Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Networks

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1 Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Networks Presented by: Parsa Yousefi Supervisors: Dr. M. Jamshidi, Dr. P. Benavidez June 23 rd, 2017

2 Outline Data Analytics Introduction Clustering Neural Networks Long Short-Term Memory Data prediction Latency Reconstructing Data Using LSTM Fault Detection Training Initial Model Using LSTM Future Works Acknowledgments References Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 2

3 Data Analytics Introduction Definition: Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements. Big Data Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 3

4 Data Analytics A Summary of Data Science Process: Data Collection Processing Cleaning Data Product Data Communication Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 4

5 Data Analytics Processing Methods Clustering Neural Networks Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 5

6 Processing Methods Data Clustering Definition Centroid-based Clustering Density-based Clustering Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 6

7 Data Clustering Centroid-based Clustering Initiating centroids Finding nearest members to centroids Calculating New Centroids Repeating the method until convergence Advantage Convergence Speed Disadvantage Number of Centroids as an input Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 7

8 Data Clustering Density-based Clustering Defining clusters as areas of higher density Advantage No need to set the number of clusters as input Disadvantage Not applicable for datasets with large differences DBSCAN-Illustration.svg/400px-DBSCAN-Illustration.svg.png Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 8

9 Processing Methods Neural Networks What is a Neural Network? Definition of Dr. Hecht- Nielsen: A computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs. The Basics Applications Long Short-Term Memory Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 9

10 Neural Networks Long Short-Term Memory A type of Recurrent Neural Networks Introduced by S. Hochreiter and J. Schmidhuber in 1997 The Core Idea Why LSTM? cb5116c9dfc5897c7296.png Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 10

11 Data Prediction Using LSTM Thrust 1, Sub-thrust 1-1 of TECHLAV Problem definition? The latency in sending and receiving data by UAVs and UGVs in the presence of heavy computation The latency is an effect of limitation in communication speed Solution? Prediction of future data based on the current data with high accuracy and reconstructing it Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 11

12 Data Prediction Using LSTM Feeding Data to UAVs Latency (Delay) Cloud Processor Unit Computation Feeding Data to UGVs Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 12

13 Data Prediction Using LSTM Our Methodology Receiving data from Reference [1], [2], and Creating Dataset 12,000 samples of angular error Simulating Latency Use 70% of Dataset as the current data Feed the current data to the Neural Network (LSTM) for Training Finding the pattern of data Forecasting next 30% of dataset by NN Reconstructing Data by Adding 70% of the Original Data and the 30% forecasted one Evaluating the predicted data comparing with original dataset Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 13

14 Data Prediction Using LSTM The Objective Original Dataset Latency 70% of Original Dataset Time Series Forecasting Reconstructed Data Predictive Model Forecasted 30% Reconstruction Method Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 14

15 Data Prediction Using LSTM The Structure of LSTM One input layer One hidden layer with four LSTM neurons Input gate Output gate Forget gate Current Condition of the network One output layer Sigmoid Function x(t) Input Gate o(t) i(t) sig c(t) sig f(t) Forget Gate Output Gate h(t) Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 15

16 Data Prediction Using LSTM The Structure of LSTM Sigmoid Function: Used as an activation function for all LSTM Blocks Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 16

17 Data Prediction Using LSTM Results Root mean square error for train: Root mean square error for test: % accuracy in training 97.68% accuracy in testing Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 17

18 Data Prediction Using LSTM Results Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 18

19 Data Prediction Using LSTM Results Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 19

20 Data Prediction Using LSTM Results Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 20

21 Data Prediction Using LSTM Results Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 21

22 Data Prediction Using LSTM Challenges: Despite the regression forecasting, time series prediction can be more challenging, due to the fact that sequence dependence increases the complexity of the problem. Our approach should be able to handle long sequence dependencies. Gaps: When the available data is only a small portion of total data, it is almost impossible to achieve a proper estimation of the lost data. Having a dataset including noise Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 22

