Strong White Gaussian Noise Research Articles

A Reinforced Noise Resistant Correlation Method for Bearing Condition Monitoring

Condition monitoring plays a significant role in guaranteeing the reliability and safety of rotating machinery, which aims to detect an incipient fault and assess the degradation tendency. The construction of a health index (HI) is a crucial step to realize above tasks. At present, kurtosis, crest factor, and so on have been recognized as popular HIs to depict the operating condition. However, shortcomings of these classical HIs still exist: 1) classical HIs are prone to be affected by strong white Gaussian noise; 2) classical HIs are not sensitive to incipient faults. To deal with these two shortcomings, a reinforced noise resistant correlation method is proposed in this paper. Firstly, a new signal is constructed using the steps of segmenting and averaging to suppress the interference of noise. Then, a novel correlation function is used to get the hidden period. The proposed HI is constructed based on the discrete version of this correlation function to increase the sensitivity of incipient faults. Subsequently, theoretical values of the proposed HI under healthy states are investigated. The effectiveness of the method is demonstrated using simulated degradation processes and two accelerated degradation datasets of rolling element bearings. Through comparisons with other classical HIs, the proposed HI can simultaneously suppress the interference of strong noise and detect incipient faults. The comparison results identify the effectiveness of the proposed method in monitoring the condition of rotating machinery. Note to Practitioners—This work aims to provide a novel health index construction method for rotating machinery condition monitoring. The key issues involved in this problem include 1) how to capture the complex relationship between the measured vibration signals and the underlying health condition of rotating machinery; 2) how to suppress the interference of environmental noise. The novelty of this work is that it develops a method for condition monitoring considering the measured signals with strong white Gaussian noise. It properly captures the complex relationship between measured signals and the underlying health condition. There are four main steps to implement this approach: 1) collecting the vibration signals from rotating machinery; 2) constructing new periodic signal to suppress the interference of strong background noise; 3) constructing novel correlation functions to establish the relationship between the vibration signals and the underlying health condition of the rotating machine; 4) modeling the HI. A simulation and two real cases of the bearing degradation process are used to show that the proposed HI can simultaneously suppress the interference of strong noise and detect the incipient faults compared with some classical HI construction methods. In the future, the problem of how to address the measured signals contaminated by non-Gaussian noise and the machinery containing compound faults should be solved to extend this method in a complex system.

A Quantum Weak Signal Detection Method for Strengthening Target Signal Features under Strong White Gaussian Noise

As the noise power increases, the target signal features become less obvious, which leads to the failure of weak signal detection methods. To address this problem, a quantum weak signal detection method, Local Semi-Classical Signal Analysis-Singular Value Decomposition (LSCSA-SVD), for strengthening target signal features under strong white Gaussian noise is proposed. Firstly, the time domain weak signal is quantized by the Schrodinger operator and its discrete spectrum formula. Then, in the quantum domain, the later eigenvalues are used to reconstruct the time domain signal, which can protect and enhance the target signal features. Finally, the difference between signal and noise in the singular value vector is used to further extract the reconstruction signal features. In simulation, the LSCSA-SVD can accurately extract target signals from white Gaussian noise signals with a signal-to-noise ratio (SNR) of −30 dB, which is better than the comparison methods. In the experiment, the weak acceleration sensor signal and the weak signal of the test circuit are successfully extracted. The results show that the LSCSA-SVD can suppress strong noise and improve the SNR.

Detection for Incipient Damages of Wind Turbine Rolling Bearing Based on VMD-AMCKD Method

Incipient damages of wind turbine rolling bearing are very difficult to be detected because of the interference of multi-frequency components and strong ambient noise. To solve this problem, this paper proposes a new detected method named VMD-AMCKD, combining complementary advantages of variational mode decomposition (VMD) and adaptive maximum correlated kurtosis deconvolution (AMCKD). A novel index is proposed to screen out the most sensitive mode containing fault information after VMD decomposition. The mode also can determine a suspectable range for the fault frequency, based on which the optimized range of devolution period T in MCKD can be pre-determined. The Grasshopper optimization algorithm (GOA) is adapted to adaptively select the key parameters in MCKD. The proposed method can successfully diagnose the simulated signal mixed with strong white Gaussian noise. Its robustness is further proven by the diagnosis for three different types of experimental signal from CWRU bearing data center. Finally, the VMD-AMCKD is applied to detect incipient damages of rolling bearings in a laboratory wind turbine.

A Noise Resistant Correlation Method for Period Detection of Noisy Signals

This paper develops a novel method called the noise resistant correlation method for detecting the hidden period from the contaminated (noisy) signals with strong white Gaussian noise. A novel correlation function is proposed based on a newly constructed periodic signal and the contaminated signal to effectively detect the target hidden period. In contrast with the conventional autocorrelation analysis (AUTOC) method, this method demonstrates excellent performance, especially when facing strong noise. Fault diagnoses of rolling element bearings and gears are presented as application examples and the performance of the proposed method is compared with that of the AUTOC method.

Fault Discrimination Using Synchronized Sequence Measurements under Strong White Gaussian Noise Background

In this paper on strong white Gaussian noise background based on discriminant analysis theory, we mainly adopt synchronized sequence measurements of PMU to search for laws of electrical quantities marked changes. We have developed a method to quickly and accurately discriminate fault components and fault sections. The research has shown that the fault discrimination by discriminant analysis theory is effective and reliable. Under the interference of strong random white Gaussian noise, discriminant analysis still has high redundancy.