Robust Feature Extraction Algorithm Research Articles

Rotating machinery fault-induced vibration signal modulation effects: A review with mechanisms, extraction methods and applications for diagnosis

Rotating machinery faults can induce characteristic modulation effects in a vibration signal, and their diagnosis can thus be conducted by extracting fault-induced modulation features. To be specific, such a diagnostic strategy typically involves three main aspects, i.e., fault-induced signal modulation mechanisms, modulation feature extraction methods and applications for diagnosis. To date, the research works on these three aspects all achieved great progresses. A systematic review is thus urgently needed to summarize these achievements, and guide their future directions and developments. This paper aims to fill these gaps and address the needs. Three kinds of typical modulation effects induced by transmission elements are firstly reviewed and summarized. For each kind of modulation effects, a representative phenomenological model is given to formulate and illustrate the relationship between fault-induced modulation patterns and characteristic spectral distributions. As the primary tools to extract signal modulation features, different kinds of time-frequency analysis and signal decomposition methods are then systematically reviewed, including their up-to-date developments, classifications and application scenarios. Some representative works of vibration signal modulation feature extraction-based fault diagnosis are finally introduced along with several typical examples. Based on current research achievements, two potential topics for the future developments of vibration-based diagnostic techniques are the quantitative study of fault feature evolution patterns and the development of more robust and efficient feature extraction algorithms.

Abstract 11178: Non-Invasive Identification of Fibrotic Atrial Cardiomyopathy with Neural Networks Based on 642,400 12-Lead Ecgs from an Extensive Cohort Simulation Study

The clinical picture of fibrotic atrial cardiomyopathy is caused by a partial replacement of atrial myocardial tissue with fibrotic substrate. Being subject to electrical and structural remodeling processes, the substrate provides the necessary properties for the maintenance of atrial fibrillation and could thus be a good risk marker. We hypothesize that the P waves in the 12-lead ECG present a non-invasive and cost-effective tool to quantify the extent of atrial fibrosis as an alternative to state-of-the-art approaches. Our virtual patient cohort comprised a combination of 80 atrial, 25 thoracic geometries and 27 rotation angle variations of the atria. For each atrial model, 10 stages of fibrosis were defined by replacing different fractions of the myocardial volume with fibrotic tissue patches exhibiting remodeled ionic and conduction velocity properties. Conducting electrophysiological simulations in sinus rhythm on each model combination with one to three different healthy baseline conduction velocities yielded 642,400 synthetic ECGs. P wave duration, dispersion, peak-to-peak amplitudes and terminal force in V1 were calculated and served, together with atrial volumes and torso sizes, as an input for a regression neural network to estimate the volume fraction of atrial fibrosis. The root mean square error between the ground truth and the estimated extent of fibrosis was 8.74% left atrial fibrotic volume. Based on the regression results, a distinction between the healthy and the diseased cohorts was possible with an accuracy of 99.01%, a sensitivity of 99.57% and a specificity of 62.38%. In conclusion, we have gauged the potential of the 12-lead ECG for estimating fibrotic atrial volume fractions in an extensive cohort simulation study comprising both, functional and anatomical inter-subject variability. As a next step, the methods need to be validated with clinical data for which robust feature extraction algorithms are required.

Real-Time 2-D Lidar Odometry Based on ICP

This study presents a 2-D lidar odometry based on an ICP (iterative closest point) variant used in a simple and straightforward platform that achieves real-time and low-drift performance. With a designated multi-scale feature extraction procedure, the lidar cloud information can be utilized at multiple levels and the speed of data association can be accelerated according to the multi-scale data structure, thereby achieving robust feature extraction and fast scan-matching algorithms. First, on a large scale, the lidar point cloud data are classified according to the curvature into two parts: smooth collection and rough collection. Then, on a small scale, noise and unstable points in the smooth or rough collection are filtered, and edge points and corner points are extracted. Then, the proposed tangent-vector-pairs based on edge and corner points are applied to evaluate the rotation term, which is significant for producing a stable solution in motion estimation. We compare our performance with two excellent open-source SLAM algorithms, Cartographer and Hector SLAM, using collected and open-access datasets in structured indoor environments. The results indicate that our method can achieve better accuracy.

