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Industrial AI Industrial automation based on auto ML and GANs to address predictive maintenance, quality control and optimization Conundrum

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Page 1: quality control and optimization GANs to address ......model v.1 New batch of data New model v.2 Data stream Model v.1 operates Model v.2 test in production Model v.2 operates New

Industrial AI

Industrial automation based on auto ML and GANs to address predictive maintenance, quality control and optimization

Conundrum

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Agenda

1. About Conundrum

2. Solution overview

3. Cases

4. Technology deep dive

5. Conclusion

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About ConundrumConundrum Industrial Limited is an international technology company focusing on research in AI and development of its proprietary machine learning technologies and software products for industries. Backed by Speedinvest, Austrian based VC.

Overview

17 completed projects

3 deployments

Wins in industrial competitions: Schneider, Aramco,

Gazprom Neft, CERN

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About ConundrumConundrum Industrial Limited is an international technology company focusing on research in AI and development of its proprietary machine learning technologies and software products for industries. Backed by Speedinvest, Austrian based VC.

Industries

Metal & Mining

Pipes manufacturing

Chemical

Oil & Gas

Pulp & Paper

Overview

17 completed projects

3 deployments

Wins in industrial competitions: Schneider, Aramco,

Gazprom Neft, CERN

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About ConundrumConundrum Industrial Limited is an international technology company focusing on research in AI and development of its proprietary machine learning technologies and software products for industries. Backed by Speedinvest, Austrian based VC.

Industries

Metal & Mining

Pipes manufacturing

Chemical

Oil & Gas

Pulp & Paper

Overview

17 completed projects

3 deployments

Wins in industrial competitions: Schneider, Aramco,

Gazprom Neft, CERN

Our partners

- our investor

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Improving Industries Performance

Reduce maintenance costs & improve throughput

Reduce manufacturing costs

Eliminate quality escapes

Predictive & prescriptive maintenance

Manufacturing process optimization

Quality control

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Conundrum System Provides the Following Solutions:

• Predictive & Prescriptive maintenance

• Quality control

• Industrial processes optimization

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Data SourcesEquipment sensors: Temperature Pressure Vibration Acoustics Amperage etc.

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Data SourcesEquipment sensors: Temperature Pressure Vibration Acoustics Amperage etc.

Measurements in laboratories:Chemical analysis

Product quality tests

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Data SourcesEquipment sensors: Temperature Pressure Vibration Acoustics Amperage etc.

Complex informationVideo

UltrasoundX-Ray

Measurements in laboratories:Chemical analysis

Product quality tests

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Data SourcesEquipment sensors: Temperature Pressure Vibration Acoustics Amperage etc.

Complex informationVideo

UltrasoundX-Ray

Measurements in laboratories:Chemical analysis

Product quality tests

Tracking of events: Failures Manual commands Control values management Operating modes

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Training in Cloud & Deployment in Cloud

ConundrumTraining Module

Historical Data

Conundrum Inference Module

Production Ready Deployed

Prepare model and assemble solution

Equipment

Automation System

Industrial Protocol

Training Module in CloudAutomated Preparation of Solution

Factory / Asset / Company Data CenterProduction

Data upload

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14

Training in Cloud & Deployment in Cloud

ConundrumTraining Module

Historical Data

Conundrum Inference Module

Production Ready Deployed

Prepare model and assemble solution

Equipment

Automation System

Industrial Protocol

Data Stream

Inferences

Data Stream

Training Module in CloudAutomated Preparation of Solution

Factory / Asset / Company Data CenterProduction

Engineers

Data upload

Conundrum Cloud Adapter

Communicate with Conundrum Cloud

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Training in Cloud & Deployment On-Premises

Equipment

Automation System

Industrial Protocol

Conundrum Training Module

Historical Data

Conundrum Inference Module

Production Ready

Prepare model and assemble solution

Training Module in CloudAutomated Preparation of Solution

Data upload

Factory / Asset / Company Data CenterProduction

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Training in Cloud & Deployment On-Premises

Equipment

Automation System

Industrial Protocol

Data Stream

Conundrum Inference Module

Production Ready Deployed

Integration To Production

Conundrum Training Module

Historical Data

Conundrum Inference Module

Production Ready

Prepare model and assemble solution

Training Module in CloudAutomated Preparation of Solution

Data upload

Factory / Asset / Company Data CenterProduction

Inferences Engineers

Conundrum Platform

Conundrum Supercomputer

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Conundrum Platform On-premise

