Cost-Effective Reduced Envelope of the Stator Current via Synchronous Sampling for the Diagnosis of Rotor Asymmetries in Induction Machines Working at Very Low Slip

Abstract

Fault diagnosis of rotor asymmetries of IM using the stator current relies on the detection of the characteristic signatures of the fault harmonics in the current spectrum. In some scenarios, such as large induction machines running at a very low slip, or unloaded machines tested offline, this technique may fail. In these scenarios, the fault harmonics are very close to the frequency of the fundamental component, and have a low amplitude, so that they may remain undetected, buried under the fundamental’s leakage, until the damage is severe. To avoid false positives, a proven approach is to search for the fault harmonics in the current envelope, instead of the current itself, because in this case the spectrum is free from the leakage of the fundamental. Besides, the fault harmonics appear at a very low frequency. Nevertheless, building the current spectrum is costly in terms of computing complexity, as in the case of the Hilbert transform, or hardware resources, as in the need for simultaneously sampling three stator currents in the case of the EPVA. In this work, a novel method is proposed to avoid this problem. It is based on sampling a phase current just twice per current cycle, with a fixed delay with respect to its zero crossings. It is shown that the spectrum of this reduced set of current samples contains the same fault harmonics as the spectrum of the full-length current envelope, despite using a minimal amount of computing resources. The proposed approach is cost-effective, because the computational requirements for building the current envelope are reduced to less than 1 % of those required by other conventional methods, in terms of storage and computing time. In this way, it can be implemented with low-cost embedded devices for on-line fault diagnosis. The proposed approach is introduced theoretically and validated experimentally, using a commercial induction motor with a broken bar under different load and supply conditions. Besides, the proposed approach has been implemented on a low-cost embedded device, which can be accessed on-line for remote fault diagnosis.

Automatic Early Broken-Rotor-Bar Detection and Classification Using Otsu Segmentation

“…The kurtosis gives a numerical value to identify the motor condition into four different classes [3]: 1) Healthy, 2) Half-Broken Rotor Bar (HBRB), 3) One-Broken Rotor Bar (1BRB), and 4) Two-Broken Rotor Bar. The normal distribution, given in (24), of the kurtosis value is used in this work as classification parameter to identify the induction-motor condition. (24) In (24), µ is the mean, σ is the standard deviation and σ 2 is the variance.…”

“…The normal distribution, given in (24), of the kurtosis value is used in this work as classification parameter to identify the induction-motor condition. (24) In (24), µ is the mean, σ is the standard deviation and σ 2 is the variance. The mean and standard deviation are computed according to (15) and (17), respectively, applying Otsu segmentation on the obtained histogram from 20 trials for each treated motor condition.…”

“…However, nearly none of the above are able to detect half BRB. Hence, reliable identification of half BRB is still open field under investigation as they are really difficult to detect [22][23][24][25][26][27].…”

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

“…To be responsive, CBMs must be able to operate on-line, in a non-invasive way, so that any fault can be detected in an incipient state and corrective measures can be deployed before the fault gets worse [6][7][8][9][10][11]. This requires fast and simple fault diagnostic techniques [12], that can be implemented in embedded field devices, such as digital signal-processors (DSPs) or field-programmable arrays (FPGAs). One of such diagnostic techniques relies on the design of sliding mode observers (SMO) for observing IM states obtained from the healthy and faulty model.…”

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

“…MCSA relies on the detection of the specific fault harmonics that each type of fault induces in the stator current, which may be considered their characteristic signature. In steady state, this signature can be detected in the spectrum of the stator current, obtained using the FFT [11][12][13][14][15]. Some of the most common induction machine faults and their characteristic signatures are:…”