DOCSLIB.ORG

Artificial Intelligence Applied to Electromechanical Monitoring, A

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Artificial Intelligence Applied to Electromechanical Monitoring, A ARTIFICIAL INTELLIGENCE APPLIED TO ELECTROMECHANICAL MONITORING, A PERFORMANCE ANALYSIS Erasmus project Authors: Staš Osterc Mentor: Dr. Miguel Delgado Prieto, Dr. Francisco Arellano Espitia 1/5/2020 P a g e II ANNEX VI – DECLARACIÓ D’HONOR P a g e II I declare that, the work in this Master Thesis / Degree Thesis (choose one) is completely my own work, no part of this Master Thesis / Degree Thesis (choose one) is taken from other people’s work without giving them credit, all references have been clearly cited, I’m authorised to make use of the company’s / research group (choose one) related information I’m providing in this document (select when it applies). I understand that an infringement of this declaration leaves me subject to the foreseen disciplinary actions by The Universitat Politècnica de Catalunya - BarcelonaTECH. ___________________ __________________ ___________ Student Name Signature Date Title of the Thesis : _________________________________________________ ________________________________________________________________ ________________________________________________________________ ________________________________________________________________ P a g e II Contents Introduction........................................................................................................................................5 Abstract ..........................................................................................................................................5 Aim .................................................................................................................................................6 Scope of the work ...........................................................................................................................6 Requirements .................................................................................................................................6 Development ......................................................................................................................................7 Background .....................................................................................................................................7 State of art review ........................................................................................................................10 Approach ......................................................................................................................................12 Design and Methodology ..............................................................................................................13 Summary of results ...........................................................................................................................18 Experimental Results.....................................................................................................................18 Conclusions and recommendations for continuation of work ........................................................29 Acknowledgements.......................................................................................................................29 Sources .........................................................................................................................................29 Works Cited ..................................................................................................................................29 P a g e III Table of Figures Figure 1 Acoustic emission sensor ................................................................................................... 11 Figure 2 Test bench ......................................................................................................................... 13 Figure 3 Visual representation of autoencoder ................................................................................ 15 Figure 4 Matlab example of calculating mse .................................................................................... 16 Figure 5 Block diagram for Methodology ......................................................................................... 17 Figure 6 Plot of new Signal............................................................................................................... 18 Figure 7 Plot of all conditions in a matrix ......................................................................................... 19 Figure 8 Plot for healthy condition ................................................................................................... 19 Figure 9 Plot of bearings fault condition .......................................................................................... 20 Figure 10 Plot of demagnetization fault condition............................................................................ 20 Figure 11 Plot of eccentricity fault ................................................................................................... 21 Figure 12 Plot of gear fault condition ............................................................................................... 21 Figure 13 Autoencoder one for testing first hidden layer ................................................................. 22 Figure 14 Autoencoder two for testing second hidden layer ............................................................ 22 Figure 15 Best configuration for hidden layer one and two .............................................................. 22 Figure 16 Autoencoder one for testing L2 Weight Regularization ..................................................... 22 Figure 17 Best configuration for L2 Weight Regularization and Sparsity proportion ......................... 23 Figure 18 Autoencoder for testing Sparsity Regularization ............................................................... 23 Figure 19 The best configuration for Sparsity Regularization ............................................................ 23 Figure 20 Healthy condition and reconstruction .............................................................................. 24 Figure 21 MSE for healthy condition ................................................................................................ 24 Figure 22 Bearings fault condition and reconstruction ..................................................................... 25 Figure 23 MSE for bearings fault ...................................................................................................... 25 Figure 24 Demagnetization fault condition and its reconstruction................................................... 