How it really works! Advanced streaming and edge analytics methods in industry 4.0 Dr. Nicole Tschauder, SAS DACH Industry of Things World 2018,

Transcription

1 How it really works! Advanced streaming and edge analytics methods in industry 4.0 Dr. Nicole Tschauder, SAS DACH Industry of Things World 2018, Berlin

2 IoT-Projects are like building a house It only succeeds through the interaction of various factors Business cases Sensors IT infrastructure Analytics Algorithms Edge & streaming analytics technology Connectivity Data management, cloud platforms Change management

3 IoT-Projects are like building a house It only succeeds through the interaction of various factors Business cases Sensors IT infrastructure Analytics Algorithms Edge & streaming analytics technology Connectivity Data management, cloud platforms Change management

4 Live Demo Edge Analytics for production quality Right here at the booth :)

5

6 IoT data present us with new challenges Many variables, high frequency Unknown data, unexplored to date Noisy, missing values How much data should the Edge send back? Which amount of data should be stored? Heavily imbalanced data sets Looking for predictive power in a huge haystack

7 The Analytics Lifecycle The three phases of an analytical model

8 IoT data require specific functionalities in each phase Transform IoT Data Process streaming data Preprocess IoT Data Intelligently filter IoT data Reduce dimensions Create features Detect anomalies Monitor process/asset degradation

9 IoT data require specific functionalities in each phase Transform IoT Data Process streaming data Preprocess IoT Data Intelligently filter IoT data We need new analytical techniques. Detect anomalies Reduce dimensions Create features Monitor process/asset degradation

10 Example 1

11 Short-Time Fourier Transform The STFT of breathing data identifies asthma onset

12 Short-Time Fourier Transform The STFT of breathing data identifies asthma onset time domain frequency domain

13 Short-Time Fourier Transform The STFT of breathing data identifies asthma onset Spectrogram for Normal Breath VS. Spectrogram for Wheezing

14 Example 2

15 Robust Principal Component Analysis Solar park data can be cleansed with RPCA

16 Robust Principal Component Analysis Solar park data can be cleansed with RPCA Input Data Low Rank Matrix Sparse Matrix

17 Robust Principal Component Analysis Solar park data can be cleansed with RPCA Input Data Low Rank Matrix Sparse Matrix Cleansed data Noise Anomalies

18 7-Nov Dec Feb-15 6-Apr May Jul-15 3-Sep Oct Dec Jan Mar-16 Robust Principal Component Analysis Solar park data can be cleansed with RPCA Input Data Sparse Matrix kwh delivered delta Generated energy higher than forecast Generated energy lower than forecast Cleansed data Noise Anomalies -800

19 Robust Principal Component Analysis With RPCA, the number of variables can be significantly reduced Input Data Number of variables (PCA, solar park data) Low Rank Matrix 77 66% Reduction 83% Reduction Cleansed data Noise Anomalies Original data PCA components to explain 95% variance RPCA components to explain 95% variance

20 Robust Principal Component Analysis With RPCA, the number of variables can be significantly reduced Input Data Number of clusters (variable clustering, telematics data) % of Variance explained Low Rank Matrix 1,0 0,8 0,6 0,4 0,2 Cleansed data Noise Anomalies 0, Original Low-Rank Number of clusters

21 Example 3

22 Support Vector Data Description SVDD identifies engine degradation

23 Support Vector Data Description SVDD identifies engine degradation

24 Support Vector Data Description SVDD identifies engine degradation Examples of degradation Applications. Automatic flag at certain threshold / change in the moving average Filtering irrelevant data (after scoring) at the edge

25 Analytics at Rest The traditional process for applying analytics Data ETL Data Storage Deploy Alerts / Reports / Decisioning Streaming Data Access - Store - Analyze

26 Streaming Analytics The application of analytics in the IoT context Data ETL Data Storage Deploy Alerts / Reports / Decisioning Score Train Store Enrich Deploy Streaming Data Streaming Model Execution Supervise Score Train

27 The intelligent handling of IoT data and the creation of significant business value requires new analytical techniques and new ways of applying them.