Gearbox fault diagnosis using acoustic signals, continuous wavelet transform and adaptive neuro-fuzzy inference system

Parey, Anand; Singh, Amandeep

doi:10.1016/j.apacoust.2018.10.013

Cited by 55 publications

(24 citation statements)

References 29 publications

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“…Wavelet transform is one of the most popular time-frequency transform methods that has the ability to characterize signal characteristics in both time and frequency domain. At present, wavelet transform is widely used in the field of rotating machinery fault diagnosis [48]. However, the decomposition method adopted by wavelet decomposition has poor time resolution in the low frequency band and poor frequency resolution in the high frequency band.…”

Section: Wavelet Packet Analysismentioning

confidence: 99%

A Diagnosis Method for the Compound Fault of Gearboxes Based on Multi-Feature and BP-AdaBoost

Zhang

Jia

et al. 2020

Symmetry

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Gearbox is an important structure of rotating machinery, and the accurate fault diagnosis of gearboxes is of great significance for ensuring efficient and safe operation of rotating machinery. Aiming at the problem that there is little common compound fault data of gearboxes, and there is a lack of an effective diagnosis method, a gearbox fault simulation experiment platform is set up, and a diagnosis method for the compound fault of gearboxes based on multi-feature and BP-AdaBoost is proposed. Firstly, the vibration signals of six typical states of gearbox are obtained, and the original signals are decomposed by empirical mode decomposition and reconstruct the new signal to achieve the purpose of noise reduction. Then, perform the time domain analysis and wavelet packet analysis on the reconstructed signal, extract three time domain feature parameters with higher sensitivity, and combine them with eight frequency band energy feature parameters obtained by wavelet packet decomposition to form the gearbox state feature vector. Finally, AdaBoost algorithm and BP neural network are used to build the BP-AdaBoost strong classifier model, and feature vectors are input into the model for training and verification. The results show that the proposed method can effectively identify the gearbox failure modes, and has higher accuracy than the traditional fault diagnosis methods, and has certain reference significance and engineering application value.

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Section: Wavelet Packet Analysismentioning

confidence: 99%

A Diagnosis Method for the Compound Fault of Gearboxes Based on Multi-Feature and BP-AdaBoost

Zhang

Jia

et al. 2020

Symmetry

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“…Fast Fourier transform (FFT)-based segmentation and acoustic fault identification algorithm were described in the paper [23]. Acoustic signals of gearbox acquired under various fault conditions were analyzed using continuous wavelet transform in the paper [24]. Acoustic signals of bearing defects using an improved one-against-all multiclass support vector machine (OAA-MCSVM) classifier were analyzed [25].…”

Section: Introductionmentioning

confidence: 99%

Detection of Deterioration of Three-phase Induction Motor using Vibration Signals

Głowacz

Kozik

et al. 2019

Measurement Science Review

109

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Nowadays detection of deterioration of electrical motors is an important topic of research. Vibration signals often carry diagnostic information of a motor. The authors proposed a setup for the analysis of vibration signals of three-phase induction motors. In this paper rotor fault diagnostic techniques of a three-phase induction motor (TPIM) were presented. The presented techniques used vibration signals and signal processing methods. The authors analyzed the recognition rate of vibration signal readings for 3 states of the TPIM: healthy TPIM, TPIM with 1 broken bar, and TPIM with 2 broken bars. In this paper the authors described a method of the feature extraction of vibration signals Method of Selection of Amplitudes of Frequencies – MSAF-12. Feature vectors were obtained using FFT, MSAF-12, and mean of vector sum. Three methods of classification were used: Nearest Neighbor (NN), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). The obtained results of analyzed classifiers were in the range of 97.61 % – 100 %.

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“…The first category is order tracking which converts the nonstationary vibration signal into a stationary one. The most widely used methods include order features extraction [9,10], order analysis [11,12] and synchronous averaging [13,14]. These methods usually require the installation of an additional key-phase device to measure the actual speed of the bearings, but it is difficult to implement when the installation of the device is inconvenient.…”

Section: Introductionmentioning

confidence: 99%

Fractional Synchrosqueezing Transformation and its Application in the Estimation of the Instantaneous Frequency of a Rolling Bearing

Liu

et al. 2020

IEEE Access

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The time-frequency energy distribution processed by a short-time Fourier transform can be compressed to the real instantaneous frequency by the synchrosqueezing transformation (SST), which improves the time-frequency energy concentration of the signal. However, there is a large error in the instantaneous frequency estimation of a multicomponent nonpure harmonic signal by the SST. Therefore, a method for determining the instantaneous frequency (IF) of a rolling bearing based on a fractional synchrosqueezing transformation (FRSST) is proposed. First, the theoretical derivation of the FRSST algorithm as a signal processing technique is given and the steps of the IF estimation are presented. Second, the main advantages of the proposed FRSST algorithm are proved. In the comparison of simulation signals, it is verified that the FRSST algorithm has a high time-frequency concentration, is non-fragile to the frequency modulation rate, has noise robustness and has nonsensitivity to the cross-frequency signal. Finally, the FRSST algorithm is applied to the IF estimation of a rolling bearing under rising speed and fluctuated speed, and is compared with the SST based on variational mode decomposition (VMD-SST), the generalized parametric SST (PSST) and polynomial chirplet transform (PCT). The test results show that the estimation error of IF based on the FRSST method is the least for a rolling bearing with the four fault types under rising speed. On average, the estimation error is 2.2180 Hz less than the corresponding error of the VMD-SST and 1.1862 Hz less than the corresponding error of the PSST method. INDEX TERMS Fractional synchrosqueezing transformation, instantaneous frequency, noise robustness, rolling bearing.

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Gearbox fault diagnosis using acoustic signals, continuous wavelet transform and adaptive neuro-fuzzy inference system

Cited by 55 publications

References 29 publications

A Diagnosis Method for the Compound Fault of Gearboxes Based on Multi-Feature and BP-AdaBoost

A Diagnosis Method for the Compound Fault of Gearboxes Based on Multi-Feature and BP-AdaBoost

Detection of Deterioration of Three-phase Induction Motor using Vibration Signals

Fractional Synchrosqueezing Transformation and its Application in the Estimation of the Instantaneous Frequency of a Rolling Bearing

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