Webinar:

Innovative and Accurate Solutions for Predictive Maintenance

Sponsored by: Enging

Focused on:

  • Electric Machines
  • Energy

Date: 5 November

Days to go: 16

Time: 3PM London/10AM New York

Non-Invasive solutions based on electrical variables for electric machines

Traditional maintenance approaches in today’s industry and energy sectors are inefficient and costly. Inducing to a large number of false alarms, the traditional maintenance approaches do not prevent or help to avoid unplanned stoppages, thus leading to high downtime costs, high maintenance costs, representing a high risk of exposure (as they need to place sensors inside the electric machines). Low efficiency is therefore unfortunately achieved, as maintenance actions and decisions rely on non-actionable or on imprecise information.

Enging’s solutions are the most advanced online real-time asset monitoring solutions, being the first non-invasive pioneering technology in the market, permitting an extremely precocious and accurate online fault detection.

Exclusively acquiring the electrical variables (currents and voltages) and using a deterministic mathematical algorithm, Enging’s solutions permit to analyse the degradation evolution and detect very incipient faults in the operating conditions of the electric machines (power transformers, distribution transformers, motors, generators, pumps).

Without the need for any historical data, the system can provide actionable insights immediately, enabling clients to schedule maintenance rather than having failures interrupting critical production processes.

Simple, Accurate and Accessible, Enging’s solutions permits to increase the company’s productivity and efficiency, while reducing its overall maintenance costs by avoiding unplanned stoppages and reducing stoppages time.


Presented by

Jorge Estima,

R&D Director

Since 2007, Jorge has been actively involved in R&D activities related to electric drives, specifically focused on the subject of condition monitoring and fault diagnosis. In an early stage, he started working with the team responsible for the development of the Park’s Vector Approach applied to electric machines condition monitoring, collaborating in several industry tests and acquiring this way precious knowledge and experience on this research field. During his PhD, Jorge natural R&D capabilities and creativeness allowed him to develop new innovative solutions related to the condition monitoring of electric drives, having highly cited papers on this subject. He has been involved in 5 research projects, and published 2 book chapters, 14 international journal papers and 31 international conference papers in the research field of this proposal. Jorge has acquired excellent collaborative skills by working together with several national and international researchers from more than 6 foreign countries. Currently he is Enging’s R&D Manager and responsible for the development of MCM algorithm. He is also a teacher at the University of Beira Interior and member of CISE-Electromechatronic Systems Research Centre.

Gualter Sampaio,

Business Development Manager

Gualter Sampaio is the Business Development Manager of Enging-Make Solutions, responsible for the marketing and commercial departments. Before starting to be part of Enging, Gualter spent several years as a National Sales Manager and Commercial Director. Since 1997, Gualter had several great professional experiences in the industry sector and that is why Enging’s young and dynamic environment perfectly matches Gualter’s experience and skills. For Gualter, Enging’s approach represents a disruptive and innovative technology that most definitely will change the way we think and understand predictive maintenance.

Key Learning Objectives

  • Enging’s Online and Real Time Monitoring solutions
  • Transition for a more intelligent and condition-based maintenance;
  • Innovative and non-invasive approach exclusively acquiring electrical variables
  • Simple and scalable solution that that effectively increases company’s productivity and maintenance efficiency

Audience

  • Head of Asset Management
  • Operations & Maintenance Director
  • Operations & Maintenance Manager
  • Chief of Innovation
  • Head of New Technologies and R&D
  • Head of Digital Solutions
  • Asset Manager
  • Electrical Engineer
  • Mechanical Engineer
  • Director of Engineering
  • General Manager
  • Chief Executive Officer
  • Head of IIOT