Adaptive Morphological Filter Research Articles

Off-line location strategy for single-phase ground fault of distribution line

This paper adopts off-line location strategy of single-phase grounding fault of distribution lines, determines size and frequency of injected AC signal by analyzing distribution parameters of lines, and then develops portable small current signal source with adjustable frequency through power conversion control technology, and develops signal detector by analyzing noise correlation and adaptive morphological filtering. Then off-line location strategy is developed according to above two signal devices. Finally, it is verified that the strategy can accurately locate fault point of distribution line.

Improved Multi-Distance ARMF Integrated With LTSA Based Pattern Matching Method and Its Application in Fault Diagnosis

Effective dimensionality reduction (DR) and classification in fault diagnosis remain a significant challenge, primarily due to the increasing scale of industrial processes and the non-linear and high-dimensional features of process data. To address this challenge, we present local tangent space alignment (LTSA) integrated with a multi-distance adaptive order morphological filter (MARMF) fault diagnosis method (LTSA-MARMF). In LTSA-MARMF, LTSA that preserves the local manifold structure using tangent space is first utilized for DR to provide the required feature space data for ARMF. Next, the cosine distance and dynamic time warping distance are introduced into the distance error of ARMF, considering the spatial similarity and dynamic features to improve classification accuracy. Finally, the distance-matching result of the pattern is applied to determine the type of fault. Through simulations, it is evident that LTSA-MARMF can achieve more satisfactory fault diagnosis accuracy than other related methods on the Tennessee-Eastman process (TEP) and the actual Grid-connected PV System (GPVS). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is inspired by the difficult-to-handle high-dimensional and non-linear features of process data but is also applicable to high-dimensional and non-linear data from other industrial processes. The DR and classification are important aspects of fault diagnosis. In this paper, a novel pattern-matching method is utilized for fault diagnosis, which uses LTSA and the modified multi-distance ARMF for DR and classification, respectively. In terms of mathematics, the distance error of ARMF is analyzed. The combination of multi-distance is used to enhance the accuracy of fault diagnosis. The preliminary experiments show that LTSA-MARMF is feasible, but has not been tested in the plant. We will consider testing LTSA-MARMF in an actual plant in future research.

Reciprocating compressors intelligent fault diagnosis under multiple operating conditions based on adaptive variable scale morphological filter

Faults of reciprocating compressors (RCs) are almost observed as impacts in the vibration signal. Thus, extracting impact features is considered an effective method to monitor RC health conditions and reduce sensor investment. However, the quantity and location of impacts are challenging to identify because of the severe background noise and the broad aliasing frequency. This study proposes a fault knowledge-independent adaptive variable scale morphological filter (AVSMF) and an automatic impact localization method to extract impact features from a vibration signal, making real-time intelligent fault diagnosis possible. The AVSMF method exploits the amplitude distribution of intrinsic mode functions and morphological filters to extract the impact components in a signal. Then, to identify impacts, the demodulation energy operator of symmetrical differencing is utilized to discover instantaneous energy changes. Furthermore, a new feature called the impact phase bin (IPB), which assembles the characteristics of impacts in the form of bins, is presented to improve the fault diagnosis performance. Simulations were carried out on a signal comprising multiple impulses with different frequencies and amplitudes. The results showed the effectiveness of the proposed methods on impact localization. Additionally, the potential application in liquid slugging fault diagnosis was demonstrated. Finally, the superiority of the proposed methods was experimentally substantiated by conducting fault diagnosis tests under single and multiple operating conditions on a two-stage double-acting RC. The fault classification accuracy under the single operating condition is 95.08% using the IPB feature only, and that is 99.06% under multiple operating conditions combining conventional statistical features with the IPB feature.

An adaptive morphological filtering and feature enhancement method for spindle motor bearing fault diagnosis

The unbalanced electromagnetic force and unqualified assembly of the spindle motor lead to weak vibration energy and the difference in fault features in different life cycles and different bearing individuals. Compared with single fault diagnosis and the datasets that come from the test bench, compound faults diagnosis of the spindle motor is a challenging task. To solve it, an improved filtering and feature enhancement method combined with the merits of AMF and TEO is proposed. Firstly, the adaptive morphological filtering (AMF) method is developed by adaptively constructing the size of the structural element (SE) for remaining in corresponding SE with the component of interest, which can reduce interference from background noise. Still, the periodic fault impulse, especially the incipient fault signals, is easily modulated by other fault-unrelated harmonic components. Thus, the filtered signals are processed by the Teager energy operator (TEO) for enhancing the faint transient impact compositions. Finally, the effectiveness of the method is verified by simulation and fault motor matched with NTN ceramic bearing and FAG metal bearing respectively, where the motor matched with NTN ceramic bearing is an early failure case and the other is a late failure case. Besides, compared with some traditional methods, the result proves that the proposed method has better performance under the actual engineering scenarios for different degrees of fault feature identification.

