The CLARION experiment

offers real-time cloud data analytics based on sensor data collected on-site and transferred on the cloud using CPS and IoT technologies, integrates several modules (energy disaggregation, DSS, device health monitoring) and delivers an overall monitoring platform and Decision Support tools for Electric Motor‐Driven Systems (EMDS) that assist the plant operators towards reduced energy usage, increased equipment availability and improved process efficiency. Electric Motor‐Driven Systems EMDS are used in pumps, fans, compressors, and material handling and processing, accounting for around 45% of all global electricity consumption. One way to improve energy efficiency is the replacement of existing motors with eco-efficient ones. Equipment upgrade is usually an important investment, but is not always feasible, especially for SMEs. The ultimate goal of the experiment is to combine and cloudify different technologies to provide a robust and efficient system. It is expected to deliver a "cloudified" solution to monitor the industrial shop floor and offer to the end-user targeted suggestions on how to improve the energy efficiency and the maintenance plan of the facility to avoid failures and downtime of the production process.

Data Layer

Gathers energy consumption data, in high frequency, and from various legacy systems that are already deployed on a site.

Back End

The backend a) collects all the available data from the different sources and stores them, b) is responsible for the data analysis using AI components for the Energy Disaggregation, Device Health monitoring and the Decision Support expert system.

Front End

CLARION offers a user-friendly interface with modular dashboards, smart alerts and visualization components to present the results of the analysis and allow easier understanding and interaction of the available data.

Experiment

The main purpose of the clarion experiment is a) to test the effectiveness of the system towards the reduction in total energy consumption and b) to test the effectiveness of the system towards efficient maintenance planning. The experiment also includes the evaluation of the algorithms and techniques used, and the fine-tuning of the algorithms. The experiment aims to enhance, integrate and cloudify existing energy disaggregation, device health monitoring and DSS tools to provide a market-oriented solution for remote device monitoring and fault detection, and energy efficiency. High frequency energy meters will be deployed to assess the minimum sampling frequency that will allow the disaggregation and device health modules to provide solid outputs. Each partner in the project will have a target role and develop specific components in order to successfully develop and integrate the CLARION system. The actual experiment will take place at the premises of the ELSAP pomace treatment factory in the region of Nafplion in Greece.

IoT dataset

A high-frequency industrial dataset for electric motors, to be used for the benchmarking of industrial energy disaggregation algorithms – Though there are available datasets with domestic and commercial data, there is lack of industrial data.

Algorithms and different techniques evaluation

Validation of the device health monitoring techniques towards predictive maintenance of EMDS using high-frequency electricity load sampling - Evaluation of algorithms and methods that have been used against the proposed ML/DL approach.

High-frequency meter

A high-frequency sampling device (meter) for electric motor systems, tested at industrial scale and industrial conditions - To be used for advanced analytics within the current framework or as input to other tools.

ML/AI Algorithm Framework

An analysis framework using ML/AI algorithms to deliver advanced diagnostics for EMDS - To improve the motor operational efficiency and support predictive maintenance features.

Clarion Project

is a co-funded Application Experiment under the CloudiFacturing Open Call 2 (CloudiFacturing-2)

More Info

The CLARION experiment

Technology

Data Layer

Back End

Front End

Experiment

Partners

ELSAP OLIVE OIL & PRODUCTS INDUSTRIAL

Plegma Labs S.A.

RiSA GmbH

National Centre for Scientific Research ”DEMOKRITOS”

Outcomes

IoT dataset

Algorithms and different techniques evaluation

High-frequency meter

ML/AI Algorithm Framework

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Clarion Project