A method of monitoring the performance of a multi-phase electric motor (13), wherein the electric motor comprises a plurality of stator windings (7, 8, 9) connected in a wye configuration to form a wye point. The method comprises measuring an electrical characteristic of the wye point in a time domain; based upon the measured electrical characteristic of the wye point in the time domain, determining an electrical characteristic of the wye point in the frequency domain; and deriving data indicative of at least one parameter of the performance of the electric motor based upon the determined electrical characteristic of the wye-point in the frequency domain.

An inductive angle sensor is provided with a stator with an excitation oscillating circuit and a pickup coil arrangement and also with a rotor which is arranged rotatably with respect to the stator and comprises an inductive target arrangement. The excitation oscillating circuit can be energizable with an alternating current, in order to induce an induction current in the target arrangement, and the target arrangement can be designed to generate a magnetic field in reaction to the induction current, which magnetic field in turn generates induction signals in the pickup coil arrangement. The angle sensor further comprises a circuit that is designed to derive an induction strength signal representing the signal strength of the induction signals from the induction signals and to ascertain the spatial clearance between the rotor and the stator on the basis of the induction strength signal, and to generate a corresponding clearance signal.

Complicated system fault diagnosis method and system based on a multi-stage model are provided. The method includes: establishing an integer-order mathematical model, a 0.1-level fractional order mathematical model, and a 0.01-level fractional order mathematical model of a permanent magnet synchronous motor system; designing an integer-order status observer based on the integer-order mathematical model, designing a 0.1-level fractional order status observer based on the 0.1-level fractional order mathematical model, and designing a 0.01-level fractional order status observer based on the 0.01-level fractional mathematical model; corresponding residual values can be obtained by the observers and compared with corresponding threshold values to judge whether there is a fault. The system includes first through third modules. Observers with different accuracy degrees are set up and the permanent magnet synchronous motor system is diagnosed through the observers. The fault diagnosis method and system are mainly used in motor diagnosis.

A test and measurement instrument includes one or more sensors configured to measure a mechanical position of a synchronous machine driven by analog three-phase signals, a converter to determine an instantaneous electrical angle from the measured mechanical position, a transform configured to generate DQ0 signals based on the instantaneous electrical angle, and a vector generator structured to produce a resultant vector from the DQ0 signals. Methods are also described.

Provided is an inspection device for a rotating electric machine, the inspection device including a photographing device, a drive mechanism, a display, and a controller. The photographing device photographs a pattern formed on a surface of a wedge constituting part of an armature. The drive mechanism moves the photographing device with respect to a stator functioning as the armature. The controller detects strain of the wedge by comparing image data of the pattern photographed by the photographing device with reference data of the pattern. In this manner, the inspection device for a rotating electric machine can easily detect the strain of the wedge. Further, the controller estimates loosening of the wedge based on the strain of the wedge, and informs an operator of the rotating electric machine through the display that the loosening of the wedge has occurred.

A testing device for testing a medium voltage starter or breaker and method of use. The testing device is configured to test starter motors in the 2,300 volts to 13,800 volts range while protecting the technician from high voltages. The testing device is electrically connectable to a variety of different medium voltage starter motors via an umbilical connector harness adapted for each specific starter. A tester control board is organized to indicate the functionality of the medium voltage starter electrical components. The control board is used to isolate circuits in open and closed positions and visual indicators are used to verify proper operation of the starter coil, primary contacts, and auxiliary contacts.

A testing device for testing a medium voltage starter or breaker and method of use. The testing device is configured to test starter motors in the 2,300 volts to 13,800 volts range while protecting the technician from high voltages. The testing device is electrically connectable to a variety of different medium voltage starter motors via an umbilical connector harness adapted for each specific starter. A tester control board is organized to indicate the functionality of the medium voltage starter electrical components. The control board is used to isolate circuits in open and closed positions and visual indicators are used to verify proper operation of the starter coil, primary contacts, and auxiliary contacts.

A machine learning apparatus that learns an alarm factor in a motor drive device includes a state observation unit that obtains a feature amount as a state variable from the motor drive device and an alarm factor as label data, the alarm factor corresponding to the feature amount, and a learning unit that generates a learning model for inferring a new alarm factor corresponding to a new feature amount, from a dataset created on a basis of a combination of the state variable and the label data. The feature amount includes at least one of a detected current value detected from the motor, a speed command value specifying a rotational speed of the motor, an output voltage value output to the motor, an estimated speed value of the motor, and a detected speed value of the motor.

An abnormality determination system includes a state quantity obtaining circuit and an abnormality determination circuit. The state quantity obtaining circuit is configured to obtain a state quantity associated with a mechanical system. The abnormality determination circuit is configured to, according to a learning content obtained in a machine learning process and based on the state quantity, determine as to at least one of an occurrence of an abnormality in the mechanical system, an occurrence position of the abnormality, and a cause of the abnormality.

A closed loop control system for controlling a plant comprises a controller includes an input arranged to receive a feedback signal from the plant. The controller is arranged to compare a value of the feedback signal to a set point value x(s) and to produce an error signal ε(s) that is at least partially dependent on a difference between the value of the feedback signal and the set point value. The controller also includes an output arranged to supply the error signal ε(s) to the plant. A monitor is arranged to compare a value of the error signal ε(s) produced by the controller to a threshold value and to produce a warning signal when the value of the error signal ε(s) exceeds the threshold value for a period of time greater than a predetermined period.