A signal-filtering and feature-enhancement method based on ensemble local mean decomposition and adaptive morphological filtering

The bearing fault signals from the spindle motors of computer numerical control machines are complex and non-linear due to being coupled to multiple subsystems. The complexity of industrial signals, with increased industrial noise, and the difference in fault features in different life cycles and different individual signals bring great challenges for fault feature extraction. In this paper, a signal-filtering and feature-enhancement method based on an ensemble local mean decomposition and adaptive morphological filtering (ELMD-AMF) method is proposed. First, the original vibration signal of the bearing is reconstructed by ELMD to reducing interference from background noise. Next, an improved feature-enhancement process based on AMF is constructed, a particle swarm optimization with maximum-weighted spectral kurtosis as an optimization objective is used to adaptively construct the size of the structural element, and a morphology hat product operator one is adapted to extract the periodic impulse features. Finally, the effectiveness of the method is proved by using an actual three-phase induction motor matched with an NTN ceramic bearing and a FAG metal bearing, respectively. Further, compared with minimum entropy deconvolution and fast kurtogram methods, the result proves that the proposed method has better performance for both early-failure and late-failure scenarios under real-world engineering conditions.

A Two-Stage Rolling Bearing Weak Fault Feature Extraction Method Combining Adaptive Morphological Filter with Frequency Band Selection Strategy

To extract the weak fault features hidden in strong background interference in the event of the early failure of rolling bearings, a two-stage based method is proposed. The broadband noise elimination ability of an adaptive morphological filter (AMF) and the superior capability of a frequency band selection (FBS) strategy for fault transient location identification are comprehensively utilized by the proposed method. Firstly, the AMF with a simple theory and high calculation efficiency is used as a preprocessing program to enhance the fault transient features. Then, the proposed FBS strategy based on the sparsity index (SI) is utilized to further handle the filtered signal processed by the AMF. Finally, the constructed optimum bandpass filter based on the analysis result of the FBS is used to further filter the handled signal processed by AMF and envelope spectral analysis is applied on the last filtered signal to realize the ideal fault feature extraction effect. Compared with the other traditional FBS methods based on kurtosis or the other index, the proposed FBS strategy based on SI has strong robustness to noise. One experimental signal and one engineering vibration signal are used, respectively, to verify the feasibility of the proposed method.

A novel pattern classification integrated GLPP with improved AROMF for fault diagnosis

With the scale expansion of industrial processes, safety has become one of its important links and the requirements for safety monitoring are getting higher. How to realize timely and effective fault diagnosis, especially for incipient faults, has attracted more discussion and research. This paper proposes a novel pattern classification integrated global−local preserving projections (GLPP) and improved adaptive rank-order morphological filter (IAROMF) for fault diagnosis. First, in order to preserve the global manifold information and local manifold information of the data, GLPP is introduced to extract the features of the data to obtain the test signal and the template signal. Second, AROMF transformation is performed on the test signal and template signal to obtain the output trend feature. Third, as the pattern matching by Euclidean distance-based AROMF has the restriction of sequence timing and the feature points need to be strictly corresponding, the Weighted Dynamic Time Warping (WDTW) distance is used to calculate the total error of iteration between the template trend and the output trend. In order to prove the effectiveness of the method proposed in this paper, a case study was carried out on the Tennessee Eastman (TE) process. The experiment results illustrated that the novel pattern classification method proposed in this paper has higher diagnostic accuracy than other fault diagnosis methods, especially for incipient faults.

Diagnosing utility grid disturbances in photovoltaic integrated DC microgrid using adaptive multiscale morphology with DFA analysis

