Particle Swarm Optimization Fractional Slope Entropy: A New Time Series Complexity Indicator for Bearing Fault Diagnosis

Li, Yuxing; Mu, Lingxia; Gao, Peiyuan

doi:10.3390/fractalfract6070345

Cited by 42 publications

(28 citation statements)

References 30 publications

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“…In 2021, fluctuation-based reverse DE (FEDE) was presented by combining the fluctuation and distance information of FDE and RDE. In addition, with the development of entropy theory, some new complexity indicators have been proposed and applied to the field of fault diagnosis and underwater acoustics, such as bubble entropy (BE), slope entropy (SE), and their improved versions [17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Four Nonlinear Dynamic Characteristics in Feature Extraction of Bearing Fault Diagnosis

Zhang

Liang

2022

Journal of Sensors

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To effectively extract the fault features of bearing signals in complex environments, four common nonlinear dynamic characteristics are used to analyze and compare the simulated signals and measured bearing fault signals in this paper, including Lempel-Ziv complexity (LZC), dispersion entropy (DE), Lyapunov exponent (LE), and fractal dimension (FD). Three groups of simulation experiments were carried out, and the simulation results show that all nonlinear dynamic characteristics can reflect the complexity change trend of MIX signal; compared with LZC and LE, DE and FD can better reflect the frequency change of amplitude modulated trend of chirped signal; only DE can reflect the amplitude change trend of amplitude modulated chirped signal. The experiments of feature extraction and classification are carried out for bearing fault signals. It concluded that compared with LZC, LE, and DE, FD has better stability and classification performance with the minimum standard deviation and the highest recognition rate.

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Section: Introductionmentioning

confidence: 99%

A Comparative Study of Four Nonlinear Dynamic Characteristics in Feature Extraction of Bearing Fault Diagnosis

Zhang

Liang

2022

Journal of Sensors

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“…Slopen is an algorithm that can characterize the complexity of a time series. It is primarily based on single-threshold and symbolic patterns, where every symbol is largely determined by the distinction between consecutive samples of the input time series [ 31 , 32 ]. The specific steps of the slope entropy algorithm are as follows: Step 1: Given a time series

, the extracted sequences are

, …,

, where the embedded dimension is m and

.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Optimized Ship-Radiated Noise Feature Extraction Approaches Based on CEEMDAN and Slope Entropy

Tang

Jiao

2022

Entropy

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Slope entropy (Slopen) has been demonstrated to be an excellent approach to extracting ship-radiated noise signals (S-NSs) features by analyzing the complexity of the signals; however, its recognition ability is limited because it extracts the features of undecomposed S-NSs. To solve this problem, in this study, we combined complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to explore the differences of Slopen between the intrinsic mode components (IMFs) of the S-NSs and proposed a single-IMF optimized feature extraction approach. Aiming to further enhance its performance, the optimized combination of dual-IMFs was selected, and a dual-IMF optimized feature extraction approach was also proposed. We conducted three experiments to demonstrate the effectiveness of CEEMDAN, Slopen, and the proposed approaches. The experimental and comparative results revealed both of the proposed single- and dual-IMF optimized feature extraction approaches based on Slopen and CEEMDAN to be more effective than the original ship signal-based and IMF-based feature extraction approaches.

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“…Particle swarm optimization is also used to optimize the parameters, which can improve the prediction ability and performance of the model [35]. However, particle swarm optimization is easy to fall into the problem of local optimization, so combining the AGA algorithm can expand the particle search space, increase the diversity of the population, and make the model performance better and the prediction accuracy higher.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Detection of Weak Pulse Signal in Chaotic Noise Based on Improved Brain Emotional Learning Model and PSO-AGA

Chen

Zhao

et al. 2022

Journal of Sensors

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A model for detecting weak pulse signals in chaotic noise was proposed. Firstly, based on the short-term predictability of chaotic signals, according to Takens’s theorem, the phase space of observed signal was reconstructed. Then, an improved brain emotional learning (BEL) model combined with PSO-AGA was proposed to predict chaotic signals, and the one-step prediction error was obtained. In order to optimize the parameters of the BEL model, an algorithm named PSO-AGA combined with particle swarm optimization and adaptive genetic algorithm was adopted to achieve the balance of global search and local search capabilities. Finally, the hypothesis testing method was used to detect whether there existed the pulse signal from the one-step prediction error. The experiments simulated the Lorenz system and the magnetic storm loop current system. In the Lorenz system, the MAD of BEL-PSO-AGA, BP-NN-PSO-AGA, and Wavelet-NN-PSO-AGA were 0.0022, 0.0142, and 0.0076; the MSE were 8.95 × 1 0 − 6 , 0.00034, and 0.00016; the RMSE were 0.0029, 0.0187, and 0.0128; the running times were 410 s, 792 s, and 721 s; the ACC were 0.999, 0.972, and 0.997; the F1 were 0.984, 0.423, and 0.878. It could be seen that the BEL model had better performance, shorter running time and higher values of the ACC and F1, indicated that the BEL model ran faster and had a better predictive effect. The MAD of BEL-PSO-AGA, BEL-WOA, BEL-AGA, and BEL-PSO were 0.0022, 0.0065, 0.0135, and 0.0071; the MSE were 8.95 × 1 0 − 6 , 0.00013, 0.00029, and 0.00014; the RMSE were 0.0029, 0.0115, 0.0173, and 0.0119; the ACC were 0.999, 0.992, 0.990, and 0.997; the F1 were 0.984, 0.733, 0.451, and 0.878. This indicated that the PSO-AGA also had better performance and higher prediction accuracy. In the magnetic storm loop current system, the experimental results were similar to the Lorenz experiment, which also indicated that the BEL-PSO-AGA model was better. To sum up, the detection results of simulations showed that the proposed model and algorithm could effectively detect weak pulse signals from the chaotic noise.

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Particle Swarm Optimization Fractional Slope Entropy: A New Time Series Complexity Indicator for Bearing Fault Diagnosis

Cited by 42 publications

References 30 publications

A Comparative Study of Four Nonlinear Dynamic Characteristics in Feature Extraction of Bearing Fault Diagnosis

A Comparative Study of Four Nonlinear Dynamic Characteristics in Feature Extraction of Bearing Fault Diagnosis

Optimized Ship-Radiated Noise Feature Extraction Approaches Based on CEEMDAN and Slope Entropy

Detection of Weak Pulse Signal in Chaotic Noise Based on Improved Brain Emotional Learning Model and PSO-AGA

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