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

Abstract

Induction machines drive many industrial processes and their unexpected failure can cause heavy production losses. The analysis of the current spectrum can identify online the characteristic fault signatures at an early stage, avoiding unexpected breakdowns. Nevertheless, frequency domain analysis requires stable working conditions, which is not the case for wind generators, motors driving varying loads, and so forth. In these cases, an analysis in the time-frequency domain-such as a spectrogram-is required for detecting faults signatures. The spectrogram is built using the short time Fourier transform, but its resolution depends critically on the time window used to generate it-short windows provide good time resolution but poor frequency resolution, just the opposite than long windows. Therefore, the window must be adapted at each time to the shape of the expected fault harmonics, by highly skilled maintenance personnel. In this paper this problem is solved with the design of a new multi-band window, which generates simultaneously many different narrow-band current spectrograms and combines them into as single, high resolution one, without the need of manual adjustments. The proposed method is validated with the diagnosis of bar breakages during the start-up of a commercial induction motor.

Locally optimized chirplet spectrogram for condition monitoring of induction machines in transient regime

“…Furthermore, this optimization process does not increase the computational costs compared with conventional TF transforms, in which the chirplet parameters are constant throughout the TF plane. The LOCS defines a large number of chirplet windows, with different parameters, and combines them into a single, complex time window, as in [61]. In this way, it can perform the chirplet transform of the current signal using a high number of chirplet windows in the dictionary, while keeping an extremely low computing cost, similar to the cost of computing a conventional, non-optimized spectrogram, based on a single window.…”

Low-Computational-Cost Hybrid FEM-Analytical Induction Machine Model for the Diagnosis of Rotor Eccentricity, Based on Sparse Identification Techniques and Trigonometric Interpolation

“…Under these conditions, traditional fast Fourier transform (FFT)-based diagnostic techniques cannot be used for fault diagnosis purposes. The current analysis of the start-up transient of the IM and time-frequency distributions as the spectrogram [10], can correctly detect and generate the evolution of the fault harmonics in the joint time-frequency domain [43].…”

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

“…In addition, variable speed drives are used to adapt their speed to production requirements. Speed variations imply the use of diagnostic techniques that are designed to operate in the join time-frequency domain, such as those presented in [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ], based on the analysis of the machine current under transient conditions. These methodologies can be considered as an extension to the transient regime of the MCSA methodology, developed for the steady state regime of induction machines [ 10 ].…”

“…In addition, the rotor speed must be accurately measured. The diagnostic process consists of detecting high amplitude harmonics at fixed frequencies, calculated with Equations (1), (2), (3), and (4). These Equations share a distinctive characteristic: they depend on the machine speed.…”

“…Therefore, in transient conditions, with non-constant speed, the positions of the fault harmonics are no longer constant, which blurs their characteristic signatures (smearing effect) and hinders the application of steady state fault diagnostic techniques. In fact, as the frequency of these harmonics change with time, what Equations (1), (2), (3), and (4) provide is their instantaneous frequency (IF) [23,24].…”