In this study, a novel approach is proposed to distinguish both single and mixed power quality (PQ) disturbances in the network connected to the utility grid in a photovoltaic (PV) integrated DC microgrid. To analyze PQ disturbances in the proposed system, AC voltage signals are captured at the PCC of the utility grid. The captured voltage signals contain strong noise which impacts the system robustness. To resolve this problem, an adaptive multiscale improved combination morphological filter (AMICMF) technique is introduced in the proposed system. In this technique, an improved combination morphological filter (ICMF) is first proposed by using the basic four morphological operations. Then multiscale operation (MICMF) is done by different structural element (SE) lengths chosen by sparse weighted Kurtosis index (SKI w ). The obtained optimized signal is processed through the detrended fluctuation analysis (DFA) which gives the scaling exponent ( λ ) values based on window length. These scaling exponent values are used to classify the PQ disturbances in terms of scatter plots. The performance of the proposed study is calculated by classification accuracy (CA) and relative computational time (RCT) and the efficacy of the proposed technique is validated by comparing the CA and RCT with existing techniques. The entire study is tested on MATLAB/Simulink environment. The complete study is tested and validated using a high-speed multifunction National Instrument USB6008 device for the online monitoring of PQ disturbances on the MATLAB/Simulink environment. • A new adaptive multiscale morphological filter is used for PQ analysis of DC microgrid. • The adaptation of the filter is accomplished using a sparse weighted kurtosis index. • Detrended fluctuation analysis is used to process the optimized signal to yield scaling exponents. • These scaling exponent values are used to classify the PQ disturbances in terms of scatter plots.

Multiple power quality disturbances analysis in photovoltaic integrated direct current microgrid using adaptive morphological filter with deep learning algorithm

In this paper, an adaptive multiscale enhanced average combination morphological filter is proposed to analyze voltage signals captured at the point of common coupling of the photovoltaic integrated direct current microgrid for diagnosing several power quality disturbances. This method performs multiscale operation on the power quality disturbance signals from which the optimized structuring element scales are chosen by using maximum value of sparse envelope kurtosis index. Further, statistical features are extracted by using optimized scales. The extracted features are used as inputs to a deep stacked extreme leaning machine autoencoder to retrieve the most distinguishable unsupervised features and these features are processed through kernel random vector functional link network based supervised classifier to classify the power quality disturbances. Further, to improve the classification performance of the proposed technique, a meta heuristic chaotic particle swarm optimization integrated with sine cosine optimization algorithm is used. This optimization algorithm optimizes the kernel parameters by minimizing the mean absolute error. The efficacy of the proposed technique is tested in terms of classification accuracy and confusion matrix and is verified by multifunction high-speed national instrument device for monitoring of power quality disturbances in the MATLAB/Simulink platform. The simulations and hardware results demonstrate that the proposed technique exhibits remarkable accuracy, faster training time and low computational complexity as compared with other existing methods.

Fast Spectral Correlation Detector for Periodic Impulse Extraction of Rotating Machinery

Periodic impulse components are a typical symptom of rotating machinery failure, which are often masked by heavy background noise. It is of great practical significance to research how to obtain periodic impulse components to achieve fault diagnosis of rotating machinery. Fast spectrum correlation (Fast-SC), as a typical non-stationary and nonlinear signal processing method, has been studied in feature extraction by suppressing background noise to enhance periodic impulse components. However, effectively determining the bandwidth of Fast-SC is still a challenging issue. To address this issue, a novel method called Fast-SC detector is developed, which decomposes the measured signal of rotating machinery into multiple Fast-SC slices with different frequency bands. The whale optimization algorithm (WOA) is utilized to optimize the number of Fast-SC slices with minimum mean entropy as the optimal Fast-SC bandwidth. In addition, an adaptive combination morphological filter (ACMF) is applied to suppress the residual noise and narrowband impulses in the WOA-based Fast-SC slices to enhance the fault features. Finally, the Fast-SC detector is constructed using the average denoising Fast-SC slices to infer the type of rotating machinery. The proposed Fast-SC detector is compared with existing methods by applying simulation signal model and experimental data. The results prove that Fast-SC detector is superior to some excellent periodic impulse extraction algorithms in extracting the symptoms of rotating machinery failure.

Rolling bearing diagnosis based on an unbiased-autocorrelation morphological filter method

Traditional morphological filter methods (MFs) are commonly affected by deterministic signals and random impulses, so an adaptive unbiased-autocorrelation morphological filter (UAMF) combined with an autoregressive filter (AR-UAMF) is proposed to tackle the issue. The deterministic component of the raw signal is first removed using an autoregressive filter (AR), and then the random impulse interference is attenuated using the UAMF. Additionally, unbiased-autocorrelation kurtosis (AK) is developed to ascertain the optimum SE scale. This indicator has the advantage of being sensitive to fault-related impulses under the interference of deterministic signals, random impulses and white noise. Finally, the simulation and experiment analysis results demonstrate that AR-UAMF is much more powerful and effective than the other single-scale and multiscale MFs in recovering the fault-related impulses and performing fault diagnoses.

