A sensorless speed estimation algorithm for use in induction motor fault detection applications

Abstract

A novel sensorless speed estimation algorithm for use with direct online three-phase induction motors is proposed. Speed information is extracted from the motor current spectrum by tracking the frequency of key components, which vary as a function of motor rotational speed. An important advantage of this technique is that the speed estimation algorithm is independent of motor mechanical and electrical parameters. The algorithm operates via estimating rotor bar number, which is in turn used to determine rotational speed via rotor bar pass frequency detection including a sanity check on estimated speed via comparison with a linear speed estimate based on rated nameplate data. Experimental results are included for a range of three-phase induction motors including motors carrying faults (bearing, rotor, stator and air-gap eccentricity). The results demonstrate the robustness of the algorithm to motors operating with a variety of faults and thus the potential for use of the algorithm in induction motor fault detection and diagnosis applications.

Sensorless Speed Estimation for the Diagnosis of Induction Motors via MCSA. Review and Commercial Devices Analysis

“…Moreover, there is also the disadvantage that MEH often do not manifest themselves with sufficient intensity to be distinguished from the noise level (see Section 5.1.1 ), being necessary in this case, as the authors comment, a no-load test to determine the number of rotor slots and the index associated with each RSH (increasing invasiveness). Something similar happens with the method proposed in [ 79 ], where in this case the unreliable source is the preliminary speed estimation based on nameplate data (see Section 5.2.1 ).…”

“…In fact, most of the papers about RSH-based algorithms either do not provide any information on how to obtain those parameters [ 4 , 65 , 66 , 73 , 74 , 78 ], or propose non-automatic/invasive methods requiring human visual inspection, which limits their applicability in industrial environments [ 67 , 69 , 70 , 75 ]. Only a few papers have proposed self-commission methods to ascertain this set [ 68 , 79 ]. For example, in [ 68 ], the method relies on a preliminary speed estimation using the MEH, which, as already stated, is an unreliable source since small errors in frequency estimation mean big errors in speed.…”

Partial discharge detection for stator winding insulation of motors using artificial neural network

“…When we measure the bar, we found that its characteristic signal has the internal electric discharge phenomenon. 10 During the thermoplastic process in manufacturing the bar, a small amount of porosity inside the bar may be produced that causes the discharge phenomenon.…”

A Precise, General, Non-Invasive and Automatic Speed Estimation Method for MCSA Diagnosis and Efficiency Estimation of Induction Motors

“…These techniques (traditionally developed for electric motor control [10]), can be classified into two major families: Fundamental Model Based [11]- [13] and Magnetic Anisotropy Based. The latter can be subdivided into: Signal Injection Based [14]- [16] and Slotting and Rotational Frequency Sideband Harmonics Based (SRFSHB) [17]- [36].…”

“…To solve this lack of data, others propose non-automatic/invasive methods that require visual inspection and/or subject the motor to different operating conditions [30]- [33]. Finally, only three papers propose selfcommission methods to ascertain this set [34]- [36]:…”