A Periodic Potential Underdamped Stochastic Resonance Method and Its Application for Gear Fault Diagnosis

Li, Zhixing; Liu, Xiandong; He, Tian; Shan, Yingchun

doi:10.1109/access.2019.2943191

Cited by 12 publications

(6 citation statements)

References 37 publications

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“…proposed a periodic potential underdamping stochastic resonance (PPUSR) in the paper and used PPUSR to extract the characteristics of the gearbox vibration signals. The results showed that the proposed method can detect gear wear faults and tooth broken faults [14]. In the research of Li Y.B.…”

Section: Introductionmentioning

confidence: 92%

A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network

Luo

Liu

2020

IEEE Access

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Among the various maintenance technologies of wind turbines, online fault prediction technology is a kind of more cost-effective and reliable method. It may also be the most promising method for wind turbines with potential mechanical faults. SCADA data-based online condition monitoring technology has become a hot spot in current researches. Therefore, a novel fault prediction method based on the Pair-Copula model is proposed in this study. First, the conditional mutual information method is introduced to screen out useful variables from a number of variables. Then aiming at the limitation that the conventional Copula model can only deal with two-dimensional variables, the Pair-Copula model is introduced. In addition, the complexity of the prediction model and the dimension of the input variables are greatly reduced by the Pair-Copula model. So, the BP neural network is selected to complete the prediction model. A combined model based on BP neural network and Pair-Copula model is proposed. In order to solve the problem that the conventional Pair-Copula model cannot process real-time data which must be required in fault prediction, a kind of improved Pair-Copula model combined with the kernel density estimation is used to calculate the real-time data. Finally, the proposed method is validated with real data from a 1.5 MW wind turbine, and the effectiveness is confirmed.

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Section: Introductionmentioning

confidence: 92%

A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network

Luo

Liu

2020

IEEE Access

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“…Then, QGAs are used to optimize the parameters pairs (a, b, α, β, m) of the improved UABSR method with the asymmetric bistable potential shown in Eq. (7). According the improved UABSR method in Section 2.4, the adaptive diagnosis result of the proposed method is displayed in Fig.…”

Section: Simulation Verificationmentioning

confidence: 99%

“…In addition, the fault location of the spindle bearing and the range of the fault characteristic frequency are usually unknown in advance. For above reasons, it is necessary to effectively diagnose the fault and estimate the running state of spindle bearings, so as to recognize the principle of spindle bearing operation, optimize the bearing design, avoid unscheduled downtime and reduce the maintenance cost [4]- [7].…”

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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“…Other works combine acoustic and vibration measurements for gear fault diagnosis [19]. Li et al propose the technique of periodic potential underdamped stochastic resonance to provide gear fault diagnosis from acoustic measurements in environments of high environmental noise [20]. Techniques of unsupervised machine learning have been applied to the detection of gear box bearing faults in environments of heavy background noises [21].…”

Section: Analysis Of the Current System And Specification Of The Smentioning

confidence: 99%

A Case about the Upgrade of Manufacturing Equipment for Insertion into an Industry 4.0 Environment

García-Garza

Ahuett–Garza

López

et al. 2019

Sensors

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Industry 4.0 is a synonym for the confluence of technologies that allows the integration of information technology, data science, and automated equipment, to produce smart industrial systems. The process of inserting new technologies into current conventional environments involves a wide range of disciplines and approaches. This article presents the process that was followed to identify and upgrade one station in an industrial workshop to make it compatible with the more extensive system as it evolves into the Industry 4.0 environment. An information processing kit was developed to upgrade the equipment from an automated machine to an Industry 4.0 station. The kit includes a structure to support the sensor and the data processing unit; this unit consisted of a minicomputer that records the data, graded the performance of the components, and sent the data to the cloud for storage, reporting, and further analysis. The information processing kit allowed the monitoring of the inspection system and improved the quality and speed of the inspection process.

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A Periodic Potential Underdamped Stochastic Resonance Method and Its Application for Gear Fault Diagnosis

Cited by 12 publications

References 37 publications

A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network

A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network

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

A Case about the Upgrade of Manufacturing Equipment for Insertion into an Industry 4.0 Environment

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