Kuo Chi

Stochastic resonance is like a nonlinear filter to detect the weak bearing fault-induced impulses that submerged in strong noises. Signal-to-noise ratio (SNR) is often used as the index to evaluate the SR output, but the fault characteristic frequency (FCF) must be known in order to calculate SNR. A novel bearing fault diagnosis method called synthetic quantitative index-based adaptive underdamped stochastic resonance (SQI-AUSR) is proposed. The synthetic quantitative index (SQI) is composed of power spectrum kurtosis, kurtosis, margin index, and correlation coefficient. The SQI is independent of FCF, which avoids the limitation that the calculation of SNR must know the FCF. Numeric simulations and two case studies of bearing faults are carried out. The results show that (1) the SQI is more effective than other proposed indexes such as correlation coefficient and weight power spectrum kurtosis and (2) the proposed SQI-AUSR is effective for bearing fault diagnosis and is better than SNR-AOSR.

Effective filtering and noise reduction, feature extraction and fault diagnosis, and prognostics technology are important to Prognostics and Health Management (PHM) of equipment. Therefore, a multifeature fusion fault diagnosis method based on the combination of quadratic filtering and QPSO-KELM algorithm is proposed. In the quadratic filtering, stable filtering can reduce the impact of noise and fast-kurtogram can filtrate fault frequency bands with rich fault information. Then, the time-domain, frequency-domain, and time-frequency parameters of the secondary filter signal are extracted. MSSST was used to analyze the filtered signal, and the time-frequency image was obtained. The time-frequency parameter was extracted from the time-frequency image by 2DPCA, and all the extracted parameters are taken as the fusion fault feature of the gearbox. Finally, the fault feature parameters are taken as the training sample and testing sample of QPSO-KELM for training and testing to achieve the purpose of fault diagnosis. The experimental results show that the proposed method can effectively filter the noise, complete the fault mode identification of gearbox, and improve the fault diagnosis accuracy better than other methods.

Multi-scale noise tuning parameter-induced stochastic resonance method (MNTPSR-1/f ^β) is proposed for planetary gearbox diagnosis. This method is divided into two steps. The first step is called multi-scale noise tuning (MNT) and the second step called parameter-induced stochastic resonance (PSR). MNT based on wavelet packet transformation tunes the noise to an approximate 1/f ^β noise which can improve PSR effect. PSR enhances the weak high-frequency fault characteristic signal. Some simulations are designed for analysing MNTPSR-1/f ^β performance such as the MNT effect, anti-noise capability, and optimal frequency response. A fault diagnosis strategy for the planetary gearbox is proposed. To validate the proposed method, two pre-planting fault tests of sun gear and ring gear are carried out. For comparison, PSR without MNT, MNTPSR-1/f and MNTPSR-1/f ^β are used to analyse the fault signals. The simulations and case validations prove that our proposed method is the most effective. Abbreviations: wavelet packet transformation (WPT), stochastic resonance (SR)

Bearing is among the most widely used components in rotating machinery. Its failure can cause serious economic losses or even disasters. However, the fault-induced impulses are weak especially for the early failure. As to the bearing fault diagnosis, a novel bearing diagnosis method based on scale-varying fractional-order stochastic resonance (SFrSR) is proposed. Signal-to-noise ratio of the SFrSR output is regarded as the criterion for evaluating the stochastic resonance (SR) output. In the proposed method, by selecting the proper parameters (integration step [ ], amplitude gain [ ] and fractional-order [ ]) of SFrSR, the weak fault-induced impulses, the noise and the potential can be matched with each other. An optimal fractional-order dynamic system can be generated. To verify the proposed SFrSR, numerical tests and application verification are conducted in comparison with the traditional scale-varying first-order SR (SFiSR). The results prove that the parameters [ ] and [ ] affect the SFrSR effect seriously and the proposed SFrSR can enhance the weak signal while suppressing the noise. The SFrSR is more effective for bearing fault diagnosis than SFiSR.

Due to the fast increase of mobile traffic, most mobile network operators face the congestion issue in licensed spectrum bands. Several telecommunication vendors and operators propose to expand LTE service to the unlicensed spectrum bands to relieve the traffic congestion. However, LTE in unlicensed spectrum may interfere with Wi-Fi communications in the same bands and cause significant decrease in the quality of service of Wi-Fi. In this paper, we propose a novel mechanism that enables negotiations between two different wireless technologies (Wi-Fi and LTE), which ensures fair spectrum sharing between Wi-Fi and LTE-Unlicensed (LTE-U) in the same bands. We formulate the co-existence of Wi-Fi and LTE-U as a constrained optimization problem, and we solve the problem. We evaluate the performance of the proposed scheme via NS-3 simulations. The simulation results show that our approach can effectively improve the overall channel utilization and reduce the interference between Wi-Fi and LTE-U.

A novel version of multi-class classification method based on fruit fly optimization algorithm (FOA) and relevance vector machine (RVM) is proposed. The one-against-one-against-rest (OAOAR) classification model based on the traditional one-against-one (OAO) and one-against-rest (OAR) algorithm is aimed at combining the advantages of them and translates the multi-class classification problem into multiple three-classification problems to accelerate the running speed with high classification precision. With RVM applied as the binary classifier, the optimal parameter values of RVM kernel function are determined by FOA. Theoretical analysis and computational comparisons on publicly available datasets both indicate that the proposed approach outperforms in terms of diagnosis accuracy and running time with more model sparsity and higher diagnosis efficiency.

It is difficult to estimate the parameters of Weibull mixtures precisely when using these distributions to analyze the reliability of equipment parts. As to this problem, an optimization model of the Weibull mixtures based on the Bayes theorem is proposed, and the cuckoo search is used to solve the optimization model. An case makes the diesel injector as the object of study and the two-component Weibull distribution as the life distribution. Three algorithms including cuckoo search (CS), particle swarm optimization (PSO) and genetic algorithm (GA) are used to solve the optimization model, and their solving results are compared. The result shows that the cuckoo search is the best algorithm of the three in solution efficiency and convergence performance.