R-K algorithm: A novel Dynamic Feature Matching Method of flotation froth

Froth flotation is a common process used for extracting a desired mineral from its ore. The dynamic characteristics extraction towards froth flotation can better identify the collapse and fragmentation. According to the characteristics of continuous movement of flotation froth, a new flotation froth feature matching method is proposed, which based on Kalman filtering and RANSAC algorithm, called R-K matching algorithm. And then, combining the R-K matching algorithm and SURF (fast robust scale invariant feature extraction algorithm) to extract the dynamic characteristics of flotation froth. Finally, the velocity and the entropy of the flotation froth are calculated to confirm the effectiveness and the practicability. The simulation results from the industrial experimental data show that the method can extract more feature matching pairs than the existing methods. So, it can obtain more accurate dynamic information on flotation froth in the changed state.

Automatic Speaker Recognition using MFCC and Artificial Neural Network

Automatic speaker recognition is the process of identification of a person automatically from his/her voices. A robust feature extraction algorithm is required for effective and efficient classification. In this paper, a new method is proposed for identifying the speaker using an artificial neural network. Here mel- frequency cepstral coefficient(MFCC) is used as a feature extraction technique that provides useful features for the recognition process. Using these extracted features value, input samples are then created and finally, classification is performed using Multilayer Perceptron (MLP) which is trained by backpropagation. This proposed method gives an accuracy of 94.44%.

A Robust Feature Extraction and Parameterized Fitting Algorithm for Bottom‐Side Oblique and Vertical Incidence Ionograms

AbstractAutomatic processing of ionograms to extract ionospheric propagation characteristics and electron density profile parameters is an important enabling step toward constructing real time data‐assimilative models of the ionosphere. A robust and reliable feature extraction and fitting algorithm that supports both oblique and vertical incidence ionograms has recently been developed to enhance the regional ionospheric model for Australia's Jindalee Operational Radar Network. The so‐called DST‐IIP algorithm is an autoscaling technique based on a constrained quasi‐parabolic segment profile, which is modified from its climatological estimate to fit a set of key peak, edge, and line features in the ionogram image, representing the E, sporadic‐E (Es), and F2 layers. The F2 fitting, in particular, is performed in the ionogram domain, using analytic ray‐tracing and homing to produce a synthetic trace that reproduces the image features. Following thorough testing on a large database of midlatitude Australian ionograms, the DST‐IIP algorithm now runs routinely on‐board DST Group's Digital Oblique Receiving System. This paper provides an overview of the technique, along with sample results and performance statistics.

Efficient parallel optimizations of a high-performance SIFT on GPUs

Stable local image feature detection is a fundamental problem in computer vision and is critical for obtaining the corresponding interest points among images. As a popular and robust feature extraction algorithm, the scale invariant feature transform (SIFT) is widely used in various domains, such as image stitching and remote sensing image registration. However, the computational complexity of SIFT is extremely high, which limits its application in real-time systems and large-scale data processing tasks. Thus, we propose several efficient optimizations to realize a high-performance SIFT (HartSift) by exploiting the computing resources of CPUs and GPUs in a heterogeneous machine. Our experimental results show that HartSift processes an image within 3.07∼7.71 ms, which is 55.88∼121.99 times, 5.17∼6.88 times, and 1.25∼1.79 times faster than OpenCV SIFT, SiftGPU, and CudaSift, respectively.

GA-SURF: A new Speeded-Up robust feature extraction algorithm for multispectral images based on geometric algebra

Speeded-Up Robust Features (SURF) can be used for tasks such as object recognition, image registration, classification or 3D reconstruction. However, existing SURF algorithm cannot be directly applied to deal with multispectral images. In this paper, based on SURF and the theory of Geometric Algebra (GA), a novel feature extraction algorithm named GA-SURF is proposed for multispectral images. First, a Hessian matrix based on GA is calculated for locating interest points in spatial and spectral space. The box filters based on GA are used to simplify the calculation of Hessian matrix and generate image pyramids. Then, following the procedures of SURF, interest points are located by the image pyramids and described in GA space. Experimental results demonstrate that, compared with other feature extraction algorithms for multispectral images, GA-SURF can be computed much faster and are more robust and distinctive.

