ai for manufacturing

1 © NEC Corporation 20201 © NEC Corporation 2020

AI FOR MANUFACTURING In Practice

1


AGENDA

02 SMART FACTORY by NEC Concept

03 Solution & System Architecture

01 AI For Manufacturing in Practice

04 AI CASE STUDY

4 © NEC Corporation 2020

4

# AI For Manufacturing in Practice


1

2

3

4

Edge Computing Device & System

HMI and SCADA

MES: Dashboard and Visualization

Digital Factory and AI Concept - Codeless programming- Support various machine brands- Scalable and flexible

- Advanced HMI applications- Enables users to create intuitive,

secure, and highly maintainable HMI

- Industrial Information Management- Synchronise production and industrial operations with business objectives.- Obtain the speed and flexibility required for sustained competitivenessMachine

MES

Data Analytics

HMI + SCADA


Data Analytics

- Predictive Maintenance- Energy Optimization- Advanced Process Control

ERP



Digital Factory and AI Concept


AI FOR MANUFACTURING: HISTROLICAL DATA



AI for Factory Concept

Data Driven are used when it is not possible to draw a complete picture of a system’s physical properties and behaviors. They usually employ machine Learning techniques to model anddetect changes in machine behavior. from available sensor, product quality, and production process data.• Machine Learning

Model Driven rely on domain expertise, knowledge, AI platform about the physical model of a system in order to predict its degrading behavior.• *Vision Inspection• AI solution for special industry or specific machines

• Energy Optimization• *APM (Predictive Maintenance) for Power Plant or Oil&Gas.


ERP / MES(Manufacturing

Execution System)

MACHINE / TOOLING

(Machine Parameters)

IOT Platform(Sensors Data)

EAM QA & QC

PREDICTION

ANALYSIS

Advanced Process Control

In Process QA

Machine Behavior Analysis

External Data

PRESCRIPTIVE

Internal Data

AI FOR MANUFACTURING

Predictive Maintenance

Energy Optimization


10

#Model Driven:

VISION INSPECTION


VVISION INSPECTION


12

#Model Driven:



Asset Monitoring Approach

Prevent Equipment Failures

• Predictive monitoring of industrial equipment assets

• Early warning detection and diagnosis of equipment problems

Benefits

• Improved asset reliability and performance

• Reduced maintenance costs

• Optimized maintenance planning

• Purpose built OOB predictive monitoring of industrial assets achieves

fast time to value

• Collaboration and Knowledge Transfer

• Compressors

• Expanders

• Gas Turbines

• Steam Turbines

• Generators

• Pumps

• Motors

• Air Heaters

• Gearboxes

• Heat Exchangers

• Quench Towers

• Fired Heaters

• Valves

• Furnaces

• Transformers

• Fans

Assets focused


Asset Strategy – Multi Dimensional

It’s a Journey

Age-related failure Random failure

ARC STUDIES SHOW ONLY 18% OF ASSET FAILURE IS AGE-R ELATED. BASED ON THESE DATA, PREVENTIVE MAINTENANCE PROVIDES A BENEFIT FOR JUST 18 PERCENT OF ASSETS, AND MONITORING FOR PREDICTIVE MAINTENANCE IS A RECOMMENDED OPTION FOR THE REST. WWW.ARCWEB.COM/LISTS/POSTS/POST.ASPX?ID=260

Reactive and Preventive Programs

Predictive Technologyfor Early Warnings

Failure Patterns

Requires a comprehensive maintenance infrastructure

APR and diagnostics to predict impending failure

Rules-based logic using sensor data

Planned based on time or usage statistics

Run to failure

Strategic,Proactive,Optimized

Risk-Based

Maintenance


Condition-Based Maintenance

Preventive Maintenance

Reactive Maintenance

18%

82%


Monitoring Approach

Traditional Monitoring

• Constant alert/alarm limits are typical

• Damage accumulates prior to

reaching limit

• Actual minus estimated (residual) signal

detects anomaly as-soon-as-possible

Predictive Asset Monitoring


Monitoring Without Predictive AnalyticsPlo t -0

7 8 1 0 8

7 6

1 0 2 1 1 3 102 6 4 1 4 1 7 0 707

5

7 4

7 2

7 0

6 8

6 6

6 4

6 2

6 0

5 8 9 6 86 10 79

4

4 6 0 1 1 0 522

54 / 6 / 2 0 0 8 4 : 3 9 : 2 1 . 7 9 1 P M 3 . 0 0 d a y s 4 / 9 / 2 0 0 8 4 : 3 9 : 2 1 . 7 9 1 P M


Model Templates

Make new templates or

projects based on templates


Web Alarm Management and Diagnosis


Fault Diagnostics and Report


Transient Analysis


For Internal Use Only

Duke Energy

• Avoid catastrophic failures at power plants. Duke Energy had a transformer failure that cascaded into other

transformers and two turbines, causing over $10 million in damages

• Data analysts at Duke Energy were typically spending 80% of their time collecting the data

and only 20% of their time analyzing it

• Inconsistent diagnosis and limited risk assessment

• Solution

• To fill the time gap between inspections, engineering determined that online continuous

monitoring was needed

• PRiSM Predictive Asset Analytics was deployed as part of Duke Energy’s SmartGen program

• Results

• Empowering people with early warning notification of equipment problems

• Optimizing assets with low-cost sensors and connectivity for high-fidelity data access

enabling predictive maintenance

• Improving operations with contextualized insights

Leverages IIoT and Predictive Analytics to Reduce Failures

• Challenges

Early warning identification and

diagnosis of equipment problems with

predictive asset analytics results in over

65M+ in savings.

