889 research outputs found
Imaging time series for the classification of EMI discharge sources
In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new and improved feature extraction and data dimension reduction algorithms based on image processing techniques. The approach is to exploit the Gramian Angular Field technique to map the measured EMI time signals to an image, from which the significant information is extracted while removing redundancy. The image of each discharge type contains a unique fingerprint. Two feature reduction methods called the Local Binary Pattern (LBP) and the Local Phase Quantisation (LPQ) are then used within the mapped images. This provides feature vectors that can be implemented into a Random Forest (RF) classifier. The performance of a previous and the two new proposed methods, on the new database set, is compared in terms of classification accuracy, precision, recall, and F-measure. Results show that the new methods have a higher performance than the previous one, where LBP features achieve the best outcome
Automatic extraction of faults and fractal analysis from remote sensing data
Object-based classification is a promising technique for image classification. Unlike pixel-based methods, which only use the measured radiometric values, the object-based techniques can also use shape and context information of scene textures. These extra degrees of freedom provided by the objects allow the automatic identification of geological structures. In this article, we present an evaluation of object-based classification in the context of extraction of geological faults. Digital elevation models and radar data of an area near Lake Magadi (Kenya) have been processed. We then determine the statistics of the fault populations. The fractal dimensions of fault dimensions are similar to fractal dimensions directly measured on remote sensing images of the study area using power spectra (PSD) and variograms. These methods allow unbiased statistics of faults and help us to understand the evolution of the fault systems in extensional domains. Furthermore, the direct analysis of image texture is a good indicator of the fault statistics and allows us to classify the intensity and type of deformation. We propose that extensional fault networks can be modeled by iterative function system (IFS)
Mechanical Wear Debris Feature, Detection, and Diagnosis: A Review
Mechanical debris is an important product of friction wear, which is also a crucial approach to know the running status of a machine. Many studies have been conducted on mechanical debris in related fields such as tribology, instrument, and diagnosis. This paper presents a comprehensive review of these studies, which summarizes wear mechanisms (e.g., abrasive wear, fatigue wear, and adhesive wear) and debris features (e.g., concentration (number), size, morphology, and composition), analyzes detection methods principles (e.g., offline: spectrograph and ferrograph, and online: optical method, inductive method, resistive-capacitive method, and acoustic method), reviews developments of online inductive methods, and investigates the progress of debris-based diagnosis. Finally, several notable problems are discussed for further studies. (C) 2017 Chinese Society of Aeronautics and Astronautics
Fractal-Constrained Crosshole/Borehole-to-Surface Full Waveform Inversion for Hydrogeological Applications Using Ground-Penetrating Radar
Peer reviewedPostprin
Semi-supervised multiscale dual-encoding method for faulty traffic data detection
Inspired by the recent success of deep learning in multiscale information
encoding, we introduce a variational autoencoder (VAE) based semi-supervised
method for detection of faulty traffic data, which is cast as a classification
problem. Continuous wavelet transform (CWT) is applied to the time series of
traffic volume data to obtain rich features embodied in time-frequency
representation, followed by a twin of VAE models to separately encode normal
data and faulty data. The resulting multiscale dual encodings are concatenated
and fed to an attention-based classifier, consisting of a self-attention module
and a multilayer perceptron. For comparison, the proposed architecture is
evaluated against five different encoding schemes, including (1) VAE with only
normal data encoding, (2) VAE with only faulty data encoding, (3) VAE with both
normal and faulty data encodings, but without attention module in the
classifier, (4) siamese encoding, and (5) cross-vision transformer (CViT)
encoding. The first four encoding schemes adopted the same convolutional neural
network (CNN) architecture while the fifth encoding scheme follows the
transformer architecture of CViT. Our experiments show that the proposed
architecture with the dual encoding scheme, coupled with attention module,
outperforms other encoding schemes and results in classification accuracy of
96.4%, precision of 95.5%, and recall of 97.7%.Comment: 16 pages, 8 figure
Characterisation of Dynamic Process Systems by Use of Recurrence Texture Analysis
This thesis proposes a method to analyse the dynamic behaviour of process systems using sets of textural features extracted from distance matrices obtained from time series data. Algorithms based on the use of grey level co-occurrence matrices, wavelet transforms, local binary patterns, textons, and the pretrained convolutional neural networks (AlexNet and VGG16) were used to extract features. The method was demonstrated to effectively capture the dynamics of mineral process systems and could outperform competing approaches
FaultFace: Deep Convolutional Generative Adversarial Network (DCGAN) based Ball-Bearing Failure Detection Method
Failure detection is employed in the industry to improve system performance
and reduce costs due to unexpected malfunction events. So, a good dataset of
the system is desirable for designing an automated failure detection system.
However, industrial process datasets are unbalanced and contain little
information about failure behavior due to the uniqueness of these events and
the high cost for running the system just to get information about the
undesired behaviors. For this reason, performing correct training and
validation of automated failure detection methods is challenging. This paper
proposes a methodology called FaultFace for failure detection on Ball-Bearing
joints for rotational shafts using deep learning techniques to create balanced
datasets. The FaultFace methodology uses 2D representations of vibration
signals denominated faceportraits obtained by time-frequency transformation
techniques. From the obtained faceportraits, a Deep Convolutional Generative
Adversarial Network is employed to produce new faceportraits of the nominal and
failure behaviors to get a balanced dataset. A Convolutional Neural Network is
trained for fault detection employing the balanced dataset. The FaultFace
methodology is compared with other deep learning techniques to evaluate its
performance in for fault detection with unbalanced datasets. Obtained results
show that FaultFace methodology has a good performance for failure detection
for unbalanced datasets
- …