Detecting industrial motor faults with current signatures

Background: A major player in industry is the induction motor. The constant motion and mechanical nature of motors causes much wear and tear, creating a need for frequent maintenance such as changing contact brushes. Unmannered and infrequent monitoring of motors, as is common in the industry, can lead to overexertion and cause major faults. If a motor fault is detected earlier through the use of automated fault monitoring, it could prevent minor faults from developing into major faults, reducing the cost and down-time of production due the motor repairs. There are few available methods to detect three-phase motor faults. One method is to analyze average vibration signals values of V, I, pf, P, Q, S, THD and frequency. Others are to analyze instantaneous signal signatures of V and I frequencies, or V and I trajectory plotting a Lissajous curve. These methods need at least three sensors for current and three for voltage for a three-phase motor detection. Methods: Our proposed method of monitoring faults in three-phase industrial motors uses Hilbert Transform (HT) instantaneous current signature curve only, reducing the number of sensors required. Our system detects fault signatures accurately at any voltage or current levels, whether it is delta or star connected motors. This is due to our system design, which incorporates normalized curves of HT in the fault analysis database. We have conducted this experiment in our campus laboratory for two different three-phase motors with four different fault experiments. Results: The results shown in this paper are a comparison of two methods, the V and I Lissajous trajectory curve and our HT instantaneous current signature curve. Conclusion: We have chosen them as our benchmark as their fault results closely resemble our system results, but our system benefits such as universality and a cost reduction in sensors of 50%.

Edge Coil Force Fitting and Current Optimal Commutation Algorithm for Magnetic Levitation Planar Motor With Moving Magnet

In this paper, edge coils are added to the commutation algorithm of the coil array. In order to reduce the theoretical modeling error of the edge coil force, a method of edge coil force fitting based on radial basis function (RBF) network is proposed. The obtained attenuation function of edge force can replace the weighting function in the switching algorithm, so it can effectively reduce the current density of the central coils and the heat loss power of the coil array. On this basis, a non-iterative current optimal commutation algorithm is proposed. The algorithm takes the weighted sum of the 2-norm of the coil current and the 2-norm of the difference between the coil current and the saturation current as the optimization objective, and obtains the analytical expression of the instantaneous current by solving the Karush Kuhn Tucker (KKT) equation. The results of simulation show that, compared with the direct decoupling algorithm with weighting function, the proposed commutation algorithm can reduce the heat loss power of the coil array and allow the translator to provide greater acceleration under the same maximum current limitation.

Approximate instantneous current in RLC circuit

In this study, a mixed rule of degree of precision nine has been developed and implemented in the field of electrical sciences to obtain the instantaneous current in the RLC- circuit for particular value .The linearity has been performed with the Volterra’s integral equation of second kind with particular kernel . Then the definite integral has been evaluated through the mixed quadrature to obtain the numerical result which is very effective. A polynomial has been used to evaluate Volterra’s integral equation in the place of unknown functions. The accuracy of the proposed method has been tested taking different electromotive force in the circuit and absolute error has been estimated.