Detection of rail local defects using in-service trains

Various faults inevitably occur in mechanical systems and may result in unexpected failures. Hence, fault detection is critical to reduce unscheduled downtime and costly breakdowns. Empirical mode decomposition (EMD) is an adaptive time-frequency domain signal processing method, potentially suitable for nonstationary and/or nonlinear processes. However, the EMD method suffers from several problems such as mode mixing, defined as intrinsic mode functions (IMFs) with incorrect scales. In this paper, an ensemble noise-reconstructed EMD method is proposed to ameliorate the mode mixing problem and denoise IMFs for enhancing fault signatures. The proposed method defines the IMF components as an ensemble mean of EMD trials, where each trial is obtained by sifting signals that have been reconstructed using the estimated noise present in the measured signal. Unlike traditional denoising methods, the noise inherent in the input data is reconstructed and used to reduce the background noise. Furthermore, the reconstructed noise helps to project different scales of the signal onto their corresponding IMFs, instrumental in alleviating the mode mixing problem. Two critical issues concerned in the method, i.e., the noise estimation strategy and the number of EMD trials required for denoising are discussed. Furthermore, a comprehensive noise-assisted EMD method is proposed, which includes the proposed method and ensemble EMD (EEMD). Numerical simulations and experimental case studies on accelerometer data collected from an industrial shaving process are used to demonstrate and validate the proposed method. Results show that the proposed method can both detect impending faults and isolate multiple faults. Hence, the proposed method can act as a promising tool for mechanical fault detection.

Application of the EEMD method for distinction and suppression of motion-induced noise in grounded electrical source airborne TEM system

Recently, ensemble empirical mode decomposition (EEMD) method has been developed for non-linear and non-stationary signal analysis. The method can work on nature signals (non-linear and nonstationary signals) and reduce the speckle noise. With the EEMD method, the signal is decomposed into several intrinsic mode functions (IMFs) and the frequencies of IMFs are arranged in decrease order (high to low) after the EEMD processing. The scaling mode of the EEMD method is similar to wavelet transform, but the signal resolutions in different frequency domains do not decrease by down-sampling. There are a large population and a developed economy in Weihe watershed, the disasters of droughts and floods caused by the autumn precipitation (here is precipitation in September and October) less or more than normal cause great loss and serious influence. In this paper, we propose the EEMD method to decompose the autumn precipitation series in the Weihe river basin during last 50 years into several IMFs, then extract the information including in the precipitation series and get the characteristics of multi-scales. The result shows that it is well response to the autumn precipitation series in the Weihe river basin and to the abrupt climate change in late 1970s and early 1980s of last century. The response appears earlier for high time scales than for low time scales In addition, the expression of the response for high time scales is the form of variability, but it is the amplitude of variability for low time scales.

A comparative study between Empirical Wavelet Transforms and Empirical Mode Decomposition Methods: Application to bearing defect diagnosis

Sudden cardiac death prediction based on the complete ensemble empirical mode decomposition method and a machine learning strategy by using ECG signals

Understanding spatial and temporal changes of seasonal sea level cycles is important because of direct influence on coastal systems. The annual sea level cycle is substantially larger than semi-annual cycle in most parts of the ocean. Ensemble empirical mode decomposition (EEMD) method has been widely used to study tidal component, long-term sea level rise, and decadal sea level variation. In this work, EEMD is used to analyze the observed monthly sea level anomalies and detect annual cycle characteristics. Considering that the variations of the annual sea level variation in the Northeast Pacific Ocean are poorly studied, the trend and characteristics of annual sea level amplitudes and related mechanisms in the North Pacific Ocean are investigated using long-term tide gauge records covering 1950–2016. The average annual amplitude of coastal sea level exhibits interannual-to-decadal variability within the range of 14–220 mm. The largest value of ~174 mm is observed in the west coast of South China Sea. In the other coastal regions of North Pacific Ocean, the mean annual amplitude is relatively low between 77 and 124 mm for the western coast and 84 and 87 mm for the eastern coast. The estimated trend values for annual sea level amplitudes in the western coastal areas of South China Sea and Northeast Pacific Ocean have statistically decreased over 1952–2014 with a range of −0.77 mm·yr−1 to −0.11 mm·yr−1. Our results suggested that the decreasing annual amplitude in the west coast of South China Sea is in good agreement with the annual mean wind stress associated with the Pacific Decadal Oscillation (PDO). This wind phenomenon also explains the temporal variations of annual sea level amplitude in Northeast Pacific Ocean, especially the high correlations since 1980 (R = 0.61−0.72).

