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INTERACTIVEActive Learning in DistributedIndustrial Data Science SettingsBernhard Haslhofer, AITJana Kemnitz, SIEMENSForum Produktion, 2021-05-11
Industrial Data Science Typical Use Cases and Scenarios
Fault Diagnosis &Remaining Useful Life
Prediction
Quality Prediction &Scrap Reduction Anomaly Detection
© AIT 2021
© AIT 2021
Machine Learning Model
TrainHistorical Data
Machine Learning Model
New Data Labels
Problem 1Missing Ground-Truth
Labels
Apply Predict
© AIT 20214
MachineLearning Model
No CentralData Repository
Problem 2Distributed Environments
© AIT 2021
Project goal
Design and develop workflows and algorithmic methods that enable machine learning in distributed edge computing
environments despite missing or insufficient ground-truth data.
© AIT 20216
Abwicklungsstelle
Österreichische Forschungsförderungsgesellschaft (FFG)10.09.2020 Seite 19/52
Target Achievement and Overall Method
From an overall project perspective, above project objectives and goals can be achieved by splitting the project into six work packages: three scientific-technical (WP 2, 3, 4), one for PoCs, Validation, and Generalization (WP 5), one management (WP 1), and one for communication and dissemination of results (WP 6). All scientific work packages yield independent research results that will be integrated for the two aforementioned PoCs. Collaboration between project members will be ensured by the work packages’ task structure, which ensures that difficulties in one work package threatens the successful completion of the entire project.
Figure 2. INTERACTIVE federated, interactive learning architecture.
Figure 2 shows the technical and scientific building blocks of the envisioned INTERACTIVE project and how they assemble into a federated, interactive learning architecture that can be deployed in a decentralized setting: (1) edge devices record various types of raw data (process, sensor, conditions, etc.) from deployed industrial assets; (2) an interactive learning algorithm selects the most relevant data points to be labelled and presents a visual representation to a human annotator (e.g., the operator of an industrial asset); annotated data points are returned to the edge device (3), where a supervised machine learning is incrementally trained and updated for the local industrial asset (4). Trained models from industrial assets of the same type are aggregated by a centralized (e.g., cloud) federated learning system, which coordinates the aggregation of received model updates and synchronizes current global model states back to connected edge devices (5). In that way, machine learning models are trained across multiple decentralized edge devices without exchanging local raw data samples. Each PoC will implement this federated, interactive learning architecture and expose a prototypical use interface for demonstration, evaluation and validation purposes (6).
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Product Quality Prediction Predictive Maintenance
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ApproachBig Picture & PoCs
Industrial Edge
Controller
Cloud
Field Devices Indu
stria
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Secu
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Computingpower
Safe
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Deterministic
AI
AI
AI
Artificial Intelligence in IndustryPlatforms & Deployment
© Siemens 2021
© Siemens 2021
Energy Efficiency Labelling
Industrial Edge
CloudAI
§ KPIs§ Pattern Recognition§ Predictive Maintenance§ Anomaly Detection§ Data Processing
Data Analysis
Visualization
Energy Monitoring without intervening in the control
Condition Monitoring
AI
© Siemens 2021
Artificial Intelligence in IndustryVision for Industrial AI Approach
Specific predictive maintenance service:§ The energy saving for one large refrigerated
warehouses are 7%
§ With an annual electricity consumption of 2.5 million kWh per refrigerated warehouse, this would be an annual saving of 175,000 kWh
§ The saved CO2 emissions over 10 years and 20 plants would be 21,000 tons of CO2
(with a CO2 equivalent / mWh of 0.6 tons) [1]
Establishing a data driven quality assurance process:
§ Downtime reductions between 25-50%
§ Costs savings to production downtime: EUR 140.000 € per manufacturing machine per year
§ Quality increase charges
§ Waste reduction
[1] https://www.energyagency.at/fileadmin/dam/pdf/projekte/klimapolitik/3_SEAP-Vorlage-Technischer-Anhang.pdf
© Siemens 2021
[2] https://www.accenture.com/_acnmedia/pdf-99/accenture-mission-mit-vision.pdf
[3] STATISTIK AUSTRIA resp. EUROSTAT
Artificial Intelligence in IndustryVision for Industrial AI Approach
Exploitation strategy
§ Cold start of AI projects with insufficient or missing round truth
§ Scalable and - in modifications – reusable solution across industrial assets
§ AI applications on the edge
Market Potential:§ The additional gross value generated by AI in
Austria is estimated to 122 billion euros in 2035 and one third is attributed to the manufacturing sector [2]
Potential Costumers:
§ Austria: 1,600 companies in Austria with more than 50 employees [3]
§ Germany: 19,000 companies with more than 50 employees [3]
Thank you!
Dr. Bernhard Haslhofer
Senior Scientist | AITThematic Coordinator Data [email protected]
Dr. Jana Kemnitz
Senior Data Scientist | [email protected]