26 Figure 25 MSE error for demagnetization fault ................................................................................ 26 Figure 26 Eccentricity fault condition and reconstruction ................................................................ 27 Figure 27 MSE error for eccentricity fault ........................................................................................ 27 Figure 28 Gear fault condition and reconstruction ........................................................................... 28 Figure 29 MSE error for gear fault.................................................................................................... 28 P a g e 5 Introduction Abstract Artificial intelligence is a wide concept and it’s being used in more and more machine applications, teaching them to perform tasks which would require human intelligence. The implemented algorithm requires samples of ''experience'' from which it can learn and predict the outcome; from there it mainly feeds itself. AI is basically divided into two subsets; deep learning and machine learning. They are mostly distinguished with the way the data is presented to the network. To summarize this project; First the data was monitored on an electromechanical system, monitored data from vibrations was saved and brought in Matlab program, there the data was transformed for future use with autoencoders, which learned the conditions, after training we have to try different parameters for getting the closest reconstruction possible. In the project we applied several techniques like using a multilayered auto-encoder and then finding the best hyper parameters for best results (they were measured by plotting signals and mean square error). P a g e 6 Aim The purpose of an auto-encoder is to learn a representation for a set of data, by training itself with it. With artificial intelligence one can develop machines that are able to read and understand, to us known as natural learning process. In our case the aim was to build 3 auto-encoders with multiple hyper parameters and train an auto- encoder in order to learn a representation for a set of data. To achieve that auto-encoder actually learns something from the process, dimensions of the input and the representation can be adjusted. We learned to construct an artificial neural network for a specific intention of predicting errors in the electromechanical device. Scope of the work Our work contains study of machine learning and study of the data for predicting outputs. Our data was extracted from electromechanical scheme from stator current, speed, gearbox vibration and motor vibrations. One step is the design of the corresponding scripts for testing size, neurons, epochs, L2 weight regularization and sparsity proportion and regularization with the included data. At first the values used were random since we were not yet familiar with function of parameters. Through study and analyze
Load more

Recommended publications
  • Training Autoencoders by Alternating Minimization
    Under review as a conference paper at ICLR 2018 TRAINING AUTOENCODERS BY ALTERNATING MINI- MIZATION Anonymous authors Paper under double-blind review ABSTRACT We present DANTE, a novel method for training neural networks, in particular autoencoders, using the alternating minimization principle. DANTE provides a distinct perspective in lieu of traditional gradient-based backpropagation techniques commonly used to train deep networks. It utilizes an adaptation of quasi-convex optimization techniques to cast autoencoder training as a bi-quasi-convex optimiza- tion problem. We show that for autoencoder configurations with both differentiable (e.g. sigmoid) and non-differentiable (e.g. ReLU) activation functions, we can perform the alternations very effectively. DANTE effortlessly extends to networks with multiple hidden layers and varying network configurations. In experiments on standard datasets, autoencoders trained using the proposed method were found to be very promising and competitive to traditional backpropagation techniques, both in terms of quality of solution, as well as training speed. 1 INTRODUCTION For much of the recent march of deep learning, gradient-based backpropagation methods, e.g. Stochastic Gradient Descent (SGD) and its variants, have been the mainstay of practitioners. The use of these methods, especially on vast amounts of data, has led to unprecedented progress in several areas of artificial intelligence. On one hand, the intense focus on these techniques has led to an intimate understanding of hardware requirements and code optimizations needed to execute these routines on large datasets in a scalable manner. Today, myriad off-the-shelf and highly optimized packages exist that can churn reasonably large datasets on GPU architectures with relatively mild human involvement and little bootstrap effort.
    [Show full text]
  • Turbo Autoencoder: Deep Learning Based Channel Codes for Point-To-Point Communication Channels
    Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels Hyeji Kim Himanshu Asnani Yihan Jiang Samsung AI Center School of Technology ECE Department Cambridge and Computer Science University of Washington Cambridge, United Tata Institute of Seattle, United States Kingdom Fundamental Research [email protected] [email protected] Mumbai, India [email protected] Sewoong Oh Pramod Viswanath Sreeram Kannan Allen School of ECE Department ECE Department Computer Science & University of Illinois at University of Washington Engineering Urbana Champaign Seattle, United States University of Washington Illinois, United States [email protected] Seattle, United States [email protected] [email protected] Abstract Designing codes that combat the noise in a communication medium has remained a significant area of research in information theory as well as wireless communica- tions. Asymptotically optimal channel codes have been developed by mathemati- cians for communicating under canonical models after over 60 years of research. On the other hand, in many non-canonical channel settings, optimal codes do not exist and the codes designed for canonical models are adapted via heuristics to these channels and are thus not guaranteed to be optimal. In this work, we make significant progress on this problem by designing a fully end-to-end jointly trained neural encoder and decoder, namely, Turbo Autoencoder (TurboAE), with the following contributions: (a) under moderate block lengths, TurboAE approaches state-of-the-art performance under canonical channels; (b) moreover, TurboAE outperforms the state-of-the-art codes under non-canonical settings in terms of reliability. TurboAE shows that the development of channel coding design can be automated via deep learning, with near-optimal performance.