In this paper a modified ensemble empirical mode decomposition (EEMD) method is presented, which is named winning-EEMD (W-EEMD). Two aspects of the EEMD, the amplitude of added white noise and the number of intrinsic mode functions (IMFs), are discussed in this method. The signal-to-noise ratio (SNR) is used to measure the amplitude of added noise and the winning number of IMFs (which results most frequency) is used to unify the number of IMFs. By this method, the calculation speed of decomposition is improved, and the relative error between original data and sum of decompositions is reduced. In addition, the feasibility and effectiveness of this method are proved by the example of the oceanic internal solitary wave.

Envelope modified versions of the empirical mode decomposition (EMD) method such as the B‐spline interpolation‐based EMD (B‐EMD) method and cardinal spline interpolation‐based EMD (C‐EMD) method have been proposed recently for purpose of improving its effectiveness. To shed further light on their performance, the behaviours of these EMD‐type methods in the presence of white Gaussian noises are investigated in this study based on extensive numerical experiments. Similarly to the EMD method, it turns out that the envelope modified EMD methods also act as filter banks essentially. However, the spectra among the first several intrinsic mode functions of the B‐EMD method have fewer overlaps than those of the EMD and C‐EMD methods, which indicate that the B‐EMD method has a better ability to alleviate the mode mixing problem for signals with higher frequencies. On the other hand, the C‐EMD method is shown to perform better than the EMD and B‐EMD methods on separating tones with lower frequencies.

<div class="section abstract"><div class="htmlview paragraph">Knock threshold detection is the key of closed loop control of ignition in gasoline engine, and it is also the difficult point in knock measurement. In this paper, an investigation of knock detection in turbocharged gasoline engine using bispectrum slice and ensemble empirical mode decomposition (EEMD) based on the engine cylinder head vibration signals is presented. By adding some finite amplitude Gaussian white noises to the signal, EEMD keeps the signal continuous in different time span, and therefore the mode mixing inhering in the classical empirical mode decomposition (EMD) method is alleviated. Power spectrum density (PSD) estimation is used to determine the band range of the resonance frequency generated by knock component. EEMD is used to decompose the original signals, the time-frequency characteristics of the Intrinsic Mode Functions (IMF) are analyzed using Continues Wavelet Transform (CWT) due to its excellent time-frequency resolution. Then bispectrum as a post processing method is adopted to process IMF which contains knock feature information. The diagonal slice of bispectrum is computed to extract the non-linear feature deriving from the quadratic phase coupling, as well as the knock characteristic frequencies. This method is endowed with characteristics of avoiding model mixing, suppressing Gaussian white noise, detecting the nonlinear coupling feature. And knock characteristic information, especially for light knock detonation can be successfully extracted. Derived from the above method, a novel knock intensity factor has been developed and the relevance of the proposed criterion for characterizing different levels of knock has been investigated.</div></div>

The Saemangeum Sea Dike, completed in 2011 in South Korea, is the longest sea dike in the world. One of the most pressing issues of today, climate change leads to sea-level rise that could increase the risk of sea dike overflow. However, what impact climate change will have on the Saemangeum Sea Dike has not been verified yet. Here, we estimate the impacts of sea-level rise on the Saemangeum Sea Dike, using the Linear Regression Analysis method, the Continuous Wavelet Transform (CWT) method, and the Ensemble Empirical Mode Decomposition (EEMD) method. The sea level data of the Saemangeum area over the last 30 years were analyzed, and the results showed that the maximum rate of sea-level rise of the Saemangeum Sea Dike is at 2.51 mm/year when the EEMD method was applied. It means that the impact of sea-level rise on the Saemangeum sea dike is reasonably low because the sea level rise will exceed the freeboard after 366 years, so there is currently no clear sign that it poses a problem.

