Novel Adaptive Search Method for Bearing Fault Frequency Using Stochastic Resonance Quantified by Amplitude-Domain Index

Huang, Dan; Yang, Jianhua; Zhou, Dengji; Litak, Grzegorz

doi:10.1109/tim.2019.2890933

Cited by 58 publications

(26 citation statements)

References 36 publications

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“…The training phase is executed repeatedly until a stopping criterion is reached, such as maximum number of epochs or minimum value of weight vector variation, which is obtained using empirical tests. In the operation phase, each sample at the input is classified by each neuron according to their respective Euclidean distance, given by (7).…”

Section: Self-organizing Mapsmentioning

confidence: 99%

Non‐invasive sound‐based classifier of bearing faults in electric induction motors

Santos

Scalassara

Endo

et al. 2021

IET Science Measure & Tech

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Induction motors play a major role in the industry nowadays due to their simple construction, uncomplicated maintenance, and cost efficiency. As the motor operates for repeated hours, some faults may occur and, depending on the process sensitivity, can cause significant losses to the industrial production. In this context, an alternative method is proposed to detect and classify bearing faults using acoustic emission signals generated by the machines and simple features obtained from them. A pair of condenser microphones was used to acquire these signals and audio feature extraction is performed to obtain time and frequency patterns to characterize healthy and faulty machines. The major differences of the acoustic signals regarding the fault frequency signatures are discussed by analyzing specific peaks observed in their spectra. Several coupled load values and levels of power supply voltage unbalance were considered in the experimental tests, which emulate common situations encountered in industrial environments, obtaining accuracy rates over 97% of success. Finally, a comparison is presented of machine learning techniques for bearing faults classification under different load values and voltage unbalance levels.

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Section: Self-organizing Mapsmentioning

confidence: 99%

Non‐invasive sound‐based classifier of bearing faults in electric induction motors

Santos

Scalassara

Endo

et al. 2021

IET Science Measure & Tech

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“…In view of the operation environment of spindle bearings and their prior knowledge [17], [18], [23]- [25], TDZC index [15] is used for parameters optimization of the UABSR-based filter. Thus, the calculation algorithm of the TDZC index is given as follow:…”

Section: Parameter Selection For Adaptive Uabsr-based Filter and Imentioning

confidence: 99%

“…The weighted power spectrum kurtosis index [23] and spectral power amplification factor [24] can quantify the SR response, without knowing the prior information of bearing failure status. But they are particularly sensitive to the noise in vibration signals [25]. For evaluating the purity of the SR output signal, TDZC index [15] can directly reflect the regularity of the bearing fault signal without knowing the exact frequency of the target signal in advance.…”

Section: Introductionmentioning

confidence: 99%

An Improved Underdamped Asymmetric Bistable Stochastic Resonance Method and its Application for Spindle Bearing Fault Diagnosis

Xia

Lei

et al. 2020

IEEE Access

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High-precision spindle bearing is one of the most critical and vulnerable parts in a motorized spindle. Its unexpected failure may lead to production loss. Stochastic resonance (SR) is a weak signal detection method, which can obtain noise energy in strong background noise and enhance incipient fault characteristics of spindle bearing. Based on the fact that asymmetry can improve the enhancement ability of asymmetric bistable SR in weak feature extraction, we introduce an underdamped well-width asymmetric bistable SR (UABSR) method to the field of bearing fault diagnosis for the first time. However, the engineering application of UABSR can still be limited by two aspects. Firstly, the SNR index can take effect only when the actual fault frequency is obtained in advance, so the UABSR method is at high-cost in real practices. Secondly, an appropriate band-pass filter band range of the bearing faults can hardly be obtained due to the massive impulsive noise in operations. Here an improved UABSR method for spindle bearing fault diagnosis is proposed. Infogram method is used to process and analysis the original vibration signal for resisting the influence from the impulsive noise and obtaining more accurate frequency range of spindle bearing fault. In addition, time domain zero-crossing (TDZC) index, as the index of the improved UABSR method, can directly reflect the fault characteristics of spindle bearings without knowing the accurate fault characteristic frequency in advance. Besides, the Quantum Genetic Algorithms (QGAs) and the fourth-order Runge-Kutta algorithm are combined to simultaneously obtain the optimal system parameter, the asymmetric ratio, the damping factor and the rescaling factor of the improved UABSR model. Comparing the Infogram and original UABSR methods, the improved UABSR method performs better effect in incipient spindle bearing fault diagnosis. INDEX TERMS Spindle rolling bearing, fault diagnosis, underdamped asymmetric bistable stochastic resonance, vibration signal analysis, time domain zero-crossing index.

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“…Bearing defects can be detected by either analyzing the fault frequency spectrum [ 2 ] or pattern recognition [ 3 ]. However, the analysis in [ 4 ] shown that the pattern recognition can give higher accuracy compared to the spectrum approach.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Using a Particle Swarm Optimization-Least Squares Wavelet Support Vector Machine Classifier

Van

Hoang

2020

Sensors

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Bearing is one of the key components of a rotating machine. Hence, monitoring health condition of the bearing is of paramount importace. This paper develops a novel particle swarm optimization (PSO)-least squares wavelet support vector machine (PSO-LSWSVM) classifier, which is designed based on a combination between a PSO, a least squares procedure, and a new wavelet kernel function-based support vector machine (SVM), for bearing fault diagnosis. In this work, bearing fault classification is transformed into a pattern recognition problem, which consists of three stages of data processing. Firstly, a rich information dataset is built by extracting the features from the signals, which are decomposed by the nonlocal means (NLM) and empirical mode decomposition (EMD). Secondly, a minimum-redundancy maximum-relevance (mRMR) method is employed to determine a subset of feature that can provide an optimal performance. Thirdly, a novel classifier, namely LSWSVM, is proposed with the aid of a PSO, to provide higher classification accuracy. The key innovative science of this work is to propropose a new classifier with the aid of an new wavelet kernel type to increase the classification precision of bearing fault diagnosis. The merit features of the proposed approach are demonstrated based on a benchmark bearing dataset and a comprehensive comparison procedure.

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Novel Adaptive Search Method for Bearing Fault Frequency Using Stochastic Resonance Quantified by Amplitude-Domain Index

Cited by 58 publications

References 36 publications

Non‐invasive sound‐based classifier of bearing faults in electric induction motors

Non‐invasive sound‐based classifier of bearing faults in electric induction motors

An Improved Underdamped Asymmetric Bistable Stochastic Resonance Method and its Application for Spindle Bearing Fault Diagnosis

Bearing Fault Diagnosis Using a Particle Swarm Optimization-Least Squares Wavelet Support Vector Machine Classifier

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