Equipment

Automation System

Industrial Protocol

Data Stream

Integration To Production

Conundrum Training Module

Historical Data

Conundrum Inference Module

Production Ready

Prepare model and assemble solution

Training Module in CloudAutomated Preparation of Solution

Data upload

Factory / Asset / Company Data CenterProduction

Inferences Engineers

Conundrum Platform

Conundrum Supercomputer

Conundrum Platform

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Training Systems

Automated Machine Learning

Conundrum Cloud automatically prepares a model for specific equipment

Delivers max benefit extracted from industrial data

Transfer Learning

Conundrum Cloud enables to use models experience and transfer knowledge from pretrained models

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Inference Systems

Production Ready

Conundrum Inference Module provides API to simplify integration & deployment

Packed into container enabling platform independence

Permanent Control & Improvement in Production

Auto monitoring keeps the performance under control

Permanent updates keep the system up-to-date

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17 Successful Cases Across Industries

Chemical

Solve

d cas

es

Oil and Gas Metal & Mining

Indus

tries

Pulp and Paper

Paper tears prediction on paper mill

Kappa number (paper quality) prediction on paper production

Predictive maintenance of chemical production equipment

Prediction of produced product quality

Optimization of chemical plant operating modes

Digital twin of chemical production

Predictive maintenance of submersible pumps in oil wells (upstream)

Anomaly detection of pumps (downstream)

Prediction and classification of drilling rigs failures events (upstream)

Quality prediction of coal in coal enrichment process

Optimization of coal enrichment process

Defects detection and classification of metal sheet during zinc coating

Predictive maintenance of welding equipment

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Case: Large-Diameter Pipes ManufacturingChallenge

Manufacturing process downtime due to welding equipment (DSAW) failure.

Tasks- Predict failures of welding equipment- Detect causes of potential failures- Speed up check-ups and maintenance

61% 72%

Detected failures True check-ups Avg. check-up time reduction

25%

Downtime reduction

-32%

Before 9% After 6%

Results

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Overview

Coal mining and processing enterprise with coal enrichment factory. It sustains quality issues of coking coal which leads to losses.

Tasks- Predict coal quality (ash conc.) - Provide real-time recommendations to adjust control parameters

to improve product quality

Case: Coal Enrichment Optimization

Conc. prediction error

±0.3%

True detected quality issues

Lower number of shipments

with quality issue

88% -40%

Results

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Predictive Maintenance of PumpsChallenge

Pumps downtime and extra maintenance costs due to abrupt breakdowns. Pumps quantity is hundreds of units. Avg. breakdown events per year is 3.

Tasks- Predict abnormal behaviour of pumps several days before breakdowns- Detect causes of breakdowns- Speed up check-ups and maintenance

Detected failures True check-ups Avg. maintenance time reduction

-31%

95% 62%

Results

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Challenge

Fertilizer manufacturer has issues of low quality of product which is estimated as a P2O5 concentration.

Tasks- Predict P2O5 conc. in the output of flotation 1 hour in advance- Recommend control parameters adjustment to keep the conc.

into required range

Chemical Production Optimization

Conc. prediction error

±0.5% 88%

True detected quality issues

Quality issues decrease

20%

Results

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Challenge

Failures of paper mill happen which lead to paper tears and downtime of the whole mill.

Tasks

- Predict failure of the mill;- Recognize possible causes of failures.

PdM of Paper Mill

Results

Detected failures True check-ups Avg. maintenance time reduction

-24%

58% 62%

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Training Module

Get Data From Storage

Build/Rebuild Digital Twin

Auto ML,RCGAN,

Attention MethodNvidia GPUs

Nvidia Docker

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Training Module

Get Data From Storage

Build/Rebuild Digital Twin

Auto ML,RCGAN,

Attention Method

Train\Retrainadditional layers

Supervised Learning

Nvidia GPUs

Nvidia Docker

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Training Module

Get Data From Storage

Build/Rebuild Digital Twin

Auto ML,RCGAN,

Attention Method

Train\Retrainadditional layers

Supervised Learning

Optimization,Clustering

Optimize Thresholds

Check Quality

Recognize Causes

Inference Control Policy

Assemble and Deploy

the Model Docker

Nvidia GPUs

Nvidia Docker

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Technology Workflow

Task Definition Uploading Data MetaData Generation

Model ArchitectureGeneration

Data Preprocessing

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Task Definition Uploading Data MetaData Generation

