Search results for "Feature extraction"
showing 10 items of 275 documents
Autoencoders and Recurrent Neural Networks Based Algorithm for Prognosis of Bearing Life
2018
Bearings are one of the most critical components in electric motors, gearboxes and wind turbines. Therefore, bearing fault detection and prognosis of remaining useful life are important to prevent productivity losses. In this study, a novel method is proposed for prognosis of bearing life using an autoencoder and recurrent neural networks-based prediction algorithm. Promising results have been obtained from the experimental data. A monotonic upward trend of the produced health indicator is obtained for all test cases, being one of critical indicators of a proper prognosis. The remaining useful life estimation is moderately accurate under a limited data.
Autoencoders and Data Fusion Based Hybrid Health Indicator for Detecting Bearing and Stator Winding Faults in Electric Motors
2018
The main objective of a condition monitoring programs is to track the health status of critical components of a machine. In this paper, a hybrid health indicator is proposed to monitor the health status of bearings and stator winding of a motor. The proposed method is based on a feature learning from deep autoencoders and data fusion. The features can be learned by autoencoders using individual current and vibration signals, and then learning features are fused to make final health indicators. The experimental data from a permanent magnet synchronous motor is used to validate the proposed method. Promising results in detecting faults and severities of the stator and bearing faults at differ…
Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction
2018
Due the fact that the required therapy to treat Ventricular Fibrillation (V F) is aggressive (electric shock), the lack of a proper detection and recovering therapy could cause serious injuries to the patient or trigger a ventricular fibrillation, or even death. This work describes the development of an automatic diagnostic system for the detection of the occurrence of V F in real time by means of the time-frequency representation (T F R) image of the ECG. The main novelties are the use of the T F R image as input for a classification process, as well as the use of combined classifiers. The feature extraction stage is eliminated and, together with the use of specialized binary classifiers, …
A massive lesion detection algorithm in mammography
2004
A new algorithm for massive lesion detection in mammography is presented. The algorithm consists in three main steps : 1) reduction of the dimension of the image to be processed through the identifi cation of regions of interest (rois) as candidates for massive lesions ; 2) characterization of the roi by means of suitable feature extraction ; 3) pattern classifi cation through supervised neural networks. Suspect regions are detected by searching for local maxima of the pixel grey level intensity. A ring of increasing radius, centered on a maximum, is considered until the mean intensity in the ring decreases to a defi ned fraction of the maximum. The rois thus obtained are described by avera…
Land cover classification of VHR airborne images for citrus grove identification
2011
Abstract Managing land resources using remote sensing techniques is becoming a common practice. However, data analysis procedures should satisfy the high accuracy levels demanded by users (public or private companies and governments) in order to be extensively used. This paper presents a multi-stage classification scheme to update the citrus Geographical Information System (GIS) of the Comunidad Valenciana region (Spain). Spain is the first citrus fruit producer in Europe and the fourth in the world. In particular, citrus fruits represent 67% of the agricultural production in this region, with a total production of 4.24 million tons (campaign 2006–2007). The citrus GIS inventory, created in…
A two-stage fault detection and classification for electric pitch drives in offshore wind farms using support vector machine
2017
This article presents a two-stage fault detection and classification scheme, for induction motor drives in wind turbine pitch systems. The presented approach is suitable for application in offshore wind farms. The adopted strategy utilizes three phase motor current sensing at the pitch drives for fault detection and only when a fault is detected at this stage, features extracted from the current signals are transmitted to a central support vector machine classifier. The proposed method is validated in a laboratory setup of the pitch drive.
Patterned wafer segmentation
2003
This paper is an extension of our previous work on the image segmentation of electronic structures on patterned wafers to improve the defect detection process on optical inspection tools. Die-to-die wafer inspection is based upon the comparison of the same area on two neighborhood dies. The dissimilarities between the images are a result of defects in this area of one of the die. The noise level can vary from one structure to the other, within the same image. Therefore, segmentation is needed to create a mask and apply an optimal threshold in each region. Contrast variation on the texture can affect the response of the parameters used for the segmentation. This paper shows a method to antic…
RootsGLOH2: embedding RootSIFT 'square rooting' in sGLOH2
2020
This study introduces an extension of the shifting gradient local orientation histogram doubled (sGLOH2) local image descriptor inspired by RootSIFT ‘square rooting’ as a way to indirectly alter the matching distance used to compare the descriptor vectors. The extended descriptor, named RootsGLOH2, achieved the best results in terms of matching accuracy and robustness among the latest state-of-the-art non-deep descriptors in recent evaluation contests dealing with both planar and non-planar scenes. RootsGLOH2 also achieves a matching accuracy very close to that obtained by the best deep descriptors to date. Beside confirming that ‘square rooting’ has beneficial effects on sGLOH2 as it happe…
Mammogram Segmentation by Contour Searching and Mass Lesions Classification with Neural Network
2006
The mammography is the most effective procedure for an early diagnosis of the breast cancer. In this paper, an algorithm for detecting masses in mammographic images will be presented. The database consists of 3762 digital images acquired in several hospitals belonging to the MAGIC-5 collaboration (Medical Applications on a Grid Infrastructure Connection). A reduction of the whole image's area under investigation is achieved through a segmentation process, by means of a ROI Hunter algorithm, without loss of meaningful information. In the following classification step, feature extraction plays a fundamental role: some features give geometrical information, other ones provide shape parameters.…
Using Hankel matrices for dynamics-based facial emotion recognition and pain detection
2015
This paper proposes a new approach to model the temporal dynamics of a sequence of facial expressions. To this purpose, a sequence of Face Image Descriptors (FID) is regarded as the output of a Linear Time Invariant (LTI) system. The temporal dynamics of such sequence of descriptors are represented by means of a Hankel matrix. The paper presents different strategies to compute dynamics-based representation of a sequence of FID, and reports classification accuracy values of the proposed representations within different standard classification frameworks. The representations have been validated in two very challenging application domains: emotion recognition and pain detection. Experiments on…