In this paper, statistical signal processing techniques are applied to electromotive force signals captured in external coil sensors for adjacent and nonadjacent broken bars detection in induction motors. An algorithm based on spectral subtraction analysis is applied for broken bar identification, independent of the relative position of the bar breakages. Moreover, power spectrum analyses enable the discrimination between healthy and faulty conditions. The results obtained with experimental data prove that the proposed approach provides good results for fault detectability. Moreover, the identification of the faults, and the signal correlation indicator to prove the results are also presented for different positions of the flux sensor.

The aim of this work is to find out, through the analysis of the time and frequency domains, significant differences that lead us to obtain one or several variables that may result in an indicator that allows diagnosing the condition of the rotor in an induction motor from the processing of the stray flux signals. For this, the calculation of two indicators is proposed: the first is based on the frequency domain and it relies on the calculation of the sum of the mean value of the bispectrum of the flux signal. The use of high order spectral analysis is justified in that with the one-dimensional analysis resulting from the Fourier Transform, there may not always be solid differences at the spectral level that enable us to distinguish between healthy and faulty conditions. Also, based on the high-order spectral analysis, differences may arise that, with the classical analysis with the Fourier Transform, are not evident, since the high order spectra from the Bispectrum are immune to Gaussian noise, but not the results that can be obtained using the one-dimensional Fourier transform. On the other hand, a second indicator based on the temporal domain that is based on the calculation of the square value of the median of the autocovariance function of the signal is evaluated. The obtained results are satisfactory and let us conclude the affirmative hypothesis of using flux signals for determining the condition of the rotor of an induction motor.

Wound rotor induction motors are used in a certain number of industrial applications due to their interesting advantages, such as the possibility of inserting external rheostats in series with the rotor winding to enhance the torque characteristics under starting and to decrease the high inrush currents. However, the more complex structure of the rotor winding, compared to cage induction motors, is a source for potential maintenance problems. In this regard, several anomalies can lead to the occurrence of asymmetries in the rotor winding that may yield terrible repercussions for the machine's integrity. Therefore, monitoring the levels of asymmetry in the rotor winding is of paramount importance to ensure the correct operation of the motor. This work proposes the use of bicoherence of the stray flux signal, as an indicator to obtain an automatic classification of the rotor winding condition. For this, the Fuzzy C-Means machine learning algorithm is used, which starts with the bicoherence calculation and generates the different clusters for grouping and classification, according to the level of winding asymmetry. In addition, an analysis regarding the influence of the flux sensor position on the automatic classification and the failure detection is carried out. The results are highly satisfactory and prove the potential of the method for its future incorporation in autonomous condition monitoring systems that can be satisfactorily applied to determine the health of these machines.

Fault diagnosis in electrical machines is a cornerstone of operational reliability and cost-effective maintenance strategies. This review provides a comprehensive exploration of the integration of higher-order spectral analysis (HOSA) techniques—such as a bispectrum, spectral kurtosis, and multifractal wavelet analysis—with advanced artificial intelligence (AI) methodologies, including deep learning, clustering algorithms, Transformer models, and transfer learning. The synergy between HOSA’s robustness in noisy and transient environments and AI’s automation of complex classifications has significantly advanced fault diagnosis in synchronous and DC motors. The novelty of this work lies in its detailed examination of the latest AI advancements, and the hybrid framework combining HOSA-derived features with AI techniques. The proposed approaches address challenges such as computational efficiency and scalability for industrial-scale applications, while offering innovative solutions for predictive maintenance. By leveraging these hybrid methodologies, the work charts a transformative path for improving the reliability and adaptability of industrial-grade electrical machine systems.

The aim of this work was to develop a new unsupervised exploratory method of characterizing feature extraction and detecting similarity of movement during sleep through actigraphy signals. We here propose some algorithms, based on signal bispectrum and bispectral entropy, to determine the unique features of independent actigraphy signals. Experiments were carried out on 20 randomly chosen actigraphy samples of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) database, with no information other than their aperiodicity. The Pearson correlation coefficient matrix and the histogram correlation matrix were computed to study the similarity of movements during sleep. The results obtained allowed us to explore the connections between certain sleep actigraphy patterns and certain pathologies.

In this work, the application of the bicoherence (a squared normalized version of the bispectrum) of the stray flux signal is proposed as a way of detecting faults in the field winding of synchronous motors. These signals are analyzed both under the starting and at steady state regime. Likewise, two quantitative indicators are proposed, the first one based on the maximum values of the asymmetry and the kurtosis of the bicoherence matrix obtained from the flux signals and the second one relying on an algorithm based on the bicoherence image segmentation of the obtained pattern for each analyzed state. The results are analyzed through a comparative study for the two considered motor regimes, obtaining satisfactory results that sustain the potential application of the proposed methodology for the automatic field winding fault detection in real applications.