which is called resonance-based time-frequency manifold (RTFM). This is suitable for analyzing signals with oscillatory, non-stationary and non-linear characteristics in a situation of serious noise pollution. Unlike the traditional methods which are sensitive to noise and just consider one side of oscillatory, non-stationary and non-linear characteristics, the proposed RTFM can provide the intact feature signature of all these characteristics in the form of a time-frequency signature by the following steps: first, RSSD is employed on the raw signal to extract the high-oscillatory component and abandon the low-oscillatory component. Second, PSR is performed on the high-oscillatory component to map the one-dimensional signal to the high-dimensional phase space. Third, TFD is employed to reveal non-stationary information in the phase space. Finally, manifold learning is applied to the TFDs to fetch the intrinsic non-linear manifold. A proportional addition of the top two RTFMs is adopted to produce the improved RTFM signature. All of the case studies are validated on real audio recordings of ship-radiated noise. Case studies of ship-radiated noise on different datasets and various degrees of noise pollution manifest the effectiveness and robustness of the proposed method.