scholarly journals Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 712
Author(s):  
Pawel Ewert ◽  
Teresa Orlowska-Kowalska ◽  
Kamila Jankowska
Keyword(s):  
Neural Networks ◽  
Network Architecture ◽  
Mechanical Damage ◽  
Rolling Bearing ◽  
Industrial Applications ◽  
Point Of View ◽  
Self Organizing Map ◽  
Base Function ◽  
Acceleration Signal ◽  
The Hilbert Transform

Permanent magnet synchronous motors (PMSMs) are becoming more popular, both in industrial applications and in electric and hybrid vehicle drives. Unfortunately, like the others, these are not reliable drives. As in the drive systems with induction motors, the rolling bearings can often fail. This paper focuses on the possibility of detecting this type of mechanical damage by analysing mechanical vibrations supported by shallow neural networks (NNs). For the extraction of diagnostic symptoms, the Fast Fourier Transform (FFT) and the Hilbert transform (HT) were used to obtain the envelope signal, which was subjected to the FFT analysis. Three types of neural networks were tested to automate the detection process: multilayer perceptron (MLP), neural network with radial base function (RBF), and Kohonen map (self-organizing map, SOM). The input signals of these networks were the amplitudes of harmonic components characteristic of damage to bearing elements, obtained as a result of FFT or HT analysis of the vibration acceleration signal. The effectiveness of the analysed NN structures was compared from the point of view of the influence of the network architecture and various parameters of the learning process on the detection effectiveness.

scholarly journals Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive

2021 ◽  
Vol 6 (1) ◽  
pp. 100-112
Author(s):  
Kamila Jankowska ◽  
Pawel Ewert
Keyword(s):  
Neural Network ◽  
Confusion Matrix ◽  
Mechanical Vibration ◽  
Rolling Bearing ◽  
Point Of View ◽  
Permanent Magnet Synchronous Motors ◽  
Kohonen Neural Network ◽  
Acceleration Signal ◽  
The Hilbert Transform ◽  
Bearing Damage

Abstract Due to their many advantages, permanent magnet synchronous motors (PMSMs) are increasingly used in not only industrial drive systems but also electric and hybrid vehicle drives, aviation and other applications. Unfortunately, PMSMs are not free from damage that occurs during their operation. It is assumed that about 40% of the damage that occurs is related to rolling bearing damage. This article focuses on the use of Kohonen neural network (KNN) for rolling bearing damage detection in a PMSM drive system. The symptoms from the fast Fourier transform (FFT) and Envelope (ENV) Analysis of the mechanical vibration acceleration signal were analysed. The signal ENV was obtained by applying the Hilbert transform (HT). Two neural network functions are discussed: a detector and a classifier. The detector detected the damage and the classifier determined the type of damage to the rolling bearing (undamaged bearing, damaged rolling element, outer or inner race). The effectiveness of the analysed networks from the point of view of the applied signal processing method, map size, type of neighbourhood radius, distance function and the influence of input data normalisation are presented. The results are presented in the form of a confusion matrix, together with 2D and 3D maps of active neurons.

scholarly journals Representation of Linguistic Form and Function in Recurrent Neural Networks

2017 ◽  
Vol 43 (4) ◽  
pp. 761-780 ◽  
Author(s):  
Ákos Kádár ◽  
Grzegorz Chrupała ◽  
Afra Alishahi
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Network Architecture ◽  
Language Model ◽  
Visual Pathway ◽  
Recurrent Network ◽  
Point Of View ◽  
Language Models ◽  
Grammatical Function ◽  
Form And Function

We present novel methods for analyzing the activation patterns of recurrent neural networks from a linguistic point of view and explore the types of linguistic structure they learn. As a case study, we use a standard standalone language model, and a multi-task gated recurrent network architecture consisting of two parallel pathways with shared word embeddings: The Visual pathway is trained on predicting the representations of the visual scene corresponding to an input sentence, and the Textual pathway is trained to predict the next word in the same sentence. We propose a method for estimating the amount of contribution of individual tokens in the input to the final prediction of the networks. Using this method, we show that the Visual pathway pays selective attention to lexical categories and grammatical functions that carry semantic information, and learns to treat word types differently depending on their grammatical function and their position in the sequential structure of the sentence. In contrast, the language models are comparatively more sensitive to words with a syntactic function. Further analysis of the most informative n-gram contexts for each model shows that in comparison with the Visual pathway, the language models react more strongly to abstract contexts that represent syntactic constructions.