Chinese License Plates Recognition Method Based on A Robust and Efficient Feature Extraction and BPNN Algorithm

The prosperity of license plate recognition technology has made great contribution to the development of Intelligent Transport System (ITS). In this paper, a robust and efficient license plate recognition method is proposed which is based on a combined feature extraction model and BPNN (Back Propagation Neural Network) algorithm. Firstly, the candidate region of the license plate detection and segmentation method is developed. Secondly, a new feature extraction model is designed considering three sets of features combination. Thirdly, the license plates classification and recognition method using the combined feature model and BPNN algorithm is presented. Finally, the experimental results indicate that the license plate segmentation and recognition both can be achieved effectively by the proposed algorithm. Compared with three traditional methods, the recognition accuracy of the proposed method has increased to 95.7% and the consuming time has decreased to 51.4ms.

Multiple RGB-D sensor-based 3-D reconstruction and localization of indoor environment for mini MAV

Micro aerial vehicle (MAV) with lower cost, smaller size and more flexible flight performance plays a greater role in the global position system-denied (GPS-denied) indoor tasks. This paper focuses on the realization of three-dimensional (3-D) panoramic environment reconstruction and target localization without relying on any external navigation aid. By establishing a visual sensor network (VSN) composed of multiple RGB-Depth (RGB-D) sensors, a fast 3-D model of the observed environment is built utilizing an improved speeded up robust feature (SURF) extraction algorithm and iterative closest point-based (ICP-based) reconstruction algorithm. A distributed data fusion algorithm known as kalman-consensus filter (KCF) is then used to estimate a more accurate global position and trajectory of mini MAV. Both software simulations and real indoor flight experiment results demonstrate that the proposed approach achieves a fast and reliable 3-D map of the indoor environment and the accuracy of localization is largely improved.

Robust feature extraction algorithm suitable for real-time embedded applications

Smart cameras integrate processing close to the image sensor, so they can deliver high-level information to a host computer or high-level decision process. One of the most common processing is the visual features extraction since many vision-based use-cases are based on such algorithm. Unfortunately, in most of cases, features detection algorithms are not robust or do not reach real-time processing. Based on these limitations, a feature detection algorithm that is robust enough to deliver robust features under any type of indoor/outdoor scenarios is proposed. This was achieved by applying a non-textured corner filter combined to a subpixel refinement. Furthermore, an FPGA architecture is proposed. This architecture allows compact system design, real-time processing for Full HD images (it can process up to 44 frames/91.238.400 pixels per second for Full HD images), and high efficiency for smart camera implementations (similar hardware resources than previous formulations without subpixel refinement and without non-textured corner filter). For accuracy/robustness, experimental results for several real-world scenes are encouraging and show the feasibility of our algorithmic approach.

A computer-vision-based rotating speed estimation method for motor bearing fault diagnosis

Diagnosis of motor bearing faults under variable speed is a problem. In this study, a new computer-vision-based order tracking method is proposed to address this problem. First, a video recorded by a high-speed camera is analyzed with the speeded-up robust feature extraction and matching algorithm to obtain the instantaneous rotating speed (IRS) of the motor. Subsequently, an audio signal recorded by a microphone is equi-angle resampled for order tracking in accordance with the IRS curve, through which the frequency-domain signal is transferred to an angular-domain one. The envelope order spectrum is then calculated to determine the fault characteristic order, and finally the bearing fault pattern is determined. The effectiveness and robustness of the proposed method are verified with two brushless direct-current motor test rigs, in which two defective bearings and a healthy bearing are tested separately. This study provides a new noninvasive measurement approach that simultaneously avoids the installation of a tachometer and overcomes the disadvantages of tacholess order tracking methods for motor bearing fault diagnosis under variable speed.

FaRoC: Fast and Robust Supervised Canonical Correlation Analysis for Multimodal Omics Data.

