A Two-Stage Method Using Spline-Kernelled Chirplet Transform and Angle Synchronous Averaging to Detect Faults at Variable Speed

Wang, Lu; Xiang, Jiawei

doi:10.1109/access.2019.2898884

Cited by 19 publications

(18 citation statements)

References 40 publications

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“…Therefore, it is vitally significant to study the fault diagnosis technology of rolled bearings [1]. The no-cross research model of traditional fault diagnosis has been changed by information fusion, which made it a new hotspot [2][3]. However, there are two inevitable problems in most methods: (1) The feature extraction of raw data is needed before information fusion, which leads to the loss of statistical information in raw data; (2) most researches are limited to the same class information due to the space-time registration problem.…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM

et al. 2020

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For an intelligent detection of bearing failure in rotating machinery, this paper proposed a fault diagnosis method based on a probability box (p-box) and support vector machine (SVM) with a genetic algorithm (GA) algorithm. Firstly, based on vibration signals of the bearing, the different p-boxes are obtained and fused using the evidence theory. Then, the different bearing p-boxes can be classified by adopting SVM model; the GA algorithm is considered to optimize key parameters of the SVM model, i.e., GA-SVM. Finally, experimental results show that total recognition rate of this method is better than that of the traditional feature extraction method, which demonstrates the effectiveness of the current method.

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Section: Introductionmentioning

confidence: 99%

“…Therefore, to ensure that the SVM has a high classification accuracy, it is necessary to select the appropriate 2  and C . The procedure of using GA to optimize2…”

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confidence: 99%

Fault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM

et al. 2020

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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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“…Currently, the vibration monitoring method is the most commonly adopted method to monitor bearing conditions [4]. When the fault first appears, its characteristic signal is very weak [5]. is is because it is overwhelmed by the power of the natural frequency vibrations, transfer modulation, and noise interference.…”

Section: Introductionmentioning

confidence: 99%

“…To calculate the maximum and minimum tness of the ants, formula (26) is used. If formula(26) is not true, then return to step(5) and continue to the next iteration. Otherwise, the iteration is stopped and output the natural elite ant lions E k…”

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confidence: 99%

Bearing Fault Diagnosis Using a Support Vector Machine Optimized by an Improved Ant Lion Optimizer

Yang

Miao

Fanyu

et al. 2019

Shock and Vibration

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Bearing is an important mechanical component that easily fails in a bad working environment. Support vector machines can be used to diagnose bearing faults; however, the recognition ability of the model is greatly affected by the kernel function and its parameters. Unfortunately, optimal parameters are difficult to select. To address these limitations, an escape mechanism and adaptive convergence conditions were introduced to the ALO algorithm. As a result, the EALO method was proposed and has been applied to the more accurate selection of SVM model parameters. To assess the model, the vibration acceleration signals of normal, inner ring fault, outer ring fault, and ball fault bearings were collected at different rotation speeds (1500 r/min, 1800 r/min, 2100 r/min, and 2400 r/min). The vibration signals were decomposed using the variational mode decomposition (VMD) method. The features were extracted through the kernel function to fuse the energy value of each VMD component. In these experiments, the two most important parameters for the support vector machine—the Gaussian kernel parameter σ and the penalty factor C—were optimized using the EALO algorithm, ALO algorithm, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. The performance of these four methods to optimize the two parameters was then compared and analyzed, with the EALO method having the best performance. The recognition rates for bearing faults under different tested rotation speeds were improved when the SVM model parameters optimized by the EALO were used.

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A Two-Stage Method Using Spline-Kernelled Chirplet Transform and Angle Synchronous Averaging to Detect Faults at Variable Speed

Cited by 19 publications

References 40 publications

Fault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM

Fault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM

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

Bearing Fault Diagnosis Using a Support Vector Machine Optimized by an Improved Ant Lion Optimizer

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