    [Show full text]
  • Double Backpropagation for Training Autoencoders Against Adversarial Attack
    1 Double Backpropagation for Training Autoencoders against Adversarial Attack Chengjin Sun, Sizhe Chen, and Xiaolin Huang, Senior Member, IEEE Abstract—Deep learning, as widely known, is vulnerable to adversarial samples. This paper focuses on the adversarial attack on autoencoders. Safety of the autoencoders (AEs) is important because they are widely used as a compression scheme for data storage and transmission, however, the current autoencoders are easily attacked, i.e., one can slightly modify an input but has totally different codes. The vulnerability is rooted the sensitivity of the autoencoders and to enhance the robustness, we propose to adopt double backpropagation (DBP) to secure autoencoder such as VAE and DRAW. We restrict the gradient from the reconstruction image to the original one so that the autoencoder is not sensitive to trivial perturbation produced by the adversarial attack. After smoothing the gradient by DBP, we further smooth the label by Gaussian Mixture Model (GMM), aiming for accurate and robust classification. We demonstrate in MNIST, CelebA, SVHN that our method leads to a robust autoencoder resistant to attack and a robust classifier able for image transition and immune to adversarial attack if combined with GMM. Index Terms—double backpropagation, autoencoder, network robustness, GMM. F 1 INTRODUCTION N the past few years, deep neural networks have been feature [9], [10], [11], [12], [13], or network structure [3], [14], I greatly developed and successfully used in a vast of fields, [15]. such as pattern recognition, intelligent robots, automatic Adversarial attack and its defense are revolving around a control, medicine [1]. Despite the great success, researchers small ∆x and a big resulting difference between f(x + ∆x) have found the vulnerability of deep neural networks to and f(x).
    [Show full text]
  • Unsupervised Speech Representation Learning Using Wavenet Autoencoders Jan Chorowski, Ron J
    1 Unsupervised speech representation learning using WaveNet autoencoders Jan Chorowski, Ron J. Weiss, Samy Bengio, Aaron¨ van den Oord Abstract—We consider the task of unsupervised extraction speaker gender and identity, from phonetic content, properties of meaningful latent representations of speech by applying which are consistent with internal representations learned autoencoding neural networks to speech waveforms. The goal by speech recognizers [13], [14]. Such representations are is to learn a representation able to capture high level semantic content from the signal, e.g. phoneme identities, while being desired in several tasks, such as low resource automatic speech invariant to confounding low level details in the signal such as recognition (ASR), where only a small amount of labeled the underlying pitch contour or background noise. Since the training data is available. In such scenario, limited amounts learned representation is tuned to contain only phonetic content, of data may be sufficient to learn an acoustic model on the we resort to using a high capacity WaveNet decoder to infer representation discovered without supervision, but insufficient information discarded by the encoder from previous samples. Moreover, the behavior of autoencoder models depends on the to learn the acoustic model and a data representation in a fully kind of constraint that is applied to the latent representation. supervised manner [15], [16]. We compare three variants: a simple dimensionality reduction We focus on representations learned with autoencoders bottleneck, a Gaussian Variational Autoencoder (VAE), and a applied to raw waveforms and spectrogram features and discrete Vector Quantized VAE (VQ-VAE). We analyze the quality investigate the quality of learned representations on LibriSpeech of learned representations in terms of speaker independence, the ability to predict phonetic content, and the ability to accurately re- [17].