One of the main problems associated with high dimensional multimodal real life data sets is how to extract relevant and significant features. In this regard, a fast and robust feature extraction algorithm, termed as FaRoC, is proposed, integrating judiciously the merits of canonical correlation analysis (CCA) and rough sets. The proposed method extracts new features sequentially from two multidimensional data sets by maximizing their relevance with respect to class label and significance with respect to already-extracted features. To generate canonical variables sequentially, an analytical formulation is introduced to establish the relation between regularization parameters and CCA. The formulation enables the proposed method to extract required number of correlated features sequentially with lesser computational cost as compared to existing methods. To compute both significance and relevance measures of a feature, the concept of hypercuboid equivalence partition matrix of rough hypercuboid approach is used. It also provides an efficient way to find optimum regularization parameters employed in CCA. The efficacy of the proposed FaRoC algorithm, along with a comparison with other existing methods, is extensively established on several real life data sets.

A Self-Regulated Interval Type-2 Neuro-Fuzzy Inference System for Handling Nonstationarities in EEG Signals for BCI

This paper addresses the key problems of nonstationarity and influence of artifacts in electroencephalogram (EEG)-based brain-computer-interface (BCI) systems. The nonstationary nature of EEG data arises due to the physiological/instrumental differences in intra/intersession of the data generation process. This paper proposes a robust common spatial pattern feature extraction algorithm (RoCSP) to overcome the effects of artifacts and a self-regulated interval type-2 neuro-fuzzy inference system (SRIT2NFIS) to handle this inherent nonstationarity. Combined together, this approach is referred to as RoCSP-SRIT2NFIS. The RoCSP algorithm provides better features than the CSP algorithm by excluding those trials that are affected by the artifacts. SRIT2NFIS uses the features generated by the RoCSP algorithm as input and handles the nonstationarity as an uncertainty using the interval type-2 fuzzy sets in the antecedent of fuzzy rules. A self-regulatory learning mechanism is used to evolve the structure automatically and learn the parameters of the network. A regularized projection-based learning algorithm and a modified rule addition criterion are also proposed to improve the generalization performance of SRIT2NFIS. Using benchmark datasets, performance evaluation has been carried out, and the results indicate that, compared with other existing algorithms, RoCSP-SRIT2NFIS produces higher classification accuracy of 3-5% in simple tasks like left-right classification and 6-8% in complex tasks like foot-tongue classification. In addition, a statistical analysis of the performance results indicates that RoCSP-SRIT2NFIS performs better and is more suitable for an efficient BCI.

Automatic 3D Coordinate Extraction from High Resolution Digital Aerial Images

Image matching has been widely used in various fields such as digital photogrammetry, remote sensing, image processing and computer vision. In this study, the speeded up robust feature extraction algorithm for automatic point extraction was applied to a pair of high resolution digital aerial stereo images. In contrast to other studies, the universal transverse mercator coordinates of randomly selected test points were computed automatically with the proposed approach and compared with the same points that were manually measured. As a result, the mean square differences for the UTM coordinates’ in the X, Y and Z axes were calculated as ±0.043, 0.030 and 0.153 m, respectively. It is proved that the proposed algorithm is successful in matching remotely sensed high-resolution images acquired with digital cameras. Moreover, the automatization of the basic tasks in image matching and the 3D coordinate acquisition were also achieved by the algorithm.

Multiple Feature Extraction Techniques in Image Stitching

Image stitching is the process of combining two or more images with overlapping area to produce a panorama of input images. In order to improve performance of creation of panorama, approach that combine different keypoint extraction methods can be used. This paper proposes a methodology for image stitching process that combines various feature detection and extraction algorithms. First, image stitching will be done based on feature keypoint matches. Final image with seam will be blended with image blending technique. The paper summarizes 3 robust feature detection and extraction algorithms namely SURF, SIFT and MSER. Combined set of keypoints will be used for image transformation. According to proposed system, multiple feature extraction techniques can be used for image stitching which can build seamless panorama image. General Terms Image Processing, Image, Image stitching Algorithm, Feature extraction algorithm, Image blending

A Robust Feature Extraction Algorithm for the Classification of Acoustic Targets in Wild Environments