Novel edge detection method for nuclei segmentation of liver cancer histopathology images

In automatic cancer detection, nuclei segmentation is a very essential step which enables the classification task simpler and computationally more efficient. However, automatic nuclei detection is fraught with the problems of inter-class variability of nuclei size and shapes. In this research article, a novel unsupervised edge detection technique, is proposed for segmenting the nuclei regions in liver cancer Hematoxylin and Eosin (H&E) stained histopathology images. In this novel edge detection technique, the notion of computing local standard deviation is incorporated, instead of computing gradients. Since, local standard deviation value is correlated with the edge information of image, this novel method can extract the nuclei edges efficiently, even at multiscale. The edge-detected image is further converted into a binary image by employing Ostu (IEEE Trans Syst Man Cybern 9(1):62–66, 1979)’s thresholding operation. Subsequently, an adaptive morphological filter is also employed in order to refine the final segmented image. The proposed nuclei segmentation method is also tested on a well-recognized multi-organ dataset, in order to check its effectiveness over wide variety of dataset. The visual results of both datasets indicate that the proposed segmentation method overcomes the limitations of existing unsupervised methods, moreover, its performance is comparable with the same of recent deep neural models like DIST, HoverNet, etc. Furthermore, three quality metrics are computed in order to measure the performance of several nuclei segmentation methods quantitatively. The mean value of quality metrics reveals that proposed segmentation method indeed outperformed other existing nuclei segmentation methods.

On the Properties of Some Adaptive Morphological Filters for Salt and Pepper Noise Removal

Mathematical Morphology (MM) is a tool that can be applied to many digital image processing tasks that include the reduction of impulsive or salt and pepper noise in grayscale images. The morphological filters used for this task are filters resulting from two basic operators: erosion and dilation. However, when the level of contamination of the image is higher, these filters tend to distort the image. In this work we propose a pair of operators with properties, that better adapt to impulsive noise than other classical morphological filters, it is demonstrated to be increasing idempotent morphological filters. Furthermore, the proposed pair turns out to be a ?-filter and a ?-filter which allow to build morphological openings and closings. Finally, they are compared with other filters of the state-of-the-art such as: SMF, DBAIN, AMF and NAFSM, and have shown a better performance in time-quality ratio when the noise level is above 50%.

Adaptive Morphological Analysis Method and Its Application for Bearing Fault Diagnosis

The vibration response of faulty bearing is always characterized by periodic transient impulses in the signal. Generally, these fault-related features are inevitably submerged in noise and harmonic components. Mathematical morphology is an excellent method of noise reduction, which can retain the detail information of impulses in the time domain. However, the filtering effect of traditional morphological operator (MO) might be easily affected by random impulses, and the proper selection of the structure element (SE) depends heavily on the experience of researchers. In order to effectively remove these interferences and extract the fault features accurately, an improved method, named adaptive morphological filter (AMF), is proposed in this article. This method utilizes autocorrelation to lift MO in time domain to enhance periodic components, and the scale of SE can, therefore, be calculated with the local maximum of the autocorrelation spectrum. Since the selection of the optimal SE scale is adaptive, researchers’ experience is no longer needed, and there is also no need to calculate the fault characteristic frequency (FCF) for the determination of maximum scale of SE. The vibration and acoustical signals of faulty locomotive wheel set bearing are analyzed with this method, and the results verify its effectiveness and ability.

Study and application analysis of random noise adaptive morphological filter algorithm reconstruction for seismic signals

Study and application analysis of random noise adaptive morphological filter algorithm reconstruction for seismic signals

A New Adaptive Multiscale Morphological Filter and Robust RVFLN Classifier for Distributed Generation Systems during Islanding and Non-Islanding Events

A new method for islanding and non-islanding disturbances detection and classification is proposed for a multiple PV based distributed generation (DG) system utilizing adaptive multi-scale morphological filter (AMF) and random vector functional link network (RVFLN) classifier. In comparison to different signal analysis techniques, the mathematical MF, that has wide application in power signals, EEG signal analysis, image processing, pattern recognition, etc. posses the benefit of easy execution, fast processing, and minimal computations. Further it is well known that a single scale morphological filter has limited noise filtering capacity and may also filter useful signal disturbance components resulting in erroneous detection of disturbance signals in microgrid. Therefore, an adaptive multiscale combined morphological filter is presented in this paper built on the concept of multiscale overall filtering which has better denoising effect and can retain useful signals better than the traditional filter. The proposed technique is built upon the measurement of voltage signal samples and the processing of these signals through AMF has been done for feature extraction. The extracted features are then employed as inputs to an efficient, fast, and easily implementable randomized network based classifier (RVFLN) which is made robust to reject the presence noise and outliers in the signal data. The outputs exhibited from the suggested technique concludes that it is s very fast and accurate technique for the detection and classification of islanding and non-islanding events in comparison to the widely used approaches.

