In the fourth industrial revolution (Industry 4.0), predictive maintenance is an essential activity for any production process, since it allows to know the real state in which a machine is, to know its evolution over time, and therefore to program the maintenance tasks.
The Smart Motor Sensor device performs a learning process based on artificial intelligence, this learning process allows predicting motor breakdowns.
These failures can cause catastrophic consequences, such as untimely shutdowns of the machine itself with consequent interruptions in production and repair costs. In addition to this, another collateral consequence of the presence of breakdowns and anomalies, which we still do not take into account, is the decrease in the efficiency of the machine itself.
Correct maintenance management guarantees maximum economic profitability for companies, due to the fact that the occurrences of motors breakdowns are reduced and unscheduled production stops are avoided.
In addition to the above, the efficiency of the motors plays a fundamental role in cost savings per operation, since a high efficiency motor consumes less electrical energy than a motor with standard efficiency to supply the same power.
Studies have been carried out where it is determined to what extent the efficiency of the machine is compromised by the presence of the different types of failure. Specifically, the following types of failure: rotor failures, stator winding asymmetries, insulation system failures, imbalance / misalignments, and ventilation system failures.
The study on which we are based has carried out tests with a bench where there is a squirrel cage induction motor, with a power of 1.1kW, coupled with a DC machine, through a torque transducer.
Destructive tests have been carried out on this motor to generate rotor failures. For this type of failure, a test will be carried out with a rotor with nine consecutive broken rotary bars. There is approximately an efficiency reduction of more than 12%.
Regarding the failures of the ventilation system, replicating the main failure in the ventilation system that occurs due to problems of dirt and dust accumulation (obstruction) and the failure of horizontal and vertical misalignment, a decrease in efficiency could be seen little more than 1% compared to that of the healthy motor.
To further understand the evolution of efficiency in failed motors, a destructive test was carried out to overheat the insulation.
The test was carried out with the insulation totally superheated, where the efficiency value was = 68.47% at 99.47 of the load. The reduction in efficiency relative to the healthy motor at full load was approximately 7%.
With this study we can not only confirm that the presence of the faults studied ends up affecting performance to a greater or lesser extent, but also that the effect in terms of decreased efficiency can be very notable. This information emphasizes the importance of carrying out good maintenance on electric motors, not only to avoid untimely stops, but also to guarantee that they operate at the level of efficiency foreseen by the manufacturer.
Therefore, with our Smart Motor Sensor device, you will be able to achieve energy savings, since it is not enough only to use high-efficiency motors, it is necessary to implement maintenance techniques that guarantee their correct operation. What is gained in acquiring a high-efficiency motor can be lost when it works in a fault condition.