Founded in 2011, Enging is an innovative Portuguese company, specialized in advanced and disruptive industrial asset condition monitoring solutions to monitor the condition of the electric motors and power transformers.
Today, and following the trend of Industry 4.0, the condition monitoring of the various assets in companies has become very important. Enging has developed a completely disruptive monitoring platform, ePrediMntc®, dedicated to the predictive maintenance of electric motors and power transformers. These valuable assets are the work horse of modern industry. With an early fault detection in these machines, it is possible to prevent unintentional stoppages and avoid large costs.
Exclusively using electrical variables and based on the latest IIoT technologies, Enging develops disruptive, non-invasive and real-time monitoring solutions, that allow for an extremely precocious and accurate online fault detection through an user-friendly web platform, ePreditMntc®. With this innovative condition monitoring platform, our customers can effectively manage the performance and anticipate failures in their assets.
Enging by constantly searching for innovation as core of its developments, offers a pioneering technology in the market, highlighted by the advantages when compared to the other existing technologies.
Enging is fully committed to the continuous development of new and disruptive online monitoring techniques with the aim to provide more efficient and effective solutions by embracing innovative technologies.
ePreditMntc® is a user-friendly platform that, in a seamless way allows an online and remote monitoring of electrical machine´s operating conditions. Exclusively using electrical variables and based on mathematical algorithms, ePreditMntc® allows for an extremely precocious and accurate real-time fault detection by using a non-invasive technique. This platform relies in specific deterministic mathematical models of electric motors and power transformers to provide very detailed information of small abnormalities that start to arise in a very early stage.
Enging’s MCM solution is specially developed to monitor electric motors through a completely different deterministic method, while using a non-invasive approach.
Based on a mathematical algorithm and only using electrical variables, Enging´s MCM solution integrates an online asset condition monitoring platform, ePreditMntc®, that allows for remote, online and real-time diagnostic analysis of the electric motor’s operating conditions
Through an early fault detection, the ePreditMntc® platform, allows to optimize and increase the electric motors’ efficiency and reliability.
This solution does not rely on machine learning techniques, and therefore, does not need large data sets, training or any kind of complex configurations. It also does not need specialized technicians and all data can be seen in real-time. It presents a great flexibility to configure and receive alarms related to incipient fault detection, providing, simultaneously, access to an historical trend and statistical analysis.
Enging’s TCM solution is a fully integrated approach to monitor one of the most valuable assets in an electrical grid or in the industry: power transformers. These solutions rely on a completely different deterministic method, based on a mathematical algorithm and using only electric variables as inputs.
This solution, based on the latest IIoT technologies, integrates an online asset condition monitoring platform, ePreditMntc®, that allows for remote and real-time diagnostic analysis of power transformer’s operating conditions.
Since failures in power transformers may strongly influence the electric power system performance, the early fault diagnosis through the ePreditMntc® platform, enables to reduce the unintentional stoppages and increase the efficiency and reliability of power transformers.
The TCM solution does not require specialized technicians and all data can be seen in real-time. It presents a great flexibility to configure and receive alarms related to incipient fault detection, providing, simultaneously, access to an historical trend and statistical analysis.