Application of Sample Entropy Based LMD-TFPF De-Noising Algorithm for the Gear Transmission System

A Bias Drift Suppression Method Based on ICELMD and ARMA-KF for MEMS Gyros

The applications of Micro-Electro-Mechanical-System (MEMS) gyros in inertial navigation system is gradually increasing. However, the random drift of gyro deteriorates the system performance which restricting the applications of high precision. We propose a bias drift compensation model based on two-fold Interpolated Complementary Ensemble Local Mean Decomposition (ICELMD) and autoregressive moving average-Kalman filtering (ARMA-KF). We modify CELMD into ICELMD, which is less complicated and overcomes the endpoint effect. Further, the ICELMD is combined with ARMA-KF to separate and simplify the preprocessed signal, resulting improved denoising performance. In the model, the abnormal noise is removed in preprocess by 2σ criterion with ICELMD. Then, continuous mean square error (CMSE) and Permutation Entropy (PE) are both applied to categorize the preprocessed signal into noise, mixed and useful components. After abandon the noise components and denoise the mixed components by ARMA-KF, we rebuild the noise suppression signal of MEMS gyro. Experiments are carried out to validate the proposed algorithm. The angle random walk of gyro decreases from 2.4156∘/h to 0.0487∘/h, the zero bias instability lowered from 0.3753∘/h to 0.0509∘/h. Further, the standard deviation and the variance are greatly reduced, indicating that the proposed method has better suppression effect, stability and adaptability.

An Integrated Approach Based on Swarm Decomposition, Morphology Envelope Dispersion Entropy, and Random Forest for Multi-Fault Recognition of Rolling Bearing

Aiming at the problem that the weak faults of rolling bearing are difficult to recognize accurately, an approach on the basis of swarm decomposition (SWD), morphology envelope dispersion entropy (MEDE), and random forest (RF) is proposed to realize effective detection and intelligent recognition of weak faults in rolling bearings. The proposed approach is based on the idea of signal denoising, feature extraction and pattern classification. Firstly, the raw signal is divided into a group of oscillatory components through SWD algorithm. The first component has the richest fault information and perceived as the principal oscillatory component (POC). Secondly, the MEDE value of the POC is calculated and used to describe the characteristics of signal. Ultimately, the obtained MEDE values of various states are trained and recognized by being input as the feature vectors into the RF classifier to achieve the automatic identification of rolling bearing fault under different operation states. The dataset of Case Western Reserve University is conducted, the proposed approach achieves recognition accuracy rate of 100%. In summary, the proposed approach is efficient and robust, which can be used as a supplement to the rolling bearing fault diagnosis methods.

Application of Mutual Information-Sample Entropy Based MED-ICEEMDAN De-Noising Scheme for Weak Fault Diagnosis of Hoist Bearing

In this paper, a novel weak fault features extraction scheme is proposed to extract weak fault features in head sheave bearings of floor-type multi-rope friction mine hoists in strong noise environments. A mutual information-based sample entropy (MI-SE) is proposed to select the effective intrinsic mode function (IMF). The numerical simulation presented in this paper has demonstrated that the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) has a poor performance on weak signals processing under a strong noise background, and fault features cannot be identified clearly. The de-noised signal is decomposed into several IMFs by the ICEEMDAN method, with the help of the minimum entropy deconvolution (MED), which works as a pre-filter to increase the kurtosis value by about 3.2 times. The envelope spectrum of the effective IMF selected by the MI-SE method shows almost all fault features clearly. An analogous experiment system was built to verify the feasibility of the proposed scheme, whose results have also shown that the proposed hybrid scheme has better performance compared with ICEEMDAN or MED on the weak fault features extraction under a strong noise background. This paper provides a novel method to diagnose the weak faults of the slow speed and heavy load rolling bearings in a strong noise environment.

