1) The document discusses three cases using AI and data visualization in smart manufacturing. The first case involves real-time monitoring and early warning systems using anomaly detection and forecasting. The second case uses connected car and spare parts history data to predict future operations and costs. The third case applies algorithms like clustering and decomposition to disaggregate building energy consumption data from appliances. 2) Grazia Cazzin leads the Knowage Labs and Data & Analytics competency center at Engineering Group, with experience in enterprise applications, data warehousing, BI, and big data analytics for industries, finance, and public sectors. 3) Usability and reducing complexity are emphasized for effective data visualization and AI applications.

1. Open source experiences in #AI and #DataViz
for manufacturing market
DATA & ANALYTICS CoE

2. About me
Introduction
Grazia Cazzin is the offering manager for Big Data &
Analytics competency center in the Research and
Innovation Division of Engineering Group S.p.A.. She is
the Knowage Labs Director (www.knowage-suite.com),
the founder of the SpagoBI project
(http://www.spagobi.org) and its historical project leader.
Working in the IT field since 1992, she has been involved
in enterprise application development, data modelling,
data warehousing, dimensional analysis and business
intelligence; she has gained valuable expertise working in
several market sectors (industry, finance, public
administration) and covering several thematic areas (ERP,
MRP, MPS, Enterprise Portals, CRM, DWH and BI, Big
Data, Analytics). She's currently involved in all the
activities concerning Big Data Analysis, as for SmartCity,
Smart Agriculture and Industry 4.0 scenarios.

3. Engineering Group
Introduction

4. Data & Analytics compentecy center
Introduction

5. #case 1: Early warning & RULE estimation
AI & Dataviz in smart manifacturing
• Real Time
• Current data
only
Monitoring Early Warning Predictive Maintenance
• Near real-time
• Current data and
recent history
• Deferred system
• Recent data and
long history
• Real Time
• Monitoring
• Alarm
• Early
Warning
On plant Data Lake Support
• Data Storage
• Prediction
• Anomaly
Detection
• Impact analysis
• Evaluation
Dashboards
• Scenario
Simulations
• Strategic decision
• Maintenance
Schedule
Registry Feedbacks
Additional information
Time Line object X
Features
Jobs
Alarms
State
Sensors
Time-based data
Logical architecture Analytical layer

6. #case 1: Early warning & RULE estimation
AI & Dataviz in smart manifacturing
Data preparation
Text mining
Feature mapping
featuressensors text messages
Real-Time monitoring
Deployed in plant
Early Warning
Aggregation
Forecasting
Anomaly detection
Un-supervised learning
Predicted states
Fault likelihood
Deviation index
Anomaly detection
Un-supervised learningAnalytical flow

7. #case 1: Early warning & RULE estimation
AI & Dataviz in smart manifacturing

8. #case 1: Early warning & RULE estimation
AI & Dataviz in smart manifacturing
Make it easy … to use !

9. #case 2: connected car & spare parts mng
AI & Dataviz in smart manifacturing
Looking at
general attributes
of every
operations
And using the history of
everyone
Predict next
operation, involved
spare parts and
related costs
And the next ones, setting
the future history of
operations that will be out of
the official network
HISTORY FUTURE
Providing useful information for
retention campaigns
• Drive-style from connected car
• Spare parts semantic map
• Fault history (operation and SP)
• KB analysis (text analysis)
• Prediction
• Impact on sales (dashboards)

10. #case 2: connected car & after sales
AI & Dataviz in smart manifacturing
Make it easy … to use !

11. #case 3: building energy consumption
AI & Dataviz in smart manifacturing
light line robot computer HVAC alarm
HVAC light lamp alwaysON
Appliance Signature identification
De-noising
Filtering
State detection
Clustering of devices
Probability distribution function estimation
Disaggregation Algorithms
From signatures to appliances combination

12. #case 3: building energy consumption
DATA & ANALYTICS CoE
Feature Extraction
Consumption Distribution Updating device statics Output
Device Identification
Event Detection
Data Preprocessing
Data Transformation
Data Integration
Data Reduction
Data Cleaning
Wavelet filter - Data Normalization - Missing data reconstruction
Data Acquisition
Fourier transform
Noise reduction
Apache Spark
Cassandra
Hive
MySQL
Input
Peaks and valley detection
spike removal
Steady state model
Signal decomposition
Temporal behavior detection
Laplace transform
Wavelet transform
Clustering
Event Pairing
ON OFF
Self Organizing Map
DBSCAN / HDBSCAN
KMEANS
KNN
Signal Similarity Distance
Meanshift
Fuzzy C-means
K-medoids
Dynamic Programming
Kernel Density Estimation
Fuzzy rule learner
Random Forest
Gaussian Mixture Model
Generative Adversarial Network
Masked autoencoder for distribution estimation
Smart Device ranking
Fuzzy Reasoning
Adaptive
probabilistic
graphical model
User experience

13. #case 3: building energy consumption
DATA & ANALYTICS CoE
Make it easy … to use !

14. Last mile: DataViz as AI enabler
Conclusion
#Usability#Complexity

15. Conclusion
@gcazzin
Grazia Cazzin
www.knowage-suite.com
www.eng.it
THANK YOU !
Meet us at Booth A3