Knowledge Discovery in ProductionAndré Karpištšenko

Knowledge Discovery Requires Automation

Growth of information and devices per knowledge worker

1. Digital universe x3.8 in size in 2020. Focus on the highest-value subset.*

2. 26.3B devices in 2020, up +61% from 2015 with x2.7 IP traffic increase.**

3. 700M knowledge workers***, automation worth $5.2T to $6.7T****

* IDC, Apr 2014 ** Cisco, Jun 2016 *** Teleport.org, Jun 2016 **** McKinsey, Jun 2016

Core Dataflow

Model Engine

Preprocessing Dataflow

System Composition: Networked Intelligence

Mature

Nascent

Emerging

networked.ai

Infrastructure, Data & IoT Platforms, Advanced Analytics Platforms

Input Data

Info Merger

Data Curator Preparer & Explorer

Base Library

SelectorExecutor

Self-improvementInterpreter

Output Interfaces Core Human Interfaces

Knowledge Manager

Knowledge Manager

Predictive Modeling Flow Example

DashOpt

FeatureEngineering

RawData

RawFeatures

Labels

FeatureIntegration

Featureswith Labels

DataPartitioning

Training Data

Validation Data

Testing Data

Model Training

Evaluate formodel selection

Compute offlineevaluation metrics

Best model

Offline scoringand indexing

Online/offline systems

Online A/B test

Labelpreparation Log data

Scoring features

Raw features

FeatureintegrationModel

Performance

Test Results

Applications in ProductionElectronics Manufacturing Biotechnology

Process time reduction

Predictive maintenance Quality improvement

Yield increase

Product Preview

Preprocessing data for manufacturing analytics is complex and time consuming.

Custom built preprocessing solutions are used to gather data in electronics manufacturing.

The problem

How do people solve it today

Product Scope

Data-driven electronics manufacturing enabling understanding and prediction

• Heavy machinery

• Automotive

• Consumer Devices & Networks

• Drives

• PLC

Product for Pilot Factories

Product Solution

• Hybrid SaaS factory subscriptions and applications via open marketplace

• Real-time data streams from the field and factories for R&D and production

Electronics Factories

End Products

IoT Platforms Cloud Services

Delivering Business Value

Enabled metrics dataIncreased engagement 2x

Enhanced usability of MESIncreased productivity

Test time reduction270k-290kEUR/plant

Reducing risk through higher quality data and improving business with data preprocessing

Industrial Analytics Example: Bosch Competition, I

4 product lines 52 stations Every feature has timestamp Data rows Parts of mechanical components

# (training data) – 1 183 747 # (test data) – 1 183 748

Data columns Anonymized features of stations

Numeric – 970 Categorical – 2 141

Bosch has to ensure that the recipes for the production of its advanced mechanical components are of the highest quality and safety standards. Part of doing so is closely monitoring its parts as they progress through the manufacturing processes.

https://www.kaggle.com/

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Utilization of stations

Industrial Analytics Example: Bosch Competition, II

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https://sites.google.com/site/iotminingtutorial/

IoT Data Streams Mining

• Continuous data, dynamic models, distributed, few seconds

Streams Mining: Actors Model

Data processing pipeline Distributed processing

Kappa Architecture https://www.oreilly.com/ideas/the-world-beyond-batch-streaming-102

DashOpt: Data Science Intelligence

Real-Time Predictive Flow

ML & Simulation Platforms

IoT Platforms

Preprocessed Data

IoT Data

Earth Data

Manufacturing

Data

Predictive Models

Decision Tree SVM

Neural Network Random Forest

Data Science

Intelligence

Outlier Detection

• Single point anomaly detection: likelihood over distribution

• Finding anomalous groups: divergence estimation

• Methods: percentage change, T-test, Chi-square test, Generalized ESD (Extreme Studentized Deviate) test, Seasonal Hybrid ESD, etc.

• Goal: move from detection to automated response

Outlier Detection in Practice

• Too many detections of too little value

• Use methods for thresholds

• Breakout detection and Concept Drift

• For changing distributions move baselines over time

• Risk of overfitting to known anomalies, not finding unknown anomalies

Bayesian aka Active Optimization

• Examples: Design of Experiments, hyper-parameters of supervised learning, algorithms tested with simulations

f is an unknown expensive black-box function with the goal to approximately optimize f with as few experiments as possible

• No free lunch theorem

• Other bio-inspired algorithms for optimization exploitation and exploration: neural networks, genetic algorithms, swarm intelligence, ant colony optimisation, etc.

Bayesian Optimization in Practice

• SigOpt experience: 20 dimensions, above human capacity.

• Uber ATC experience: scaling active optimization to high dimensions default works reliably for 5-7 dim.

• Variables are added during optimization.

• Choose fidelity using heuristics.

DashOpt: Data Science Intelligence

US Patent pending

Extensive data bases of DNA sequences, metabolism of cells and components – enzymes etc., high-throughput experimental omics-methods

Software environment for in silico ab initio design of cells, and in silico testing (predictive modeling) of the cell designs in manufacturing processes

Current State in Biotech

Already available Future state

Thinking about Value from Data Science