A test device is configured for testing a specimen which has an inductor. The test device includes a controllable unit for reducing a current intensity of a current flowing in the inductor.

Disclosed is an intelligent on-line diagnosis method for winding deformation of power transformer. When a transformer is subjected to short-circuit shock or transportation collision, transformer windings may undergo local twisting, swelling or the like under the action of an electric power or mechanical force, which is called winding deformation and will cause a huge hidden danger to the safe operation of the power network. Commonly used diagnosis methods for winding deformation are all off-line diagnosis methods, which have the disadvantages that transformers need to be shut down and highly skilled operators are required. The present invention provide an intelligent on-line diagnosis method for winding deformation on the basis of combination of information entropy and support vector machine. By carrying out feature extraction of current and voltage signals based on permutation entropy and wavelet entropy, integrating the variation of the monitoring indicators of the power transformers in complexity, time-frequency domain and the like and automatically learning the diagnostic logic from fault features through the machine learning algorithm, intelligent diagnosis of winding deformation is realized, thereby reducing labor costs and improving diagnosis efficiency.

An electromagnetic valve identification device configured to realize individual identification of an electromagnetic valve while suppressing an increase in manufacturing cost. The electromagnetic valve identification device is mounted on an industrial machine, such as a construction machine or an industrial vehicle, configured to move a hydraulic actuator to perform work. The electromagnetic valve identification device includes: an inductance measuring circuit configured to supply an alternating current to a solenoid of an electromagnetic valve of a hydraulic device, the hydraulic device being configured to supply pressure oil to the hydraulic actuator to operate the hydraulic actuator; a calculating portion configured to calculate an inductance of the solenoid based on the alternating current supplied to the solenoid by the inductance measuring circuit; and a storage portion configured to store the calculated inductance of the solenoid as individual identification information of the electromagnetic valve.

An apparatus for checking an inductor includes a detector outputting a detection signal including information corresponding to a parallel resistance component of a checking target inductor; and a determiner receiving the detection signal and determining whether the checking target inductor is satisfactory based on a magnitude of the parallel resistance component.

An apparatus for checking an inductor includes a detector outputting a detection signal including information corresponding to a parallel resistance component of a checking target inductor; and a determiner receiving the detection signal and determining whether the checking target inductor is satisfactory based on a magnitude of the parallel resistance component.

A power inductor includes: a body including a magnetic material; an internal coil disposed in the body and including a plurality of coil patterns; and a sensing coil disposed on the body and facing the internal coil.

A power inductor includes: a body including a magnetic material; an internal coil disposed in the body and including a plurality of coil patterns; and a sensing coil disposed on the body and facing the internal coil.

A diagnostic method for solenoid valves includes detecting, at excitation of the solenoid, a variation rate of the supply voltage of the solenoid and comparing it with a predetermined value, generating an error signal if the variation rate is lower than the predetermined value, otherwise, detecting characteristics of the waveform of the solenoid current over a time interval between a moment of excitation of the solenoid and a moment wherein the movable core reaches end-stroke position, comparing the detected characteristics with threshold values, generating an alarm signal if the detected characteristics are lower than the threshold values, otherwise calculating the value of solenoid resistance and comparing it with a minimum resistance value and a maximum resistance value when the solenoid current is in steady state, and generating an alarm signal if the solenoid resistance value is lower than the minimum resistance value or greater than the maximum resistance value.

A system and a method for diagnosing a contactor life using contactor coil current, whereby, after a current value of a current contactor coil is determined based on a resistance value and a voltage value according to a real-time temperature of a contactor coil, whether contactor lifetime has expired and an abnormal operation thus occurs can be previously diagnosed and predicted on the basis of whether an average current value when a corresponding contactor operates a preset number of times or more exceeds a threshold value.

The invention discloses an online diagnosis method for transformer winding deformation position, including: (1) collecting a transformer with known winding state and decomposing into several position sub-samples; (2) performing feature extraction on each position sub-sample with information entropy, adding with label indicating deformation and inputting into support vector machine to train diagnosis model; (3) decomposing a transformer under diagnosis into 9 position subsamples in the way of step (1), performing feature extraction of step (2) and inputting into the diagnostic model trained in step (2); (4) outputting diagnosis result from the support vector machine about whether the position sub-samples of the transformer is deformed. The invention can achieve intelligent diagnosis of winding deformation by comprehensively considering variations of monitoring indicators of the transformer in complexity, time-frequency domain and other aspects and automatically learning diagnostic logic from fault features through machine learning algorithms, thereby reducing labor costs and improving diagnostic efficiency.