Short-Frequency Fourier Transform for Fault Diagnosis of Induction Machines Working in Transient Regime

Abstract

Transient-based methods for fault diagnosis of induction machines are attracting a rising interest, due to their reliability and ability to adapt to a wide range of induction machine (IM)'s working conditions. These methods compute the time-frequency (TF) distribution of the stator current, where the patterns of the related fault components can be detected. A significant amount of recent proposals in this field have focused on improving the resolution of the TF distributions, allowing a better discrimination and identification of fault harmonic components. Nevertheless, as the resolution improves, computational requirements (power computing and memory) greatly increases, restricting its implementation in low cost devices for performing on-line fault diagnosis. To address these drawbacks, in this paper the use of the short frequency Fourier transform (SFFT) for fault diagnosis of IMs working under transient regimes is proposed. The SFFT not only keeps the resolution of traditional techniques, such as the short time Fourier transform (STFT), but also achieves a drastic reduction of computation time and memory resources, making this proposal suitable for on-line diagnosis. This method is theoretically introduced and experimentally validated using a laboratory test bench.

Automatic Fault Diagnostic System for Induction Motors under Transient Regime Optimized with Expert Systems

“…As explained in [5], this simplification process is implemented using a Gaussian window and a short time Fourier transform (STFT), by iteratively moving the Gaussian window along the time domain and performing the frequency axis re-scaling at each step (see an example in Figure 1). An alternative option is to replace the STFT transform in HOTA by the short-frequency Fourier transform (SFFT), as in [6]. SFFT generates a time-frequency Gaussian window which is displaced along the frequency axis, instead of the time axis ( Figure 1).…”

“…As discussed above, the t-f spectrum is generated with a STFT transform. Nevertheless, as explained in [6], a SFFT transform can be used instead STFT, obtaining the same t-f spectrum. It is remarkable that the use of the SFFT in diagnostic applications reduces greatly the required computational power, regarding the STFT.…”

“…Therefore, fault diagnosis of IMs has become an important area of condition-based maintenance (CBM) programs, to avoid the high economic losses generated by unexpected breakdowns of IMs and sudden stoppages of the production lines that they drive. Specifically, fault diagnosis techniques based on the analysis of the MCSA [1][2][3][4][5][6] have gained a wide industrial deployment, due to their simplicity, low requirements of hardware and software, and capability for on-line simultaneous detection of a wide range of machine faults. Despite its advantages, industrial application of MCSA in harsh industrial environments, under real working conditions, is challenging.…”

Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines

“…To extend MCSA to the fault diagnosis of IM working in transient regime, advanced time-frequency (TF) transforms of the current are needed, so that the transient fault signatures can be identified in a joint TF domain. These transforms can be linear, such as the short-time Fourier transform (STFT) [28][29][30][31], the short-frequency Fourier transform (SFFT) [31,32] and the wavelet transform (WT) [33], or quadratic, such as the Wigner-Ville distribution (WVD) [34] or the ambiguity function [8]. Quadratic TF transforms can achieve optimal resolution for mono-component chirp signals but, in case of multi-component ones, they produce cross-terms artifacts that pollute the TF representation of the current, making it difficult the correct identification of the fault harmonics.…”

Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder‐Based Deep Neural Network

“…Traditional fault diagnosis methods use efficient feature extraction and a machine learning algorithm, such as -nearest neighbors ( -NN), support vectors machines (SVMs), and artificial neural networks (ANNs), to perform fault diagnosis [16][17][18][19][20]. Feature extraction is a cumbersome process that requires expert knowledge and also adds to the complexity of the fault diagnosis scheme [21].…”