Compound fault extraction method via self-adaptively determining the number of decomposition layers of the variational mode decomposition

Local mean decomposition (LMD) is a self-adaptive method, which has been widely applied to extract early fault signals from bearings. However, mode mixing occurs during the decomposition process. Moreover, in processing signals with strong noise, false frequency components can be generated by variational mode decomposition (VMD). To address these problems, a weak fault extraction method based on VMD is proposed for rolling bearings. This method regards LMD and the combination production function (CPF) as prefilters for VMD. First, LMD is used for denoising the original signal, and then the CPF components that contain the fault information are combined into a new signal. Second, this method determines the decomposition level K of the VMD from the spectral peaks of the recombined signal. Finally, this method decomposes the recombined signal using the VMD. The main contributions of the proposed method are (i) the CPF method is employed for adaptively de-noising, and the power of the fault feature can be improved; (ii) the decomposition level K of the VMD can be determined adaptively. After processing a simulated signal, fault information of the gears and rolling elements is successfully extracted, thereby demonstrating the feasibility of the presented method. Moreover, the feasibility of the proposed method is further demonstrated in a comparison of results with those obtained from the MOMEDA (Multipoint Optimal Minimum Entropy Deconvolution Adjusted) algorithm.

A New Compound Fault Feature Extraction Method Based on Multipoint Kurtosis and Variational Mode Decomposition

Due to the weak entropy of the vibration signal in the strong noise environment, it is very difficult to extract compound fault features. EMD (Empirical Mode Decomposition), EEMD (Ensemble Empirical Mode Decomposition) and LMD (Local Mean Decomposition) are widely used in compound fault feature extraction. Although they can decompose different characteristic components into each IMF (Intrinsic Mode Function), there is still serious mode mixing because of the noise. VMD (Variational Mode Decomposition) is a rigorous mathematical theory that can alleviate the mode mixing. Each characteristic component of VMD contains a unique center frequency but it is a parametric decomposition method. An improper value of K will lead to over-decomposition or under-decomposition. So, the number of decomposition levels of VMD needs an adaptive determination. The commonly used adaptive methods are particle swarm optimization and ant colony algorithm but they consume a lot of computing time. This paper proposes a compound fault feature extraction method based on Multipoint Kurtosis (MKurt)-VMD. Firstly, MED (Minimum Entropy Deconvolution) denoises the vibration signal in the strong noise environment. Secondly, multipoint kurtosis extracts the periodic multiple faults and a multi-periodic vector is further constructed to determine the number of impulse periods which determine the K value of VMD. Thirdly, the noise-reduced signal is processed by VMD and the fault features are further determined by FFT. Finally, the proposed compound fault feature extraction method can alleviate the mode mixing in comparison with EEMD. The validity of this method is further confirmed by processing the measured signal and extracting the compound fault features such as the gear spalling and the roller fault, their fault periods are 22.4 and 111.2 respectively and the corresponding frequencies are 360 Hz and 72 Hz, respectively.

A Hybrid De-Noising Algorithm for the Gear Transmission System Based on CEEMDAN-PE-TFPF

In order to remove noise and preserve the important features of a signal, a hybrid de-noising algorithm based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Permutation Entropy (PE), and Time-Frequency Peak Filtering (TFPF) is proposed. In view of the limitations of the conventional TFPF method regarding the fixed window length problem, CEEMDAN and PE are applied to compensate for this, so that the signal is balanced with respect to both noise suppression and signal fidelity. First, the Intrinsic Mode Functions (IMFs) of the original spectra are obtained using the CEEMDAN algorithm, and the PE value of each IMF is calculated to classify whether the IMF requires filtering, then, for different IMFs, we select different window lengths to filter them using TFPF; finally, the signal is reconstructed as the sum of the filtered and residual IMFs. The filtering results of a simulated and an actual gearbox vibration signal verify that the de-noising results of CEEMDAN-PE-TFPF outperforms other signal de-noising methods, and the proposed method can reveal fault characteristic information effectively.

Recognition of Traveling Surges in HVDC with Wavelet Entropy

Traveling surges are commonly adopted in protection devices of high-voltage direct current (HVDC) transmission systems. Lightning strikes also can produce large-amplitude traveling surges which lead to the malfunction of relays. To ensure the reliable operation of protection devices, recognition of traveling surges must be considered. Wavelet entropy, which can reveal time-frequency distribution features, is a potential tool for traveling surge recognition. In this paper, the effectiveness of wavelet entropy in characterizing traveling surges is demonstrated by comparing its representations of different kinds of surges and discussing its stability with the effects of propagation distance and fault resistance. A wavelet entropy-based recognition method is proposed and tested by simulated traveling surges. The results show wavelet entropy can discriminate fault traveling surges with a good recognition rate.