scholarly journals Self-organized map: the new aproach for study of genetic divergence in kale

2020 ◽  
Author(s):  
Orlando Gonçalves Brito ◽  
Valter Carvalho de Andrade Júnior ◽  
Alcinei Mistico Azevedo ◽  
Maria Thereza Netta Lopes Silva ◽  
Ludimila Geiciane de Sá ◽  
...  
Keyword(s):  
Neural Networks ◽  
Discriminant Analysis ◽  
Genetic Divergence ◽  
Network Architecture ◽  
Network Architectures ◽  
Self Organizing Map ◽  
Network Configuration ◽  
Dissimilarity Matrix ◽  
Self Organized ◽  
Plant Level

The objective of this study was to study the genetic divergence between genotypes of kale, to propose a methodology for the use of neural networks of the SOM type and to test its efficiency through Anderson's discriminant analysis. We evaluated 33 families of half-siblings of kale and three commercial cultivars. The design was a randomized block with four replications with six plants per plot. A total of 14 plant-level quantitative traits were evaluated. Genetic values were predicted at family level via REML / BLUP. For the study of divergence, neural networks of the SOM type (Self-organizing Map) were adopted. We evaluated different network architectures, whose consistencies of the clusters were identified by the Anderson discriminant analysis and by the number of empty clusters. After selecting the best network configuration, a dissimilarity matrix was obtained, from which a dendrogram was constructed using the UPGMA method. The best network architecture was formed with five rows and one column, totaling five neurons and consequently five clusters. The greatest dissimilarity was established between clusters I and V. The crossing between the genotypes of cluster I and those belonging to clusters III and V are the most recommended, since they aim to recombine families with characteristics of interest to the improvement and high dissimilarity. Anderson's discriminant analysis showed that the genotype classification was 100% correct, indicating the efficiency of the methodology used.

scholarly journals Memprediksi Jumlah Siswa Baru Menggunakan Metode Backpropagation (STUDI KASUS: SMK HARAPAN BANGSA KUALA)

2021 ◽  
Vol 3 (1) ◽  
pp. 10-16
Author(s):  
Kris Jayanti ◽  
Katen Lumbanbatu ◽  
Suci Ramadani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Time Series Data ◽  
Real Data ◽  
Activation Function ◽  
Series Data ◽  
Base Function ◽  
Artificial Neural

Artificial Neural Network (ANN) and time series data can be used for forecasting methods well. Artificial Neural Network is a method whose working principle is adapted from a mathematical model in humans or biological nerves. Neural networks are characterized by; (1) the pattern of connections between neurons (called architecture), (2) determining the weight of the connection (called training or learning), and (3) the activation function. The research objective was to obtain the best artificial neural network architecture, comparing the two methods of Backpropogation Neural Networks with the Radial Base Function Artificial Neural Network (RBF) method. This research is a research using real data (true experimental). This research was conducted at SMK Harapan Bangsa Kuala, which was obtained from 2015 to 2019. The results showed that for one iteration using the backpropagation method the result was 0,378197657 with a squared error 0.143033468, then the results achieved were not in accordance with the target.

scholarly journals Topological Uncertainty: Monitoring Trained Neural Networks through Persistence of Activation Graphs

2021 ◽  
Author(s):  
Théo Lacombe ◽  
Yuichi Ike ◽  
Mathieu Carrière ◽  
Frédéric Chazal ◽  
Marc Glisse ◽  
...  
Keyword(s):  
Neural Networks ◽  
Network Architecture ◽  
Data Augmentation ◽  
Industrial Applications ◽  
Data Types ◽  
Open World ◽  
Use Of Data ◽  
Uncertainty Monitoring ◽  
Final Layer ◽  
Benchmark Datasets

Although neural networks are capable of reaching astonishing performance on a wide variety of contexts, properly training networks on complicated tasks requires expertise and can be expensive from a computational perspective. In industrial applications, data coming from an open-world setting might widely differ from the benchmark datasets on which a network was trained. Being able to monitor the presence of such variations without retraining the network is of crucial importance. In this paper, we develop a method to monitor trained neural networks based on the topological properties of their activation graphs. To each new observation, we assign a Topological Uncertainty, a score that aims to assess the reliability of the predictions by investigating the whole network instead of its final layer only as typically done by practitioners. Our approach entirely works at a post-training level and does not require any assumption on the network architecture, optimization scheme, nor the use of data augmentation or auxiliary datasets; and can be faithfully applied on a large range of network architectures and data types. We showcase experimentally the potential of Topological Uncertainty in the context of trained network selection, Out-Of-Distribution detection, and shift-detection, both on synthetic and real datasets of images and graphs.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

