Team AquaFu (Aperio) Data Science on Water Data

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Phillip Quentin Parks
5 years ago
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1 Team AquaFu (Aperio) Data Science on Water Data

2 Analysis of water quality problems - a challenge Water Quality sensors, Flow sensors Sensors of different types on different pipes. Unlabeled data.

3 Tools Python - Pandas, scikit-learn, jupyter notebook Python - OSISOFT PI Web API library (written by Marcos Loeff) Elasticsearch + Kibana. Python code written to Export PI to Elasticsearch

4 Correlate the Flow Rate & Temperature vs Dist (km)

5 Correlate the Water Quality (Optical Refraction) Distance - km

9 Thanks!

42 PyVision Advanced Anomaly Detection for Water Quality Monitoring

43 What Replace the monitor lizard Advanced Anomaly Detection at all locations Automatically create alarms & notifications

44 Why Ensuring reliable service Enabling faster maintenance response Reduce likelihood of false alarms by combining multiple sensors

45 How HDBSCAN (hierarchical density-based spatial clustering w/ noise) Looking for spatial density-adaptive outliers in multiple dimensions

46 Production Site Noordbergum - conductivity

47 Production Site Noordbergum ph & turbidity

48 PI AF

49 PI Vision Dashboard

50 Thank you!

51 OSIsoft EMEA 2017 Hackathon event

52 OSIsoft EMEA 2017 Hackathon event Objects on the presentation might look older than it appear

53 Marek Data Plumber Who are we? Jakub Coder Alexa her 5 3 Przemyslaw Coder Gregor The other

54 The challenge Provide our of their future A connected system that combines realtime data, detects anomalies and provide this to the right person to enable the right action. 5 4

Provide our of their future When the data indicates an issue that can cause a problem, it is assigned to the right work force and affected parties are notified if required.

55 Provide our of their future When the data indicates an issue that can cause a problem, it is assigned to the right work force and affected parties are notified if required. Interventions are performed to resolve the issue. Analytics in real time identify anomalies. Machine learning analytics predicts by correlating signals and past experience Connected system enabling learning cycles where gained knowledge can be apply to future issue to detect earlier or resolve faster 5 5 Data is acquired and transmitted to a central location Solutions are recorded. Outage and incidents are prevented and private households dissatisfaction eliminated

56 5 6 What we did

57 Used technologies PI AssetFramework -Analysis -EventFrames Data Analytics - Tableau Desktop - Microsoft Azure Machine Learning Studio - SQL - PI OLEDB Webtechnologies -PI Web API -Realtime data (channels/websockets) Extended Userexperience - Alexa - Alexa Skills Kit - AWS Lambda Function (Node.js) -Batch queries -EventFrames (fetch and acknowledge) 5 7 -Vue.js + Vuestic

58 5 8 Dashboard

59 Data Analytics (Cloud) Using data extracts via PI OLEDB. Visualizing the data with Tableau or experimenting with Microsoft Azure Machine Learning Studio by apply existing algorithms. 6 1

60 6 2 Data Analytics (Cloud-Integration)

61 Data Analytics (Tableau) Using data extracts via PI OLEDB. Visualizing the data with Tableau or experimenting with Microsoft Azure Machine Learning Studio by apply existing algorithms. 6 3

62 6 4 Data Analytics (PI Analytics)

63 Consider all kind of data + By categorizing issues / anomalies not just to operative events but public events too (e.g. football, vacation) is it possible to predict data anomalies by just checking the calendar?

64 How this will help f A connected system provides a better overview and enables a seamless workflow by having systems integrated Using existing powerfull tools to prevent implementation of things that already exists (e.g. Cortana Intelligence Gallery) Enabling data exploration by using Tableau for vizualisation Interaction with the system in new way (Alexa) Saving costs predicting events faster and assigning field forces at the right time

65 Thanks

networks Customer solutions The company is present in Poland and Germany with the team of experienced professional in IT/OT integration and SW development It operates in over 30 countries and serves

66 E.ON Climate & Renewables - E.ON is an international, privately-owned energy supplier focused on 3 areas: Renewables ConnectPoint is a Software vendor and System Integrator specialized in Utilities & Industry sector Energy networks Customer solutions The company is present in Poland and Germany with the team of experienced professional in IT/OT integration and SW development It operates in over 30 countries and serves over 33 million customers. ConnectPoint started co-operation with E.ON Climate & Renewables in 2013 and since that time the company conducted above 20 projects in areas like The Company employs above people world-wide and annually achieves revenues above 100 billions Euro E.ON Climate & Renewables (EC&R), headquartered in Essen, Germany, is responsible for E.ON s industrial-scale renewable energy activities. Custom Software development System Integration in IT/OT area Business Intelligence applications development (e.g. OSIsoft PI) EC&R develops, builds and operates large renewable energy assets in Europe and North America. 6 8

67 Find out more. If you would like to find out more about how ConnectPoint can help transform your utility service with intelligent network capability, get in touch with pl 6 9

The Role of the PI System in IIoT. Dr Dominic John, VP of Marketing and Technical Communication

The Role of the PI System in IIoT Dr Dominic John, VP of Marketing and Technical Communication LATAM Regional Conference 27 Copyright 27 OSIsoft, LLC Our World Is Digitally Transforming.2B.5B 4M 5M M 3M