Insight into the condition of electric motors prevents failure


Electric motors are the workhorses of the industry. Failure is never convenient. Maintenance is usually planned based on statistic failure data and time-based checks and is sometimes unnecessary. But there are other alternatives. Semiotic Labs monitors the current status of electrical motors using sensors, intelligent algorithms and a cloud-based dashboard. Technolution develops and supplies the required smart sensor module.

Maintenance only when needed

In order to perform maintenance, an electrical motor needs to be opened. This can never be done without the risk of damaging the motor. SAM4, the solution from Semiotic Labs, provides insight into the status of the electrical motor without having to open it. Sensors attached to the power cables measure the current and voltage to which the motor is exposed. Intelligent algorithms analyze the measured data and thus determine the condition of the motor. Is the performance suffering or does the motor require maintenance? A notification will then automatically appear on the dashboard. This allows for maintenance to be scheduled and shutdown of the production lines is kept to a minimum.

Sensors in the control cabinet

Technolution supports Semiotic Labs with the development and production of the smart sensor module that performs high-frequency measurements of the current and voltage in the cables of the motors. In most cases, the smart sensor module is placed in a control cabinet. The installation of the sensor is simple and the production does not have to be shutdown. Moreover, the control cabinet is a clean, dry and secured environment for precision instruments.

According to Simon Jagers, COO at Semiotic Labs, the contribution of Technolution has resulted in a product that has much improved since the original prototype. “Thanks to Technolution's professionalism and their thorough knowledge of the matter, we are able to work with a much clearer signal.”

Cloud and self-learning algorithms

The measured data is sent to the cloud via a switch and an Internet gateway. Here, the self-learning algorithms of Semiotic Labs determine the current status of the electrical motor based on fluctuations in the current and voltage and imperfections in the current sine wave . The status data is shown on the dashboard for the end user or is transferred to the user's existing operating system. The user does not only gain insight in the condition of the motor, but also in data concerning uptime, energy usage, pressure and power.

Improving uptime

In the industry, a pay-per-use model is being used more often. According to this model, the clients do not pay the supplier for ownership, but for the use of the machines. Maximum uptime is thus crucial for the supplier. Therefore, there is a great demand for solutions that can maximize the machines' uptime. Semiotic Labs' solutions meets this demand.


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