Morphological Undecimated Wavelet Research Articles

Multioperator Morphological Undecimated Wavelet for Wheelset Bearing Compound Fault Detection

Wheelset bearing compound faults are observed as impulses in the vibration measurements but immersed in noise. The morphological undecimated wavelet (MUW) is an effective tool for recovering fault-related impulses from vibration mixtures. To date, the reported MUWs adopt an identical morphological operator in each level of decomposition without exception. However, effectively capturing signal signatures by repeatedly using a noise elimination operator, an impulse extraction operator, or even the product of two operators is unattainable. Spurred by this deficiency, in this paper, we propose a multi-operator MUW (MOMUW). A three-level structure: noise reduction, impulse extraction, and further denoising and feature enhancement, is developed in the MOMUW, and different morphological operators are designed for each decomposition level to more purposefully denoise and extract impulse features. The developed MOMUW is applied to measured wheelset bearing vibration data, with the results demonstrating that it can accurately detect wheelset bearing compound faults. Compared with the reported MUWs, the proposed approach presents superior performance. Furthermore, MUWs with varying operators and levels are analyzed and compared. Their success and failure in bearing fault diagnosis are interpreted and discussed, laying a theoretical foundation for constructing new MUWs.

A travelling wave based primary and backup protection for MMC-MTDC transmission system using morphological un-decimated wavelet scheme

The primary protection speed requirement for the HVDC grid is much faster as compared to the conventional HVAC grid. This research article proposes a travelling wave-based primary and backup protection scheme for the multi terminal HVDC transmission system using the morphological un-decimated wavelet decomposition signal processing technique. Depending on the polarity and arrival time information of the first incident travelling wave at both DC transmission line terminals, the fault is identified to be either in the internal zone, forward external zone, or backward external zone of the DC transmission line. Each DC transmission line is covered by its own internal zone as well as forward & backward external zone of its neighbouring line. The internal zone fault identification of the protection scheme sends the primary relay trip signals to associated hybrid DC circuit breakers. But if the primary protection relay fails, then the neighbouring DC substation protection system identifies the fault either in forward external or backward external zone. Then it sends a remote backup trip signal to the DC circuit breaker in the forward or backward external direction with a coordinated time delay.

An iterative morphological difference product wavelet for weak fault feature extraction in rolling bearing fault diagnosis

Weak fault feature extraction is of great significance to the fault diagnosis of rolling bearing. At the early stage of defects, fault features are usually weak and easily submerged in strong background noise, which makes feature information extremely difficult to be excavated. This paper proposes an iterative morphological difference product wavelet (MDPW) to address this issue. In this scheme, firstly, the morphological difference product filter (MDPF) is developed using the combination morphological filter-hat transform operator and difference operator. The MDPF is then incorporated into a morphological undecimated wavelet to construct the MDPW, which can achieve noise suppression and fault feature enhancement. Subsequently, the optimal iteration numbers that influence the performance of MDPW is determined using the fault severity indicator, which effectively extracts periodic impulse related to the failure of rolling bearing. Finally, the fault identification is inferred by the occurrence of fault defect frequencies in the MDPW spectrum with the optimal iteration numbers. The validity of the iterative MDPW is evaluated through numerical simulations and experiment cases. The analysis results demonstrate that the iterative MDPW has higher diagnosis accuracy than existing algorithms (e.g., adaptive single-scale morphological wavelet and weighted multi-scale morphological wavelet). This research provides a new perspective for improving the weak fault feature extraction of rolling bearing.

Faulty Bearing Signal Analysis With Empirical Morphological Undecimated Wavelet

Morphological undecimated wavelet (MUDW) is a powerful method, which can capture features accurately through removing noise by signal decomposition. It has therefore been widely used in the fault diagnosis of rotating machine. However, the setting of some key parameters (including the selection of morphological operator (MO), the length of structure elements (SEs), and the number of decomposition level) still depends heavily on prior experience. Not only is the computational efficiency seriously affected by the time-consuming trial-and-error process, but its filtering effect is also vulnerable to the interference of external factors like random impulses. In view of that, an improved method, called empirical MUDW (EMUW), is proposed in this article. Waveform trend (WT) is utilized first to make up for the deficiency of the MOs, which eliminates the interferences brought in by random impulses. Based on the similarity evaluation between the WT signals of adjacent level, the number of decomposition level of EMUW can therefore be determined. Finally, the signal can be reconstructed with the subsignals obtained by decomposition. Compared with traditional MUDW, EMUW shortens the calculation time to less than 2 s.

Development of a morphological convolution operator for bearing fault detection

This paper presents a novel signal processing scheme, namely morphological convolution operator (MCO) lifted morphological undecimated wavelet (MUDW), for rolling element bearing fault detection. In this scheme, a MCO is first designed to fully utilize the advantage of the closing & opening gradient operator and the closing–opening & opening–closing gradient operator for feature extraction as well as the merit of excellent denoising characteristics of the convolution operator. The MCO is then introduced into MUDW for the purpose of improving the fault detection ability of the reported MUDWs. Experimental vibration signals collected from a train wheelset test rig and the bearing data center of Case Western Reserve University are employed to evaluate the effectiveness of the proposed MCO lifted MUDW on fault detection of rolling element bearings. The results show that the proposed approach has a superior performance in extracting fault features of defective rolling element bearings. In addition, comparisons are performed between two reported MUDWs and the proposed MCO lifted MUDW. The MCO lifted MUDW outperforms both of them in detection of outer race faults and inner race faults of rolling element bearings.