    [Show full text]
  • De Novo Molecular Design by Combining Deep Autoencoder
    De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping Boris Sattarov, Igor Baskin, Dragos Horvath, Gilles Marcou, Esben Jannik Bjerrum, Alexandre Varnek To cite this version: Boris Sattarov, Igor Baskin, Dragos Horvath, Gilles Marcou, Esben Jannik Bjerrum, et al.. De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping. Journal of Chemical Information and Modeling, American Chemical Society, 2018, 59 (3), pp.1182-1196. 10.1021/acs.jcim.8b00751. hal-02346951 HAL Id: hal-02346951 https://hal.archives-ouvertes.fr/hal-02346951 Submitted on 13 Nov 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping Boris Sattarov a, Igor I. Baskin b, Dragos Horvath a, Gilles Marcou a, Esben Jannik Bjerrum c, Alexandre Varnek a* a Laboratory of Chemoinformatics, UMR 7177 University of Strasbourg/CNRS, 4 rue B. Pascal, 67000 Strasbourg, France b Faculty of Physics, M.V. Lomonosov Moscow State University, Leninskie Gory, Moscow 19991, Russia c Wildcard Pharmaceutical Consulting, Zeaborg Science Center, Frødings Allé 41, 2860 Søborg, Denmark 1.
    [Show full text]
  • Accent Transfer with Discrete Representation Learning and Latent Space Disentanglement
    Accent Transfer with Discrete Representation Learning and Latent Space Disentanglement Renee Li Paul Mure Dept. of Computer Science Dept. of Computer Science Stanford University Stanford University [email protected] [email protected] Abstract The task of accent transfer has recently become a field actively under research. In fact, several companies were established with the goal of tackling this challenging problem. We designed and implemented a model using WaveNet and a multitask learner to transfer accents within a paragraph of English speech. In contrast to the actual WaveNet, our architecture made several adaptations to compensate for the fact that WaveNet does not work well for drastic dimensionality reduction due to the use of residual and skip connections, and we added a multitask learner in order to learn the style of the accent. 1 Introduction In this paper, we explored the task of accent transfer in English speech. This is an interesting problem because it shows the ability of neural network to generate novel contents based on partial information, and this problem has not been explored as much as some of the other topics in style transfer. We believe that this task is very interesting and challenging because we will be developing the state-of-the-art model for our specific task: while there are some published papers about accent classification, so far, we have not been able to find a good pre-trained model to use for evaluating our results. Moreover, raw audio generative models are also a very active field of research, so there is not much literature or implementations for relevant tasks.
    [Show full text]
  • Autoencoder-Based Initialization for Recur- Rent Neural Networks with a Linear Memory
    Under review as a conference paper at ICLR 2020 AUTOENCODER-BASED INITIALIZATION FOR RECUR- RENT NEURAL NETWORKS WITH A LINEAR MEMORY Anonymous authors Paper under double-blind review ABSTRACT Orthogonal recurrent neural networks address the vanishing gradient problem by parameterizing the recurrent connections using an orthogonal matrix. This class of models is particularly effective to solve tasks that require the memorization of long sequences. We propose an alternative solution based on explicit memorization using linear autoencoders for sequences. We show how a recently proposed recurrent architecture, the Linear Memory Network, composed of a nonlinear feedforward layer and a separate linear recurrence, can be used to solve hard memorization tasks. We propose an initialization schema that sets the weights of a recurrent architecture to approximate a linear autoencoder of the input sequences, which can be found with a closed-form solution. The initialization schema can be easily adapted to any recurrent architecture. We argue that this approach is superior to a random orthogonal initialization due to the autoencoder, which allows the memorization of long sequences even before training. The empirical analysis shows that our approach achieves competitive results against alternative orthogonal models, and the LSTM, on sequential MNIST, permuted MNIST and TIMIT. 1 INTRODUCTION Several sequential problems require the memorization of long sequences of patterns. As an example, a generative model for music should be able to memorize long sequences of notes and be able to repeat them, as it is typically done in musical pieces. RNNs and LSTMs struggle to solve even simple memorization tasks (Arjovsky et al., 2015; Graves et al., 2014).
