Oblique projection pre-processing and TLS application for diagnosing rotor bar defects by improving power spectrum estimation

Abstract

Diagnosing defects on rotating machines can be reached by several angles. When dealing with asynchronous motor drive, such physical elements rotate that a natural angle for treating the healthiness of the motor can be obtained by the use of spectral analysis tools. It is now stated that electrical or mechanical defects, which appear periodically as well, can be retrieved by analyzing the amplitude of particular frequencies inside an estimated power spectrum. When dealing with broken rotor bars detection it is essential to accurately localize the frequencies related to the slip inside the power spectrum. The diagnosis is thereafter made by indicators given with respect to their power. For actual low level of load operations, the supply frequency generally masks the frequencies which could be exploited for indicators. Therefore, we propose to cancel, as well as possible, the contribution of this supply frequency to develop the useful and closely components. The resolution should be very thin for the components to be estimated. In consequence, we use a prior-knowledge subspace-based frequency estimator, already developed in the literature, we complete with an Oblique Projection coupled with a Total Least Squares solution for estimating the power of the resulting estimated frequencies. Finally, we show by means of a real application how it contributes to improve the power spectrum estimation when compared to the FFT or periodogram-based analysis and how the aforementioned power spectrum makes the diagnosis indicator of rotor bars efficient.

Improving Health Care Management Through Persistent Homology of Time-Varying Variability of Emergency Department Patient Flow

“…Time series analysis has long been used and hundreds of methods have been proposed. One way of dealing with this, is to embed the time series on a manifold [6] with for example Hankel-like embedding [3]. Inspired by [22], we use a sliding window of size N on the piecewise time series {Y k } N L k=1 determined by (2).…”

Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation

“…In order to successfully perform fault detection, a fault criterion is required to measure the machine state and fault severity. Since the information about the fault is carried out by the sidebands amplitude a k (k 6 = 0), we propose to compute the sidebands energy to fundamental frequency energy ratio as a fault indicator [58]. This criterion is expressed mathematically as…”

Motor Current Signal Analysis Based on a Matched Subspace Detector

“…Nevertheless, the detection step is often performed manually, based on visual inspection of the fault signature on the stator currents. To automatically detect a fault, several authors have proposed algorithms based on threshold detectors [10], [11]. However, the implementation of these techniques requires the user to set a threshold manually, based on the knowledge of the induction machine to be supervised.…”

“…In [10], [21], the authors have proposed a Maximum Likelihood (ML) parametric estimator for current spectral analysis, and a simple ad-hoc fault criterion. In [11], the same fault criterion is used in conjunction with an interference cancellation technique for the removal of the supply component at 50Hz. However, in these two studies, no strategy has been provided to automatically detect a fault from the estimated spectral components.…”

“…As compared to the techniques in [11], [21], this paper directly focuses on the MCSA fault detection problem. Specifically, we propose a new matched subspace detector that treats the supply frequency components as an interference signal.…”