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