    [Show full text]
  • Audio Word2vec: Unsupervised Learning of Audio Segment Representations Using Sequence-To-Sequence Autoencoder
    INTERSPEECH 2016 September 8–12, 2016, San Francisco, USA Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Autoencoder Yu-An Chung, Chao-Chung Wu, Chia-Hao Shen, Hung-Yi Lee, Lin-Shan Lee College of Electrical Engineering and Computer Science, National Taiwan University {b01902040, b01902038, r04921047, hungyilee}@ntu.edu.tw, [email protected] Abstract identification [4], and several approaches have been success- fully used in STD [10, 6, 7, 8]. But these vector representa- The vector representations of fixed dimensionality for tions may not be able to precisely describe the sequential pho- words (in text) offered by Word2Vec have been shown to be netic structures of the audio segments as we wish to have in this very useful in many application scenarios, in particular due paper, and these approaches were developed primarily in more to the semantic information they carry. This paper proposes a heuristic ways, rather than learned from data. Deep learning has parallel version, the Audio Word2Vec. It offers the vector rep- also been used for this purpose [12, 13]. By learning Recurrent resentations of fixed dimensionality for variable-length audio Neural Network (RNN) with an audio segment as the input and segments. These vector representations are shown to describe the corresponding word as the target, the outputs of the hidden the sequential phonetic structures of the audio segments to a layer at the last few time steps can be taken as the representation good degree, with very attractive real world applications such as of the input segment [13]. However, this approach is supervised query-by-example Spoken Term Detection (STD).
    [Show full text]
  • Nonparametric Guidance of Autoencoder Representations Using Label Information
    JournalofMachineLearningResearch13(2012)2567-2588 Submitted 4/12; Revised 7/12; Published 9/12 Nonparametric Guidance of Autoencoder Representations using Label Information Jasper Snoek [email protected] Department of Computer Science University of Toronto Toronto, ON, Canada Ryan P. Adams [email protected] School of Engineering and Applied Sciences Harvard University Cambridge, MA, USA Hugo Larochelle [email protected] Department of Computer Science University of Sherbrooke Sherbrooke, QC, Canada Editor: Neil Lawrence Abstract While unsupervised learning has long been useful for density modeling, exploratory data analysis and visualization, it has become increasingly important for discovering features that will later be used for discriminative tasks. Discriminative algorithms often work best with highly-informative features; remarkably, such features can often be learned without the labels. One particularly ef- fective way to perform such unsupervised learning has been to use autoencoder neural networks, which find latent representations that are constrained but nevertheless informative for reconstruc- tion. However, pure unsupervised learning with autoencoders can find representations that may or may not be useful for the ultimate discriminative task. It is a continuing challenge to guide the training of an autoencoder so that it finds features which will be useful for predicting labels. Similarly, we often have a priori information regarding what statistical variation will be irrelevant to the ultimate discriminative task, and we would like to be able to use this for guidance as well. Although a typical strategy would be to include a parametric discriminative model as part of the autoencoder training, here we propose a nonparametric approach that uses a Gaussian process to guide the representation.
    [Show full text]
  • Anomaly Detection of Power Plant Equipment Using Long Short-Term Memory Based Autoencoder Neural Network
    sensors Article Anomaly Detection of Power Plant Equipment Using Long Short-Term Memory Based Autoencoder Neural Network Di Hu , Chen Zhang, Tao Yang * and Gang Chen School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; [email protected] (D.H.); [email protected] (C.Z.); [email protected] (G.C.) * Correspondence: [email protected] Received: 7 October 2020; Accepted: 27 October 2020; Published: 29 October 2020 Abstract: Anomaly detection is of great significance in condition-based maintenance of power plant equipment. The conventional fixed threshold detection method is not able to perform early detection of equipment abnormalities. In this study, a general anomaly detection framework based on a long short-term memory-based autoencoder (LSTM-AE) network is proposed. A normal behavior model (NBM) is established to learn the normal behavior patterns of the operating variables of the equipment in space and time. Based on the similarity analysis between the NBM output distribution and the corresponding measurement distribution, the Mahalanobis distance (MD) is used to describe the overall residual (OR) of the model. The reasonable range is obtained using kernel density estimation (KDE) with a 99% confidence interval, and the OR is monitored to detect abnormalities in real-time. An induced draft fan is chosen as a case study. Results show that the established NBM has excellent accuracy and generalizability, with average root mean square errors of 0.026 and 0.035 for the training and test data, respectively, and average mean absolute percentage errors of 0.027%. Moreover, the abnormal operation case shows that the proposed framework can be effectively used for the early detection of abnormalities.