Enzyme Promiscuous Activity: How to Define it and its Evolutionary Aspects

2020 ◽  
Vol 27 (5) ◽  
pp. 400-410
Author(s):  
Valentina De Luca ◽  
Luigi Mandrich
Keyword(s):  
Gene Evolution ◽  
Sequence Divergence ◽  
High Specificity ◽  
Industrial Applications ◽  
Point Of View ◽  
Enzyme Promiscuity ◽  
Primary Activity ◽  
Starting Point ◽  
Main Activity

: Enzymes are among the most studied biological molecules because better understanding enzymes structure and activity will shed more light on their biological processes and regulation; from a biotechnological point of view there are many examples of enzymes used with the aim to obtain new products and/or to make industrial processes less invasive towards the environment. Enzymes are known for their high specificity in the recognition of a substrate but considering the particular features of an increasing number of enzymes this is not completely true, in fact, many enzymes are active on different substrates: this ability is called enzyme promiscuity. Usually, promiscuous activities have significantly lower kinetic parameters than to that of primary activity, but they have a crucial role in gene evolution. It is accepted that gene duplication followed by sequence divergence is considered a key evolutionary mechanism to generate new enzyme functions. In this way, promiscuous activities are the starting point to increase a secondary activity in the main activity and then get a new enzyme. The primary activity can be lost or reduced to a promiscuous activity. In this review we describe the differences between substrate and enzyme promiscuity, and its rule in gene evolution. From a practical point of view the knowledge of promiscuity can facilitate the in vitro progress of proteins engineering, both for biomedical and industrial applications. In particular, we report cases regarding esterases, phosphotriesterases and cytochrome P450.

scholarly journals Deep Convolutional Neural Network with RNNs for Complex Activity Recognition Using Wrist-Worn Wearable Sensor Data

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1685
Author(s):  
Sakorn Mekruksavanich ◽  
Anuchit Jitpattanakul
Keyword(s):  
Neural Networks ◽  
Activity Recognition ◽  
Human Activities ◽  
Recognition Performance ◽  
Confusion Matrix ◽  
Experimental Studies ◽  
Industrial Applications ◽  
Sensor Data ◽  
Complex Activity ◽  
Activity Data

Sensor-based human activity recognition (S-HAR) has become an important and high-impact topic of research within human-centered computing. In the last decade, successful applications of S-HAR have been presented through fruitful academic research and industrial applications, including for healthcare monitoring, smart home controlling, and daily sport tracking. However, the growing requirements of many current applications for recognizing complex human activities (CHA) have begun to attract the attention of the HAR research field when compared with simple human activities (SHA). S-HAR has shown that deep learning (DL), a type of machine learning based on complicated artificial neural networks, has a significant degree of recognition efficiency. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two different types of DL methods that have been successfully applied to the S-HAR challenge in recent years. In this paper, we focused on four RNN-based DL models (LSTMs, BiLSTMs, GRUs, and BiGRUs) that performed complex activity recognition tasks. The efficiency of four hybrid DL models that combine convolutional layers with the efficient RNN-based models was also studied. Experimental studies on the UTwente dataset demonstrated that the suggested hybrid RNN-based models achieved a high level of recognition performance along with a variety of performance indicators, including accuracy, F1-score, and confusion matrix. The experimental results show that the hybrid DL model called CNN-BiGRU outperformed the other DL models with a high accuracy of 98.89% when using only complex activity data. Moreover, the CNN-BiGRU model also achieved the highest recognition performance in other scenarios (99.44% by using only simple activity data and 98.78% with a combination of simple and complex activities).

MeliusNet: An Improved Network Architecture for Binary Neural Networks

2021 ◽  
Author(s):  
Joseph Bethge ◽  
Christian Bartz ◽  
Haojin Yang ◽  
Ying Chen ◽  
Christoph Meinel
Keyword(s):  
Neural Networks ◽  
Network Architecture

scholarly journals SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

2021 ◽  
Vol 40 (3) ◽  
pp. 1-13
Author(s):  
Lumin Yang ◽  
Jiajie Zhuang ◽  
Hongbo Fu ◽  
Xiangzhi Wei ◽  
Kun Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Large Scale ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Structure Information ◽  
Graph Neural Networks ◽  
Node Labels ◽  
Point Level

We introduce SketchGNN , a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture to extract the features at three levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.

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