Induction machine model with finite element accuracy for condition monitoring running in real time using hardware in the loop system

Abstract

Most industrial processes are run by induction machines (IMs). Condition monitoring of IM assures their continuity of service, and it may avoid highly costly breakdowns. Among the methods for condition monitoring, online motor current signature analysis is being attracting a rising interest, because it is non-invasive, and it can identify a wide variety of faults at early stage. To favour the development of on-line fault diagnosis techniques, it is necessary to have real-time currents with which test the new techniques and devices. Models running in real time in hardware-in-the-loop (HIL) simulators are a suitable alternative to balance the drawbacks of test benches (costly, limited machines, faults and working conditions). These models must be accurate enough to reflect the effects of a fault and they must be running in real time. A promising technique based on the equivalent circuit parameters calculation of IM by finite element analysis (FEA) is attracting a rising interest due to its reliability, performance and the possibility of being run in a HIL. Nevertheless, prior to running in a HIL, it is necessary to compute the IM parameters using FEA, which requires long simulation times and high computing resources. Consequently, covering a whole range of degrees of a giving fault could be unaffordable. What is proposed in this paper is to apply the sparse subspace learning (SSL) in combination with the hierarchical Lagrangian interpolation (HLI) to obtain the parametric solutions of the faulty IM model that cover the whole range of severity of a given fault, with a reduced number of FEA simulations. By means of this approach it is possible not only to boost the computation speed but also to achieve a significant reduction of memory requirements while retaining reasonable accuracy compared to traditional FEA, so enabling the real-time simulation of predictive models.

Winding Tensor Approach for the Analytical Computation of the Inductance Matrix in Eccentric Induction Machines

“…For computing the inductance matrix, the inverse of the air gap length function is needed to obtain the permeance function of the machine. It can be fully defined in terms of the coordinates of the rotor center ( 17 ) as [ 23 ] where …”

“…Its components can be determined by direct measurements, as in [35,36], or computed numerically. FEM models have been used for inductance computation in [23], and in [37] a FEM model is combined with a Preisach model for iron loss evaluation. An alternative is to use analytical methods for inductance computation.…”

“…The IM models needed in the aforementioned diagnostic techniques can be built using the finite elements method (FEM) with a very high accuracy [ 21 , 22 , 23 ], but FEM demands huge computing resources, in terms of time and memory, which hinders its use in low-power embedded units. A faster and leaner alternative is to use analytical models [ 23 , 24 ] that can reproduce the characteristic harmonics induced in the current by a given fault.…”

“…The IM models needed in the aforementioned diagnostic techniques can be built using the finite elements method (FEM) with a very high accuracy [ 21 , 22 , 23 ], but FEM demands huge computing resources, in terms of time and memory, which hinders its use in low-power embedded units. A faster and leaner alternative is to use analytical models [ 23 , 24 ] that can reproduce the characteristic harmonics induced in the current by a given fault. Another diagnostic area in which IM models are used is in the training of neural networks or expert systems for fault diagnosis [ 25 , 26 , 27 , 28 , 29 , 30 , 31 ], which need thousands of tests performed under different working conditions with controlled degrees of a machine fault.…”

Fault Diagnosis in the Slip–Frequency Plane of Induction Machines Working in Time-Varying Conditions

“…Some of the most common induction machine faults and their characteristic signatures are: Rotor asymmetries, which generate fault related harmonics at frequencies given by [ 16 , 17 ]: where s is the slip, , being the synchronous speed of the machine and n its actual speed), and is the power supply frequency. Mixed eccentricity, which generates fault harmonics at frequencies given by [ 18 , 19 , 20 ] where is the rotational frequency of the motor, and p is the pole pairs number. Cyclic faults in the outer or inner races of bearings, which generate fault harmonics at frequencies that depend on the fault localization (inner or outer race), and on the bearing characteristics (number of balls, ball diameter, bearing pitch diameter, and the contact angle) [ 21 , 22 ], given by for the outer and inner race, respectively, where is the number of bearing balls.…”