347 research outputs found
Fault Detection and Diagnosis of Electric Drives Using Intelligent Machine Learning Approaches
Electric motor condition monitoring can detect anomalies in the motor performance which have the potential to result in unexpected failure and financial loss. This study examines different fault detection and diagnosis approaches in induction motors and is presented in six chapters. First, an anomaly technique or outlier detection is applied to increase the accuracy of detecting broken rotor bars. It is shown how the proposed method can significantly improve network reliability by using one-class classification technique. Then, ensemble-based anomaly detection is utilized to compare different methods in ensemble learning in detection of broken rotor bars. Finally, a deep neural network is developed to extract significant features to be used as input parameters of the network. Deep autoencoder is then employed to build an advanced model to make predictions of broken rotor bars and bearing faults occurring in induction motors with a high accuracy
Robust Decision Trees Against Adversarial Examples
Although adversarial examples and model robustness have been extensively
studied in the context of linear models and neural networks, research on this
issue in tree-based models and how to make tree-based models robust against
adversarial examples is still limited. In this paper, we show that tree based
models are also vulnerable to adversarial examples and develop a novel
algorithm to learn robust trees. At its core, our method aims to optimize the
performance under the worst-case perturbation of input features, which leads to
a max-min saddle point problem. Incorporating this saddle point objective into
the decision tree building procedure is non-trivial due to the discrete nature
of trees --- a naive approach to finding the best split according to this
saddle point objective will take exponential time. To make our approach
practical and scalable, we propose efficient tree building algorithms by
approximating the inner minimizer in this saddle point problem, and present
efficient implementations for classical information gain based trees as well as
state-of-the-art tree boosting models such as XGBoost. Experimental results on
real world datasets demonstrate that the proposed algorithms can substantially
improve the robustness of tree-based models against adversarial examples
Comparing Computing Platforms for Deep Learning on a Humanoid Robot
The goal of this study is to test two different computing platforms with
respect to their suitability for running deep networks as part of a humanoid
robot software system. One of the platforms is the CPU-centered Intel NUC7i7BNH
and the other is a NVIDIA Jetson TX2 system that puts more emphasis on GPU
processing. The experiments addressed a number of benchmarking tasks including
pedestrian detection using deep neural networks. Some of the results were
unexpected but demonstrate that platforms exhibit both advantages and
disadvantages when taking computational performance and electrical power
requirements of such a system into account.Comment: 12 pages, 5 figure
Behavior-grounded multi-sensory object perception and exploration by a humanoid robot
Infants use exploratory behaviors to learn about the objects around them. Psychologists have theorized that behaviors such as touching, pressing, lifting, and dropping enable infants to form grounded object representations. For example, scratching an object can provide information about its roughness, while lifting it can provide information about its weight. In a sense, the exploratory behavior acts as a ``question\u27\u27 to the object, which is subsequently ``answered by the sensory stimuli produced during the execution of the behavior. In contrast, most object representations used by robots today rely solely on computer vision or laser scan data, gathered through passive observation. Such disembodied approaches to robotic perception may be useful for recognizing an object using a 3D model database, but nevertheless, will fail to infer object properties that cannot be detected using vision alone. To bridge this gap, this dissertation introduces a framework for object perception and exploration in which the robot\u27s representation of objects is grounded in its own sensorimotor experience with them. In this framework, an object is represented by sensorimotor contingencies that span a diverse set of exploratory behaviors and sensory modalities. The results from several large-scale experimental studies show that the behavior-grounded object representation enables a robot to solve a wide variety of tasks including recognition of objects based on the stimuli that they produce, object grouping and sorting, and learning category labels that describe objects and their properties
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
On utilizing weak estimators to achieve the online classification of data streams
Author's accepted version (post-print).Available from 03/09/2021.acceptedVersio
Intelligent tracking of handball players at F.C. Porto and FADEUP
Tese de Mestrado Integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201
Refining understanding of corporate failure through a topological data analysis mapping of Altman’s Z-score model
Corporate failure resonates widely leaving practitioners searching for
understanding of default risk. Managers seek to steer away from trouble, credit
providers to avoid risky loans and investors to mitigate losses. Applying
Topological Data Analysis tools this paper explores whether failing firms from
the United States organise neatly along the five predictors of default proposed
by the Z-score models. Firms are represented as a point cloud in a five
dimensional space, one axis for each predictor. Visualising that cloud using
Ball Mapper reveals failing firms are not often neighbours. As new modelling
approaches vie to better predict firm failure, often using black boxes to
deliver potentially over-fitting models, a timely reminder is sounded on the
importance of evidencing the identification process. Value is added to the
understanding of where in the parameter space failure occurs, and how firms
might act to move away from financial distress. Further, lenders may find
opportunity amongst subsets of firms that are traditionally considered to be in
danger of bankruptcy but actually sit in characteristic spaces where failure
has not occurred
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