A simple and fast guideline for generating enhanced/squared envelope spectra from spectral coherence for bearing fault diagnosis

Wang, Dong; Zhao, Xuejun; Kou, Linlin; Qin, Yong; Zhao, Yang; Tsui, Kwok‐Leung

doi:10.1016/j.ymssp.2018.12.055

Cited by 106 publications

(61 citation statements)

References 19 publications

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“…To validate the capability of the TKEO for AM and FM components' demodulation and separation, a simulation study is carried out mathematically. A simulated signal including the pure AM component at the frequency of f a is given in Equation 15; a simulated signal including the pure FM component at the frequency of f f is given in Equation 16; a simulated signal including the AM and FM components simultaneously is expressed in Equation (17). The simulated carrier frequency for the three modulated signals is f c .…”

Section: Simulation Studymentioning

confidence: 99%

“…The analytic signal constructed by HT contains amplitude and phase information, which can be used to reflect the envelope of a signal [12]. Wang et al [17] applied Hilbert transform to demodulate the signal for bearing fault diagnosis. Trajin [18] proposed the Concordia transform to construct a complex vector based on a two-phase current signal from a three-phase AC IM.…”

Section: Introductionmentioning

confidence: 99%

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Modulation Sideband Separation Using the Teager–Kaiser Energy Operator for Rotor Fault Diagnostics of Induction Motors

Wang

Zhen

et al. 2019

Energies

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Broken rotor bar (BRB) faults are one of the most common faults in induction motors (IM). One or more broken bars can reduce the performance and efficiency of the IM and hence waste the electrical power and decrease the reliability of the whole mechanical system. This paper proposes an effective fault diagnosis method using the Teager–Kaiser energy operator (TKEO) for BRB faults detection based on the motor current signal analysis (MCSA). The TKEO is investigated and applied to remove the main supply component of the motor current for accurate fault feature extraction, especially for an IM operating at low load with low slip. Through sensing the estimation of the instantaneous amplitude (IA) and instantaneous frequency (IF) of the motor current signal using TKEO, the fault characteristic frequencies can be enhanced and extracted for the accurate detection of BRB fault severities under different operating conditions. The proposed method has been validated by simulation and experimental studies that tested the IMs with different BRB fault severities to consider the effectiveness of the proposed method. The obtained results are compared with those obtained using the conventional envelope analysis methods and showed that the proposed method provides more accurate fault diagnosis results and can distinguish the BRB fault types and severities effectively, especially for operating conditions with low loads.

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Section: Simulation Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modulation Sideband Separation Using the Teager–Kaiser Energy Operator for Rotor Fault Diagnostics of Induction Motors

Wang

Zhen

et al. 2019

Energies

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“…Tyagi et al [19] aimed to address the problem of traditional envelope detection being highly sensitive to the envelope window, and employs a particle swarm optimization method to select the most optimum envelope window to band pass the vibration signals induced by fault rolling element bearings. In order to determine an informative spectral frequency band for generating an enhanced/squared envelope spectrum, Wang et al [22] proposed a simple and fast guideline and conducted an experiment to highlight its superiority by comparing it with the fast Kurtogram. Tsao et al [23] introduced empirical mode decomposition to select an appropriate resonant frequency band for characterizing the characteristic frequencies of bearing faults by using the envelope analysis subsequently, and the experimental results showed that the proposed method can diagnose the bearing fault types efficiently and correctly.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Bearing Fault Diagnosis Method Based on Characteristics Frequency Ratio

Wang²,

Wang³

et al. 2020

Sensors

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Bearing is a key component of satellite inertia actuators such as moment wheel assemblies (MWAs) and control moment gyros (CMGs), and its operating state is directly related to the performance and service life of satellites. However, because of the complexity of the vibration frequency components of satellite bearing assemblies and the small loading, normal running bearings normally present similar fault characteristics in long-term ground life experiments, which makes it difficult to judge the bearing fault status. This paper proposes an automatic fault diagnosis method for bearings based on a presented indicator called the characteristic frequency ratio. First, the vibration signals of various MWAs were picked up by the bearing vibration test. Then, the improved ensemble empirical mode decomposition (EEMD) method was introduced to demodulate the envelope of the bearing signals, and the fault characteristic frequencies of the vibration signals were acquired. Based on this, the characteristic frequency ratio for fault identification was defined, and a method for determining the threshold of fault judgment was further proposed. Finally, an automatic diagnosis process was proposed and verified by using different bearing fault data. The results show that the presented method is feasible and effective for automatic monitoring and diagnosis of bearing faults.

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“…In recent decades, various methods based on vibration signal have been introduced by researchers for the diagnosis of faulty rolling bearings, such as deconvolution analysis [4,5], wavelet transform [6], spectral kurtosis-based method [7][8][9][10], time-frequency analysis [11,12], and the adaptive signal decomposition methods [13,14]. Among these methods, the adaptive signal decomposition methods have attracted more and more attention and are the hot topic for bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced VMD with the Guidance of Envelope Negentropy Spectrum for Bearing Fault Diagnosis

et al. 2020

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Currently, study on the relevant methods of variational mode decomposition (VMD) is mainly focused on the selection of the number of decomposed modes and the bandwidth parameter using various optimization algorithms. Most of these methods utilize the genetic-like algorithms to quantitatively analyze these parameters, which increase the additional initial parameters and inevitably the computational burden due to ignoring the inherent characteristics of the VMD. From the perspective to locate the initial center frequency (ICF) during the VMD decomposition process, we propose an enhanced VMD with the guidance of envelope negentropy spectrum for bearing fault diagnosis, thus effectively avoiding the drawbacks of the current VMD-based algorithms. First, the ICF is coarsely located by envelope negentropy spectrum (ENS) and the fault-related modes are fast extracted by incorporating the ICF into the VMD. Then, the fault-related modes are adaptively optimized by adjusting the bandwidth parameters. Lastly, in order to identify fault-related features, the Hilbert envelope demodulation technique is used to analyze the optimal mode obtained by the proposed method. Analysis results of simulated and experimental data indicate that the proposed method is effective to extract the weak faulty characteristics of bearings and has advantage over some advanced methods. Moreover, a discussion on the extension of the proposed method is put forward to identify multicomponents for broadening its applied scope.

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A simple and fast guideline for generating enhanced/squared envelope spectra from spectral coherence for bearing fault diagnosis

Cited by 106 publications

References 19 publications

Modulation Sideband Separation Using the Teager–Kaiser Energy Operator for Rotor Fault Diagnostics of Induction Motors

Modulation Sideband Separation Using the Teager–Kaiser Energy Operator for Rotor Fault Diagnostics of Induction Motors

An Automatic Bearing Fault Diagnosis Method Based on Characteristics Frequency Ratio

An Enhanced VMD with the Guidance of Envelope Negentropy Spectrum for Bearing Fault Diagnosis

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