Variations in leaf area index (LAI) are critical to research on forest ecosystem structure and function, especially carbon and water cycle, and their responses to climate change. Using the ensemble empirical mode decomposition (EEMD) method and global inventory modeling and mapping studies (GIMMS) LAI3g dataset from 1982 to 2010, we analyzed the nonlinear feature and spatial difference of forest LAI variability over China for the past 29years in this paper. Results indicated that the national-averaged forest LAI was characterized by quasi-3- and quasi-7-year oscillations, which generally exhibited a rising trend with an increasing rate. When compared with 1982, forest LAI change by 2010 was more evident than that by 1990 and 2000. The largest increment of forest LAI occurred in Central and South China, while along the southeastern coastal areas LAI increased at the fastest pace. During the study period, forest LAI experienced from decrease to increase or vice versa across much of China and varied monotonically for only a few areas. Focusing on regional-averaged trend processes, almost all eco-geographical regions showed continuously increasing trends in forest LAI with different magnitudes and speeds, other than tropical humid region and temperate humid/subhumid region, where LAI decreased initially and increased afterwards.

Feed-axis gearbox condition monitoring using built-in position sensors and EEMD method

A distributed optical fiber acoustic sensing system (DAS) based on phase-sensitive optical time domain reflectometry ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula> -OTDR) can detect the vibration information along an optical fiber and has been widely used for various engineering detections; however, the high-coherence detection light source used in a DAS will cause coherent fading in the fiber, leading to system fading noise and affecting the monitoring of the target vibration events. In order to solve the problem of the fading noise in the DAS, this study considered a method of suppressing the fading noise based on the genetic algorithm (GA) and variational mode decomposition (VMD). Furthermore, the parameter selection problem in the VMD method is improved by introducing the GA. By introducing sample entropy as a fitness function and combining it with the GA, this method adaptively searches for the optimal decomposition parameters by iterating the minimum value of the fitness function according to the type of input signal. Then, it decomposes the noisy DAS data into multiple intrinsic mode functions (IMFs), and the permutation entropy and time-frequency domain information of each component were used to extract the effective IMF components for signal reconstruction to obtain the denoised signal. Experimental results show that compared with empirical mode decomposition (EMD), ensemble EMD (EEMD), and complete EEMD (CEEMD) methods, the proposed method can eliminate the mode aliasing problem, effectively suppress the fading noise of the DAS, improve the signal-to-noise ratio, and reduce the root-mean-square error. It is, thus, possible to more accurately monitor vibration events, improving the practical application value of the DAS.

대부분의 철강산업 기계 등에 설치되어 사용되는 선회베어링은 교체를 위한 정확한 정비계획이 필요하기 때문에 저속회전체의 선회베어링에 대한 진동 상태감시가 매우 중요하게 되었다. 지금까지 음향방출(AE)법이 저속베어링의 상태감시에 가장 많이 사용되는 기술이고 몇몇의 경우는 진동을 사용한다. 음향방출을 사용하는 일반적인 이유는 저속에서 구름요소와 결함위치 사이의 충격에 의하여 발생되는 신호가 약하고 때때로 노이즈나 다른 간섭 주파수에 결함신호가 묻혀 검출이 어렵기 때문이다. 따라서 쉽게 특정 결함에 대한 결함주파수의 동정을 위하여 몇몇 연구자들은 충격에너지를 증가시키기 위하여 인위적으로 미리 정해진 길이, 넓이와 깊이의 결함을 베어링의 내, 외부 레이스에 인가하기도 한다. 이 논문에서는 15 rpm에서 운전하는 저속 선회베어링의 진동신호에 EMD와 EEMD를 적용하였고 논문에서 사용한 진동결함 신호는 국내 산업체에서 공급받은 것이다. 이 논문에서는 베어링결함 주파수 동정을 위하여 EEMD를 사용하여 결함신호의 FFT처리 결과를 입증하고 설명하였다.

The electrical energy supply relies on the satisfactory operation of insulators. The ultrasound recorded from insulators in different conditions has a time series output, which can be used to classify faulty insulators. The random convolutional kernel transform (Rocket) algorithms use convolutional filters to extract various features from the time series data. This paper proposes a combination of Rocket algorithms, machine learning classifiers, and empirical mode decomposition (EMD) methods, such as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), empirical wavelet transform (EWT), and variational mode decomposition (VMD). The results show that the EMD methods, combined with MiniRocket, significantly improve the accuracy of logistic regression in insulator fault diagnosis. The proposed strategy achieves an accuracy of 0.992 using CEEMDAN, 0.995 with EWT, and 0.980 with VMD. These results highlight the potential of incorporating EMD methods in insulator failure detection models to enhance the safety and dependability of power systems.