An adaptive morphology based segmentation technique for lung nodule detection in thoracic CT image

Lung cancer is one of the most life-threatening cancers mostly indicated by the presence of nodules in the lung. Doctors and radiological experts use High-Resolution Computed Tomography (HRCT) images for nodule detection and further decision making from visual inspection. Manual detection of lung nodules is a time-consuming process. Therefore, Computer-aided detection (CADe) systems have been developed for accurate nodule detection and segmentation. CADe-based systems assist radiologists to detect lung nodules with greater confidence and a lesser amount of time and have a significant impact on the accurate, uniform, and early-stage diagnosis of lung cancer. In this research work, an adaptive morphology-based segmentation technique (AMST) has been introduced by designing an adaptive morphological filter for improved segmentation of the lung nodule region. The adaptive morphological filter detects candidate nodule regions by employing adaptive structuring element (ASE) and at the same time improves nodule detection accuracy by reducing false positives (FPs) from the Computed Tomography (CT) slices. The detected nodule candidate regions are then processed for feature extraction. In this study, morphological, texture and intensity-based features have been used with support vector machine (SVM) classifier for lung nodule detection. The performance of the proposed framework has been evaluated by incorporating a 10-fold cross-validation technique on Lung Image Database Consortium-Image Database Resource Initiative (LIDC/IDRI) dataset and on a private dataset, collected from a consultant radiologist. It has been observed that the proposed automated computer-aided detection system has achieved overall classification performance indices with 94.88% sensitivity, 93.45% specificity and 94.27% detection accuracy with 1.8 FPs/scan on LIDC/IDRI dataset and 91.43% sensitivity, 90.45% specificity, 92.83% accuracy with 3.2 FPs/scan on a private dataset. The results show that the proposed CADe system presented in this paper outperforms the other state-of-the-art methods for automatic nodule detection from the HRCT image.

Tacholess bearing fault detection based on adaptive impulse extraction in the time domain under fluctuant speed

The bearing diagnosis in industrial applications is limited because the traditional equal-time sampling strategy results in the loss of signal period and fault feature under fluctuant speed. To address these issues, a new tacholess bearing fault diagnosis method based on time-domain impulse extraction (TDIE) is proposed. With the TDIE method, the frequency fluctuant phenomenon is eliminated and fault characteristic frequencies are exposed distinctly. In contrast to traditional time-frequency analysis techniques, straightforward phase information is analyzed in the time-domain with the new adaptive morphological filtering and identified impulse optimization methods. Finally, angular resampling is employed to realize bearing fault diagnosis under fluctuant speed. To demonstrate the performance of the proposed method, simulation and experiments are conducted. Results indicate that the technique is an effective tacholess bearing fault detection under fluctuant speed with enhanced efficiency and robustness.

Bearing Fault Signal Analysis Based on an Adaptive Multiscale Combined Morphological Filter

Bearing fault signal analysis is an important means of bearing fault diagnosis. To effectively eliminate noise in a fault signal, an adaptive multiscale combined morphological filter is proposed based on the theory of mathematical morphology. Both simulation and experimental results show that the adaptive multiscale combined morphological filter can remove noise more thoroughly and retain details of the fault signal better than the dual-tree complex wavelet filter, traditional morphological filter, adaptive singular value decomposition method (ASVD), and improved switching Kalman filter (ISKF). The adaptive multiscale combined morphological filter considers both positive and negative impulses in the signal; therefore, it has strong adaptability to complex noise in the environment, making it an effective new method for bearing fault diagnosis.

Novel Pattern-Matching Integrated KCVA with Adaptive Rank-Order Morphological Filter and Its Application to Fault Diagnosis

With the scale expansion of industrial processes, the relationship between process variables becomes complex and highly nonlinear. As a result, the requirements for fault diagnosis and safety monitoring become demanding. To address this problem, a novel and effective pattern matching method using kernel canonical variate analysis (KCVA) integrated with an adaptive rank-order morphological filter (ARMF) is proposed for fault diagnosis. In the proposed method, KCVA is first used to extract the nonlinear correlation information with dynamic characteristics from the original process data and achieve feature dimension reduction; the features extracted by KCVA are then subjected to ARMF transformation for output trend features and pattern matching. To accurately evaluate the morphological similarity between the test trends and template trends of ARMF, the dynamic time warping distance is adopted for pattern classification. Finally, the proposed KCVA-ARMF pattern matching method is developed as an effective faul...