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Cyclostationary analysis has been strongly recognized as an effective demodulation tool in identifying fault features of rotating machinery based on vibration signature analysis. This study improves two current mature cyclostationary approaches, cyclic modulation spectrum (CMS) and fast spectral correlation (Fast-SC), combined with the novel frequency-domain application of Teager Kaiser energy operator (TKEO). They can enhance fault feature identification with the lower computational burden. Firstly, the raw vibration signal is transformed into the time–frequency domain through the short-time Fourier transform (STFT) to realize the conversion of the multi-carrier signal to a multiple signal-carrier signal. Secondly, the TKEO is utilized to enhance the fault feature by taking full advantage of demodulating the mono-component. Finally, the spectral coherence and enhanced envelope spectrum (EES) are calculated to effectively exhibit fault features. The superiority of the proposed methods is successfully validated by the simulation study and diagnosing the broken rotor bar (BRB) and bearing outer race faults of induction motors (IMs) under various operating conditions. • The frequency domain TKEO is proposed for processing the single carrier signal. • The fault extraction capabilities of CMS and Fast-SC are enhanced using TKEO. • The optimized methods have high computational efficiency. • The results validate the effectiveness of the proposed methods for IM fault diagnosis. [ABSTRACT FROM AUTHOR]

Transient impulses caused by local defects are critical for the fault detection of rotating machines. However, they are extremely weak and overwhelmed in the strong noise and harmonic components, making the transient features are very difficult to be extracted. This paper proposes an adaptive multi-scale improved differential filter (AMIDIF) to enhance the identification of transient impulses for rotating machine fault diagnosis. In this scheme, firstly, the AMIDIF is performed to decompose the measured signal of rotating machine into a series of multi-scale improved differential filter (MIDIF) filtered signals. Subsequently, in view of the MIDIF filtered signals exhibit varying extents of validity in revealing fault features, a weighted reconstruction method using correlation analysis is proposed in which the weighted coefficients are counted and distributed to the corresponding MIDIF filtered signals to highlight the effective MIDIF filtered signals and weaken the invalid ones. Finally, the transient impulse components of rotating machinery are obtained by multiplying the weighted coefficients and the MIDIF filtered signals under different scales. Furthermore, the fault types of rotating machines are inferred from the fault defect frequencies in the envelope spectrum of the transient impulses. Simulation analysis and experimental studies are implemented to verify the performance of the AMIDIF compared with the state-of-the-art methods including spectral kurtosis (SK), multi-scale average combination different morphological filter (ACDIF) and multi-scale morphology gradient product operation (MGPO). The results prove that the AMIDIF has excellent performance in extracting transient features for rotating machines fault diagnosis. • An AMIDIF is developed for transient impulse enhancement. • AMIDIF can extract the bidirectional impulses in the signal at the same time. • Correlation coefficient is used to optimize the weighted coefficient in AMIDIF. • Performance of the AMIDIF is validated by simulation and experimental cases. [ABSTRACT FROM AUTHOR]

In analyzing signals from a wind turbine gearbox this paper suggests a new signal processing procedure named as CMF-EEMD method which is formed by applying conventional EEMD to a new type of combined mode function (CMF). This CMF consists of a low frequency CMF, denoted as C L , and a high frequency CMF, denoted as C h . Then it optimizes the amplitude of the added noise in decomposing C h and C L using EEMD. Finally, it calculates cyclic autocorrelation function (CAF) for every characteristic IMF from EEMD. The proposed procedure is applied to analyze the multi-faults of a wind turbine gearbox and the results confirm better performances in resolving different signal components by the proposed method than that from the cyclic autocorrelation function (CAF) of a direct EEMD analysis. [ABSTRACT FROM AUTHOR]

Rolling element bearings are one of the critical elements in rotating machinery of energy engineering systems. A defective roller of bearing moves in and out of the load zone during each revolution of the cage. Larger amplitude impact transients (LAITs) are produced when the defective roller passes the load zone centre and the defective area strikes the inner or outer races. A series of LAIT segments with higher signal to noise ratio are separated from a continuous vibration signal according to the bearing geometry and kinematics. In order to eliminate the phase errors between different LAIT segments that can arise from rotational speed fluctuations and roller slippages, unbiased autocorrelation is introduced to align the phases of LAIT segments. The unbiased autocorrelation signals make the ensemble averaging more accurate, and hence, archive enhanced diagnostic signatures, which are denoted as LAIT-AEAs for brevity. The diagnostic method based on LAIT separation and autocorrelation ensemble average (AEA) is evaluated with the datasets captured from real bearings of two different experiment benches. The validation results of the LAIT-AEAs are compared with the squared envelope spectrums (SESs) yielded based on two state-of-the-art techniques of Fast Kurtogram and Autogram. [ABSTRACT FROM AUTHOR]

