Senseye > Case Studies > Predictive maintenance in Schneider Electric

Predictive maintenance in Schneider Electric

Senseye Logo
 Predictive maintenance in Schneider Electric - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Machine Learning
  • Analytics & Modeling - Predictive Analytics
  • Functional Applications - Enterprise Asset Management Systems (EAM)
  • Functional Applications - Remote Monitoring & Control Systems
  • Sensors - Temperature Sensors
  • Sensors - Voltage Sensors
Applicable Industries
  • Electronics
Applicable Functions
  • Maintenance
Use Cases
  • Predictive Maintenance
The Customer
Schneider Electric
About The Customer
With a presence in 100 countries, 137,000 employees and €25,7 billion revenue in 2018, Schneider Electric is leading the digital transformation of energy management and automation. The company does 90% of its business on energy demand, covering 4 mai
The Challenge

Schneider Electric Le Vaudreuil factory in France is recognized by the World Economic Forum as one of the world’s top nine most advanced “lighthouse” sites, applying Fourth Industrial Revolution technologies at large scale. It was experiencing machine-health and unplanned downtime issues on a critical machine within their manufacturing process. They were looking for a solution that could easily leverage existing machine data feeds, be used by machine operators without requiring complex setup or extensive training, and with a fast return on investment.

The Solution

Initially, Schneider Electric ran an analysis on the problematic machine to determine the main cause of the downtime and assess the machine health. After this analysis, they discovered it was partly due to the long timeframe applied for changing old parts. To address this issue Schneider Electric began regularly changing components. However, only 18% of the unplanned downtime was related to the age of components. Based on this, Schneider Electric installed temperature and current sensors to monitor this critical machine’s behavior. The data from these sensors was stored in Aveva Insight, then passed into Senseye PdM to be analyzed against historical data. Using powerful AI and ML algorithms based on mechanical frameworks, Senseye PdM was able to automatically provide maintenance engineers with alerts before the machine would fail. For example, machine operators were notified before a spindle or counter spindle would break giving a 7-point improvement in OEE.

Operational Impact
  • [Data Management - Data Availability]

    Gathered more machine insights and improved critical machine uptime.

  • [Financial Growth - ROI]

    Achieved a fast ROI.

Quantitative Benefit
  • Reduced the maintenance costs on a single machine by 20%.

Case Study missing?

Start adding your own!

Register with your work email and create a new case study profile for your business.

Add New Record

Related Case Studies.

Contact us

Let's talk!
* Required
* Required
* Required
* Invalid email address
By submitting this form, you agree that IoT ONE may contact you with insights and marketing messaging.
No thanks, I don't want to receive any marketing emails from IoT ONE.

Thank you for your message!
We will contact you soon.