“

Savings of

$34.5 millionsingle early warning catch

Serving

7.2 Millioncustomers

Generation capacity

58,000 MW


Duke Energy - Steam Turbine Efficiency

Loss

• Received alarm on low extraction steam temp

• $$$$ additional fuel burned over 8 days

• Could have gone a month or more before plant found it

Early warning identification and

diagnosis of equipment problems with

predictive asset analytics results in over

65M+ in savings.

“

Savings of

$34.5 millionsingle early warning catch

Serving

7.2 Millioncustomers

Generation capacity

58,000 MW


Duke Energy – LP Rotor - L-0 Blade Problem• Unit was started after an outage and there was a vibration step

change on one of the LP turbines (Vibration levels were well below the alarm level)

• Engineering and the plant were notified

• Vibration data was collected and unit was retired for an inspection

• Bolts on lower half of flow sleeve had broke and flow sleeve contacted L-0 blades

• Upper half of flow sleeve was no longer supported by lower half

• Although we had minor damage to the LP blades, we avoided damaging multiple stages of blades, packing, and diaphragms if we had a severe blade liberation.

• Estimated avoided cost - $4.1M


Southern Company

• Solution• With Avantis PRiSM and early notifications, the energy generation provider identified a

BFP coupling shim pack on the verge of failure. Southern Company estimates this single

early detection saved $260,000 alone.

• ResultsWe’re Closing the Loop to help Southern Company:

• Reduce unexpected maintenance

• Maintain data quality reliability

• Manage web alarms effectivity

Selects AVEVA for Predictive Analytics

• Challenges

• With approx. 4.4 million customers across the SE United States, 46,000 MW, 27,000 miles

of transmission lines and 3,700 substations, Southern Company places a premium on

uninterrupted service.

We’re helping Southern Company close

the loop across the entire asset lifecycle“

$4,500,000in performance efficiencies

2,200 Modelsacross gas and biomass power stations

“We potentially

Averted Disaster”- Southern Company Plant Mgr.


Southern Company – Boiler Feedwater PumpEarly warning catch BFP coupling shim pack

• Observation

• Model is indicating an increase in

vibration on multiple bearings

• Results

• A BFP coupling shim pack that was

on the verge of failure

• Estimated avoided cost - $260,000


26

3 Analytic Framework


https://becominghuman.ai/a-simple-way-to-explain-how-to-build-an-ai-system-61f0e7367606

1

2 4

3 5

Get DataGet DataGet Data

Train Model Improve

Clean , Prepare & Manipulate Data

Test Data

ข �นตอนปญญาประดษฐ (Artificial Intelligence : AI)Artificial Intelligence


from DATA to DECISION จากขอมลสการตดสนใจท�แมนยา

Ref: https://www.mit.com


Analytics Workflow

Ref: https://www.matlabexpo.com

Analytics Frameworkแนวทางการวเคราะหขอมล


● Data Types: Structure or Non-Structure Data

● Transaction Data, Batch Data or Streaming Data

● Gather information from historian database

● Sensors information e.g.

○ Pressure

○ Temp

○ Speed

○ Time

○ pH

Data Acquisition



● Gather information from historian database

● Sensors information e.g.

○ Pressure

○ Temp

○ Speed

○ Time

○ pH

Data Acquisition




1

2 4

3 5


Train Model Improve


Test Data



● Data cleansing and standardize data

● Find correlation and relevant from existing

information

● Visualize data using standard techniques

● Give some insightful information for further

machine learning analysis

Data Engineering



● Outlier and missing-value removal, offset removal, and detrending.

● Noise reduction, such as filtering or smoothing.

● Transformations between time and frequency domain.

● More advanced signal processing such as short-time Fourier transforms and

transformations to the order domain.

Preprocess data

Analytics Workflow



1

2 4

3 5


Train Model Improve


Test Data



● Using machine learning or deep learning to

analyze and find the pattern

● Possible algorithm

○ Classification Techniques

○ Regression Techniques

○ Neural Networks using LSTM

Data Analytics



Explore data with command line or in the application to identify features that

can indicate system state and/or predict future states

● Mean value of the data over time

● Frequency of the peak magnitude in a signal spectrum, or a statistical

moment describing changes in the spectrum over time

Identify condition indicators

Analytics Workflow


Train decision models for condition monitoring and fault

detection

● Use statistical Model

○ Find correlation between parameter with Pearson

correlation

○ Find time to event with Survival analysis /remaining

useful life (RUL) and compare accuracy

Train Model

Analytics Workflow

Pearson correlation Coefficient

● Finding any strong the relationships between the two parameters in machine

Ref: https://www.wallstreetmojo.com/pearson-correlation-coefficient/

Pearson CorrelationCoefficient

LogisticRegression Model

When● Xi is a value from each sensor● P is interested event (broken machine)

Survival Analysis

Remaining useful life (RUL)


1

2 4

3 5


Train Model Improve


Test Data


● Integrate analytics model to other

applications

● Possible application and usage

○ MES

○ Dynamic Threshold Adaptation

○ Dashboard

Data Integration


ai for manufacturing

Documents