Bearing fault diagnosis with morphological gradient wavelet

In order to extract impulsive features and perform noise reduction in the vibration signals of defective bearings, morphological gradient wavelet (MGW) scheme is proposed. This approach is based on a combination of gradient operator and morphological wavelet theory. In order to validate the effectiveness of the proposed scheme, computer experiments are performed for two different scenarios. In these scenarios, vibration signals for two defective bearings are investigated; one with an inner race fault and the other with an outer race fault. The main advantages of the proposed MGW algorithm are its speed and simplicity of implementation. Therefore, it is suitable for real-time signal processing aimed at online condition monitoring. The proposed method is compared with two other similar algorithms namely, the morphological Haar wavelet (MHW) and the morphological undecimated wavelet decomposition (MUWD). Simulation results confirm the superiority of the proposed MGW in terms of effectiveness and speed.

The morphological undecimated wavelet decomposition – Discrete cosine transform composite spectrum fusion algorithm and its application on hydraulic pumps

Degradation feature extraction of hydraulic pumps is a key step of the condition-based maintenance. In this paper, a novel method based on MUWD-DCS fusion algorithm is proposed. In order to decrease noises and disturbances, the method for obtaining detail components by the Morphological Undecimated Wavelet Decomposition (MUWD) with the selected parameters is presented firstly. Multi-channel vibration signals are proposed by the MUWD and the detail components containing sensitive information are achieved. Furthermore, on the basement of the earlier Composite Spectrum (CS), the DCS fusion algorithm is proposed to make fusion of the obtained detail components for improving the feature performance. The DCS entropy is extracted to be the degradation feature. Analysis of application on the hydraulic pump degradation experiment demonstrates that the proposed algorithm is feasible and it is effective to reveal the performance degradation of the hydraulic pump.

Application of Power Quality Disturbance Location Based on MUDW

It is the random and irregular and variable properties of disturbance signal in the detection of power quality, there is lack of mature methods for detection and location of PQ disturbances. An improved morphological undecimated wavelet scheme was presented in this paper and applied to the detection of power quality disturbance. The improved MUDW scheme, which meets signal reconstruction conditions, contains broad open-close or close-open filter and morphological gradient which detect mutations on the top and bottom edges of signal. It used MATLB to detect transient or steady state with single or composite disturbance signals and made comparison with the existing form of sampling wavelet. The result shows that the new MUDW scheme can recognize and detect the signal and has a good anti-noise performance.

An Improved Multi-Scale Morphological Undecimated Wavelet Method and its Application in Rolling Bearing Fault Feature Extraction

For the purpose of extract the fault feature hidden by strong noise background in rolling bearing fault signal, a morphological undecimated wavelet method was proposed. A undecimated wavelet decomposition operator called gradient filter was presented based on an open-closed and closed-open mixed filter. The morphological gradient filter was used to extract the impulse feature of signal. The type and the length of structure elements used in these filters were alterable adapt to the signal. The method was applied to analyze the simulated data and measured vibration signals from the bearing with fault. The results confirm that the proposed method is feasible in impulse feature extraction of signal, and it is more effective than other traditional morphological undecimated wavelet methods.

Real-time image fusion based on morphological un-decimated wavelets

An efficient Morphological Un-Decimated Wavelet(MUDW) transform with more delicate and accurate multi-scale decomposition performance that suites real-time image fusion was proposed.It took the average of dilation and erosion as the analysis operator,and the difference of adjacent scale images as the detail image.Size-increasing structure elements were adopted to get better fusion result.Due to the simplicity of dilation and erosion operator,computation time is shorter than other real-time algorithms.Furthermore,a factor was added during reconstruction,to obtain an obvious enhancement effect.The experimental results show that the new method outperforms other real-time algorithms.

A New Intelligent Fault Recognition Method for Gearbox

In this paper, a novel method to recognize gear fault pattern was approached based on multi-scale morphological undecimated wavelet decomposition, sample entropy and grey incidence. Firstly, multi-scale morphological undecimated wavelet decomposition was developed based on the characteristic of impulse feature extraction in difference morphological filter. And it was used to process different gear fault signals in five levels. Secondly, the sample entropy of each level was calculated. Finally, the sample entropy was served as the feature vectors and the grey incidence of different gear vibration signals was calculated to identify the fault pattern and condition. Practical example shows the efficiency of the proposed recognition method. It is suitable for on-line monitoring and fault diagnosis of gear.

Morphological undecimated wavelet decomposition for fault diagnostics of rolling element bearings

This paper presents a novel morphological undecimated wavelet (MUDW) decomposition scheme for fault diagnostics of rolling element bearings. The MUDW scheme is developed based on the morphological wavelet (MW) theory for both the extraction of impulse features and noise smoothing in signal processing. The analysis operators and the synthesis operator of MUDW strictly satisfy the pyramid condition. The MUDW scheme is used to extract impulse features from rolling element bearing defect signals imposed with noise. The efficiency of the MUDW scheme used for noise smoothing and the extraction of impulse components is evaluated using the simulated data and measured signals from the bearing test rig. Compared with enveloping demodulation analysis, the MW transform and the traditional wavelet transform (WT), the MUDW decomposition scheme is more effective and suitable for the on-line diagnostics of bearings in rotating machines.

Morphological undecimated wavelet decomposition for fault location on power transmission lines

This paper presents a novel morphological undecimated wavelet (MUDW) decomposition scheme for fault location on power transmission lines. The MUDW scheme is developed based on the morphological wavelet (MW) theory for both the extraction of transient features and noise reduction in signal processing. The analysis operators and the synthesis operator of MUDW strictly satisfy the pyramid condition. In this paper, the MUDW scheme is used to extract features from noise imposed fault-generated transient voltage and/or current signals of power transmission lines. The efficiency of the MUDW scheme used for noise reduction and the extraction of sudden changes in the transient signals are evaluated in simulation studies. The simulation results show that the fault location can be accurately detected in noisy environments