• A metastructure-aided bearing fault signal enhancing diagnosis method is proposed. • Drum-shaped metastructure creates selective frequency band enhancement. • Artificially adjustable resonance for full band demodulation. • Cost-efficient rolling bearing fault diagnosis with low sampling rate. Rolling bearings, extensively utilized in rotating machinery, have critical significance for online fault diagnosis in the domains of industrial equipment maintenance and accident prevention. Presently, fault diagnosis methods heavily rely on identifying the optimal resonance band in the high-frequency range (several kHz) to achieve high signal-to-noise ratio (SNR) fault information. However, these approaches, which necessitate high sampling rate sensing systems and complex algorithm deployments, contradict the practical requirements for cost-effective sensors and edge computing in online diagnosis. To address this contradiction, this paper introduces a novel Drum-shaped Metastructure (DMS) to enhance weak bearing fault signals, thus promoting the detection performance of conventional sensors. The DMS is constructed with a drumhead metastructure consisting of a central block mass attached with four spiral beams, of which its length can be adjusted by a tunable frequency mechanism (TFM) for different frequency responses of interest. The detail of its selective frequency enhancement characteristics is first studied through numerical simulations within a frequency range of 200 Hz to 1000 Hz. Subsequently, the effectiveness of the weak signal enhancement is verified on various bearing test systems, which utilize a prototype DMS fabricated by 3D printing. The results present a significant enhancement in the SNRs of the bearing fault signal, which is achieved by demodulating the full frequency band without the need for complex algorithms. Therefore, the proposed DMS provides a new cost-efficient approach to weak bearing fault diagnosis and online monitoring. [ABSTRACT FROM AUTHOR]

• The proposed method extends the cyclic frequency range to Fs /2. • The designed scale factor in CWT can help locate important frequency bands. • TKEO is improved to process the single-carrier signal in the frequency domain. • The developed method can enhance the fault features effectively. • Induction motor tests validate the superiority of the method for early fault detection. 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. [ABSTRACT FROM AUTHOR]

• A multiscale denoising branch is developed to extract multi-level information and reduce noise interference. • An improved flow direction strategy-based adaptive resonance branch is introduced to learn fault-related periodic impulsive features. • Case studies are conducted to validate the efficacy of the developed IFD-MDCN. Rolling bearings are the core components of rotating machinery, and their normal operation is crucial to the entire industrial production. Most existing condition monitoring methods have been devoted to extracting discriminative features from vibration signals that reflect bearing health status information. However, the complex working conditions of rolling bearings often make the periodic impulsive characteristics related to fault information easily buried in noise interferences. Therefore, it is challenging for existing approaches to learning discriminative fault-related features in these scenarios. To address this issue, a novel multibranch CNN named IFD-MDCN is developed in this paper, which represents multibranch denoising convolutional networks (MDCN) with an improved flow direction (IFD) strategy. The main contributions of this work include: (1) designing a multiscale denoising branch to extract multi-level information and reduce noise impact. More specifically, the multiscale denoising branch adopts a Gaussian multi-level noise reduction procedure to represent vibration signals at multiple levels and filter out the noise components, and then it uses a multiscale convolutional module to extract abundant features from these denoised signal representations; (2) establishing an improved flow direction strategy-based adaptive resonance branch to learn periodic impulsive features associated with fault information from vibration signals. Extensive experimental results reveal that the IFD-MDCN outperforms five state-of-the-art approaches, especially in strong noise scenarios. [ABSTRACT FROM AUTHOR]

• An improved form of SWNSE is developed by generalizing the order of the norm. • A design framework for improved Gini indices is established and four specific forms of improved Gini index are proposed. • Improved Gini indices are proven to be sparsity measures. • A series of improved Gini indices are proposed for characterizing repetitive transients. • The improved Gini indices with norm order 3 exhibit better comprehensive performance. Sparsity measures are important and effective tools for accurately characterizing fault features and degradation trends of rotating machinery. In the past few decades, numerous sparsity measures, such as kurtosis, negentropy, Lp/Lq norm, pq-mean, smoothness index and Gini index, have been proposed and introduced for condition monitoring, fault diagnosis and remaining life prediction of rotating machinery. However, it is difficult for traditional sparsity measures to possess strong random impulse resistance and fault impulse discernibility simultaneously. To design robust sparsity measures for repetitive transient characterization and machine condition monitoring, an improved form of the sum of weighted normalized square envelope (SWNSE) is firstly developed by generalizing the order of the norm as a framework for adaptively quantifying the repetitive transients. Under this framework, a rank-dependent generalized Gini index (RDGGI) is proposed, allowing four specific forms of RDGGI to be reasonably constructed by introducing four weight sequence design methods. Theoretical derivation shows that the quantitative indices produced by the four specific forms of RDGGI satisfy at least five of the six attributes of sparsity measures, proving that the newly designed quantitative indices are sparsity measures, and are also scale-invariant and have a limited magnitude range between 0 and 1. Furthermore, a series of improved Gini indices are derived by setting typical norm orders and designing appropriate monotonically decreasing weight sequences, and simulation analysis is conducted to verify their three key properties in characterizing transient features. Finally, the performance of the improved Gini indices in characterizing bearing fault features for condition monitoring is verified using vibration datasets from two bearing run-to-failure experiments. Simulation and experimental results show that the improved Gini indices with norm order 3 can simultaneously possess strong resistance to random impulses and discernibility of fault impulses, demonstrating the superior performance of improved Gini indices in bearing condition monitoring. [ABSTRACT FROM AUTHOR]