23 Data Prediction Using LSTM Future Works Using LSTM for a noisy dataset and comparing the results Using bi-directional recurrent neural networks in case of irregular dispersed data loss for using both past and future samples Using Centroid-based and Density-based clustering methods for classification the dataset Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 23

24 Fault Detection Using LSTM Thrust 2, Sub-thrust 2-1 of TECHLAV Problem definition? Failure in sensors, actuators, and components of a robot Propagating wrong data to the computation unit Failure in completing tasks Solution? Using Another agent for observing fault Using neural networks for forecasting fault in the future Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 24

25 Fault Detection Using LSTM Our Methodology Using two Kobuki TurtleBot 2, the first as the remote observer Helper, and the second as the Faulty Inducing mechanical fault on the odometry sensors of Faulty by using electrical tape to modify the friction of the wheels Measuring the sensors of the Helper and the Faulty 19,000 testing datapoints Training the model using LSTM Validating the model with high accuracy and prediction of future fault Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 25

26 Fault Detection Using LSTM Type of faults No Fault Right wheel fault Left wheel fault Fault in both wheels Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 26

27 Fault Detection Using LSTM Results Training the initial fault detection model with LSTM and Testing it Using 6 inputs from the odometry sensors (x and y position, orientation in quaternions) 4 outputs (no fault, left fault, right fault, both faults) The testing accuracy of 88% on ~19,000 datapoints Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 27

28 Fault Detection Using LSTM Results Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 28

29 Fault Detection Using LSTM Challenges Sensing: For a remote observer robot Helper to be able to detect a fault in the Faulty the sensors on the robot need to be able to capture the fault in sufficient detail. Cameras for instance need to be capable of recording at a rate high enough to capture the fault over more than one image frame. Computation: The fault diagnosis algorithm should be able to run near real-time in order to make it useful in live detection of faults in the system. Training of the models should be completed with sufficient amounts of training data to capture types and levels of faults. Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 29

30 Fault Detection Using LSTM Future Works Integrating JSON, the extended version of AgentSpeak into our ROS system through the use of ROSJAVA and RSON, the ROS interface to JSON Using camera on Helper for processing the images captured from Faulty to detect the faults Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 30

31 Acknowledgments These researches are supported by Air Force Research Laboratory and OSD for sponsoring this research under agreement number FA The Thrust 1, Sub-thrust 1-1 was done with collaboration with Nima Ebadi, Ph.D. student of ECE Department at UTSA The Thrust 2, Sub-thrust 2-1 was done by Jonathan Lwowski and Shubham Sarpal, Ph.D. and M.Sc. students of ECE Department at UTSA Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 31

32 References N. Gamez, P. Kolar and M. Jamshidi, Impact of Time Delays on Networked Control of Autonomous Systems," a chapter in Beyond Traditional Probabilistic Data Processing Techniques: Interval and Fuzzy Logic Methods and Their Applications}", Springer-Verlag, Heidelberg, German, to appear in N. Gamez, Modeling, Simulation, and Design of a Time-Delayed Multi-Agent System of Autonomous Vehicles, Masters Thesis, University of Texas at San Antonio, J. Schmidhuber, D. Wierstra, and F. J. Gomez Evolino, Hybrid Neuroevolution and Optimal Linear Search for Sequence Learning, Proceedings of the 19 th International Joint Conference on Artificial Intelligence (IJCAI), Edinburgh, pp , K. Greff, R. Srivastava, J. Koutník, B. Steunebrink and J. Schmidhuber, LSTM: A Search Space Odyssey, Cornell University Library, March L. N. Smith, Best Practices for Applying Deep Learning to Novel Applications, Navy Center for Applied Research in Artificial Intelligence, Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 32

33 Thanks for your attention! Data Analysis, Estimation, and Fault detection of Large-Scale Autonomous System of Vehicles Using Neural Network 33