Description
SH31402S0001381 Motor parameter settings
SH31402S0001381 Motor parameter settings
Module Clips Drive controller servo moto
SH31402S0001381 Motor is a device that converts electrical energy into mechanical energy.
It uses energized coils (i.e. stator windings) to generate a rotating magnetic field and applies it to the rotor (such as a squirrel cage closed aluminum frame) to form a magnetic electric rotational torque.
SH31402S0001381 Electric motors are divided into DC motors and AC motors according to their power sources. Most electric motors in the power system are AC motors,
which can be synchronous motors or asynchronous motors (the stator magnetic field speed and rotor rotation speed of the motor do not maintain synchronous speed).
The SH31402S0001381 electric motor is mainly composed of a stator and a rotor. The direction of force movement of the energized wires in the magnetic field is related to the direction
of the current and the direction of the magnetic field lines (magnetic field direction). The working principle of an electric motor is that the magnetic field exerts force on the current, causing the motor to rotate.
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(3) A large number of features. Different from the data sets in traditional data mining, the features observed in manufacturing analysis may be thousands. Care must therefore be taken to avoid that machine learning algorithms can only work with reduced datasets (i.e. datasets with a small number of features).
(4) Multicollinearity, when products pass through the assembly line, different measurement methods are taken at different stations in the production process. Some of these measurements can be highly correlated, however many machine learning and data mining algorithm properties are independent of each other, and multicollinearity issues should be carefully studied for the proposed analysis method.
(5) Classification imbalance problem, where there is a huge imbalance between good and bad parts (or scrap, that is, parts that do not pass quality control testing). Ratios may range from 9:1 to even lower than 99,000,000:1. It is difficult to distinguish good parts from scrap using standard classification techniques, so several methods for handling class imbalance have been proposed and applied to manufacturing analysis [8].
(6) Non-stationary data, the underlying manufacturing process may change due to various factors such as changes in suppliers or operators and calibration deviations in machines. There is therefore a need to apply more robust methods to the non-stationary nature of the data. (7) Models can be difficult to interpret, and production and quality control engineers need to understand the analytical solutions that inform process or design changes. Otherwise the generated recommendations and decisions may be ignored.
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