The acoustic recognition technology of unattended ground sensor systems applied in wild environments is faced with the challenge of complicated and strong acoustic noise, especially wind noise. Moreover, the commonly used Mel-frequency cepstral coefficients (MFCCs) are sensitive to noise interference. To resolve the problem, a robust feature extraction method, called harmonic Mel-frequency cepstral coefficients (HMFCCs), is proposed for acoustic target classification. By combining an acoustic signal's harmonic model with the MFCC method, the HMFCC has the ability to emphasize the signals emitted by the principal acoustic components of the target. In the experiment conducted for this study, three data sets are sampled under the same conditions, except for wind power levels. Then the classifier, which is trained by one of the three data sets, is used to recognize the others data sets. According to the experimental results, the HMFCC-based classification accuracies of the three data sets are higher than those of other state-of-the-art methods, indicating that HMFCC is a kind of noise-insensitive feature.

A new deformable mesh model for face tracking using edge based features and novel sets of energy functions

This paper presents a new method for automatic human face locating and tracking. The proposed method consists of two modules including face locating and face tracking. The face locating module has a hierarchical structure, which consists of a skin color classifier together with AdaBoost based face detectors. The face tracking module is considered to be the main contribution of the paper. The module is based on the unstructured 2-D triangular deformable meshes, which employs a new robust and illumination insensitive feature extraction and matching algorithms as well as new sets of mesh energy functions. The feature extraction and matching algorithms are established upon edge points and their representation using fuzzy set theory, which is called fuzzy edges. For matching features, a multiresolution algorithm is utilized based on fuzzy edges and edge pyramid. The new mesh energy functions are also employed to manage both rigid and non-rigid motions in the head and face. Experimental results demonstrate the accuracy and stability of the proposed method for both face locating and face tracking.

EFFECTIVE FACE FEATURE FOR HUMAN IDENTIFICATION

Face image is one of the most important parts of human body. It is easily use for identification process. People naturally identify one another through face images. Due to increase rate of insecurity in our society, accurate machine based face recognition systems are needed to detect impersonators. Face recognition systems comprise of face detector module, preprocessing unit, feature extraction subsystem and classification stage. Robust feature extraction algorithm plays major role in determining the accuracy of intelligent systems that involves image processing analysis. In this paper, pose invariant feature is extracted from human faces. The proposed feature extraction method involves decomposition of captured face image into four sub-bands using Haar wavelet transform thereafter shape and texture features are extracted from approximation and detailed bands respectively. The pose invariant feature vector is computed by fusing the extracted features. Effectiveness of the feature vector in terms of intra-person variation and inter-persons variation was obtained from feature plots.

An explanation of frequency features enabling detection of faults in equally spaced planetary gearbox

Equally spaced planetary gearboxes are important power-train components for varied engineering systems. Their failures can result in significant capital losses and pose safety concerns. The vibration measurements perceived by a sensor mounted on the gearbox housing can provide valuable diagnostic information without normal gearbox operation interference. However, such vibration based monitoring techniques are difficult to implement in planetary gearboxes because of the complex nature of measured vibration spectra that is a result of planets revolving with respect to the stationary sensors mounted on the gearbox housing. Previous research with simulations and experiments using such measurements has reported distinct sideband patterns in the resulting vibration spectra, which differ significantly from the spectra of a normal fixed-axis/parallel gear pair system. In this paper, Fourier series analysis is used to explain these distinct sideband patterns that contain rich diagnostic information. The results obtained are useful to understand the cause of the observed vibration behavior in both healthy and faulty planetary gearboxes and identify the locations of additional frequency components introduced by the damaged gear in a complex measured vibration spectrum. Thus, the formulation presented in this paper can assist in developing robust feature extraction algorithms for early detection of planetary gearbox failures. The theoretical derivations presented in this paper are validated by both dynamic simulations and experiments on a dynamometer test bed using a 750kW gearbox damaged during its operation while installed in a wind turbine. The predicted frequencies for observed faults in the annulus and sun gears of the gearbox are vividly presented in the experimentally measured frequency spectrum.