    [Show full text]
  • Unsupervised Feature Extraction with Autoencoder Trees
    Neurocomputing 258 (2017) 63–73 Contents lists available at ScienceDirect Neurocomputing journal homepage: www.elsevier.com/locate/neucom Unsupervised feature extraction with autoencoder trees ∗ Ozan Irsoy˙ a, , Ethem Alpaydın b a Department of Computer Science, Cornell University, Ithaca, NY 14853, United States b Department of Computer Engineering, Bo gaziçi˘ University, Bebek, Istanbul˙ 34342, Turkey a r t i c l e i n f o a b s t r a c t Article history: The autoencoder is a popular neural network model that learns hidden representations of unlabeled data. Received 20 May 2016 Typically, single- or multilayer perceptrons are used in constructing an autoencoder, but we use soft de- Revised 31 January 2017 cision trees (i.e., hierarchical mixture of experts) instead. Such trees have internal nodes that implement Accepted 18 February 2017 soft multivariate splits through a gating function and all leaves are weighted by the gating values on Available online 16 March 2017 their path to get the output. The encoder tree converts the input to a lower dimensional representation MSC: in its leaves, which it passes to the decoder tree that reconstructs the original input. Because the splits 00-01 are soft, the encoder and decoder trees can be trained back to back with stochastic gradient-descent to 99-00 minimize reconstruction error. In our experiments on handwritten digits, newsgroup posts, and images, we observe that the autoencoder trees yield as small and sometimes smaller reconstruction error when Keywords: compared with autoencoder perceptrons. One advantage of the tree is that it learns a hierarchical repre- Unsupervised learning Decision trees sentation at different resolutions at its different levels and the leaves specialize at different local regions Autoencoders in the input space.
    [Show full text]
  • Segmental Audio Word2vec: Representing Utterances As Sequences of Vectors with Applications in Spoken Term Detection
    SEGMENTAL AUDIO WORD2VEC: REPRESENTING UTTERANCES AS SEQUENCES OF VECTORS WITH APPLICATIONS IN SPOKEN TERM DETECTION Yu-Hsuan Wang, Hung-yi Lee, Lin-shan Lee College of Electrical Engineering and Computer Science National Taiwan University fr04922167, [email protected], [email protected] ABSTRACT structure information for a spoken word. This actually extends While Word2Vec represents words (in text) as vectors carrying se- the audio Word2Vec from word-level up to utterance-level. Such mantic information, audio Word2Vec was shown to be able to rep- segmental audio Word2Vec can have plenty of potential applications resent signal segments of spoken words as vectors carrying phonetic in the future, for example, speech information summarization, structure information. Audio Word2Vec can be trained in an un- speech-to-speech translation or voice conversion[14]. Here we show supervised way from an unlabeled corpus, except the word bound- the very attractive first application in spoken term detection. aries are needed. In this paper, we extend audio Word2Vec from The segmental audio Word2Vec proposed in this paper is word-level to utterance-level by proposing a new segmental audio based on a segmental sequence-to-sequence autoencoder (SSAE) Word2Vec, in which unsupervised spoken word boundary segmenta- for learning a segmentation gate and a sequence-to-sequence au- tion and audio Word2Vec are jointly learned and mutually enhanced, toencoder jointly. The former determines the word boundaries in so an utterance can be directly represented as a sequence of vectors the utterance, and the latter represents each audio segment with an carrying phonetic structure information. This is achieved by a seg- embedding vector.
    [Show full text]