Model ArchitectureGeneration

Train Digital Twin

Data Preprocessing

RCGAN

Technology Workflow

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Conundrum 34

Task Definition Uploading Data MetaData Generation

Model ArchitectureGeneration

Train Digital Twin

Data Preprocessing

Quality Prediction

RCGAN

Train Additional

Model

Semi-Supervised Learning

Technology Workflow

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Conundrum 35

Task Definition Uploading Data MetaData Generation

Model ArchitectureGeneration

Train Digital Twin

Data Preprocessing

Predictive Maintenance

Quality Prediction

Train Additional Model

Anomaly Detection

Supervised Learning

RCGAN

Train Additional

Model

Semi-Supervised Learning

Technology Workflow

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Conundrum 36

Task Definition Uploading Data MetaData Generation

Model ArchitectureGeneration

Train Digital Twin

Data Preprocessing

Train Agent

Predictive Maintenance

Optimization Task

Quality Prediction

Train Additional Model

Anomaly Detection

Supervised Learning

RCGAN

RLTrain

Additional Model

Semi-Supervised Learning

Technology Workflow

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Conundrum 37

Task Definition Uploading Data MetaData Generation

Model ArchitectureGeneration

Train Digital Twin

Data Preprocessing

Train Agent

Explore Interpretations

Predictive Maintenance

Optimization Task

Quality Prediction

Train Additional Model

Anomaly Detection

Supervised Learning

RCGAN

RLTrain

Additional Model

Semi-Supervised Learning

Optimise Thresholds

Software Assemble and

Deploy

Economic Effect Maximization

Attention Techniques Clusterization

Docker + API + UI

Conundrum Technology Workflow

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Conundrum 38

Inference Module

Preprocess Data

normalization, denoising, cleaning,

measurements frequency normalization,

delayed signals handling

Nvidia GPUs

Sensor, Labs and Cameras

data flow

Nvidia Docker

API

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Conundrum 39

Inference Module

Preprocess Data

normalization, denoising, cleaning,

measurements frequency normalization,

delayed signals handling

Inference

Online learning

Nvidia GPUs

Sensor, Labs and Cameras

data flow

Nvidia Docker

API

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Conundrum 40

Inference Module

Preprocess Data

normalization, denoising, cleaning,

measurements frequency normalization,

delayed signals handling

Inference

Online learning

Real-time dashboard

3d Party Systems

Nvidia GPUs

Sensor, Labs and Cameras

data flow

Nvidia Docker

Store data

Docker

Docker

API

API

API

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Permanent Improvement In Production

Fine tuning of model v.1

New batch of data

New model v.2

Data stream

Model v.1 operates

Model v.2 test in production

Model v.2 operates

New version is deployed to production

Steps of improvement are repeated

Conundrum enables to keep the performance of it’s models under control and permanently improve the performance using new data.

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Permanent Improvement In Production

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Transfer Learning ImpactPrecision/Recall curve for

− Traditional supervised (light blue);− Traditional unsupervised (blue);− Conundrum solution (red);− Pretrained Conundrum solution using transfer

learning (green).

Conundrum Transfer Learning technology enables to leverage other datasets from similar cases to boost the performance of the solution.

Here the solution has been pre-trained using the data from 3 other pumps (from energy industry), 1 year.

Method AreaMax F1 Recall = 0.8

Prec Rec F1 Prec Rec F1

Conundrum + TL 0.70 0.74 0.53 0.62 0.46 0.80 0.59

Conundrum 0.49 0.44 0.69 0.54 0.37 0.80 0.50

Trad. superv 0.29 0.23 0.97 0.37 0.21 0.80 0.33

Trad. unsuperv 0.28 0.28 0.49 0.35 0.20 0.80 0.32

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Conclusion

Conundrum is targeting to become a leader provider of AI powered automation solutions for industries helping industrial companies to leapfrog into an Industry 4.0 era and attain the next level of intelligent control.

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Name: Konstantin Kiselev, CEOEmail: [email protected]

mailto:[email protected]

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