Magnetic flux analysis is a condition monitoring technique that is drawing the interest of many researchers and motor manufacturers. The great enhancements and reduction in the costs and dimensions of the required sensors, the development of advanced signal processing techniques that are suitable for flux data analysis, along with other inherent advantages provided by this technology, are relevant aspects that have allowed the proliferation of flux-based techniques. This article reviews the most recent scientific contributions related to the development and application of flux-based methods for the monitoring of rotating electric machines. Particularly, aspects related to the main sensors used to acquire magnetic flux signals as well as the leading signal processing and classification techniques are commented on. The discussion is focused on the diagnosis of different types of faults in the most common rotating electric machines used in industry, namely: squirrel cage induction machines, wound rotor induction machines, permanent magnet machines, and wound field synchronous machines. A critical insight of the techniques developed in the area is provided and several open challenges are also discussed.

In recent years wound-rotor induction motors (WRIM) have received special attention because of their broad use as generators in wind turbine units as well as in some large power applications in industrial plants. Some classical approaches perform the detection of certain faults based on the FFT analysis of the steady state current (MCSA); they have been lately complemented with new transient time-frequency-based techniques to avoid false alarms. Nonetheless, there is still a need to improve the already existing methods to overcome their remaining drawbacks and increase the reliability of the diagnostic. In this regard, emergent technologies are being explored, such as the analysis of stray flux at the vicinity of the motor. Recently, this technique has been applied to detect broken rotor bar failures and misalignments in cage motors, avoiding some drawbacks of well-established tools. However, the application of these techniques to WRIM has not been studied. This work explores the analysis of the external magnetic field under the starting to detect rotor winding asymmetry defects in WRIM by using advanced signal processing techniques. Moreover, a new fault indicator based on this quantity is introduced, demonstrating the potential of this technique to quantify and monitor rotor winding asymmetries in WRIM.

Induction motors are essential and widely used components in many industrial processes. Although these machines are very robust, they are prone to fail. Nowadays, it is a paramount task to obtain a reliable and accurate diagnosis of the electric motor health, so that a subsequent reduction of the required time and repairing costs can be achieved. The most common approaches to accomplish this task are based on the analysis of currents, which has some well-known drawbacks that may lead to false diagnosis. With the new developments in the technology of the sensors and signal processing field, the possibility of combining the information obtained from the analysis of different magnitudes should be explored, in order to achieve more reliable diagnostic conclusions, before the fault can develop into an irreversible damage. This paper proposes a smart-sensor that explores the weighted analysis of the axial, radial, and combination of both stray fluxes captured by a low-cost, easy setup, non-invasive, and compact triaxial stray flux sensor during the start-up transient through the short time Fourier transform (STFT) and characterizes specific patterns appearing on them using statistical parameters that feed a feature reduction linear discriminant analysis (LDA) and then a feed-forward neural network (FFNN) for classification purposes, opening the possibility of offering an on-site automatic fault diagnosis scheme. The obtained results show that the proposed smart-sensor is efficient for monitoring and diagnosing early induction motor electromechanical faults. This is validated with a laboratory induction motor test bench for individual and combined broken rotor bars and misalignment faults.

In the induction motor predictive maintenance area there is a continuous search for new techniques and methods that can provide additional information for a more reliable determination of the motor condition. In this context, the analysis of the stray flux has drawn the interest of many researchers. The simplicity, low cost and potential of this technique makes it attractive for complementing the diagnosis provided by other well-established methods. More specifically, the study of this quantity under the starting has been recently proposed as a valuable tool for the diagnosis of certain electromechanical faults. Despite this fact, the research in this approach is still incipient and the employed signal processing tools must be still optimized for a better visualization of the fault components. Moreover, the development of advanced algorithms that enable the automatic identification of the resulting transient patterns is another crucial target within this area. This paper presents an advanced algorithm based on the combined application of MUSIC and neural networks that enables the automatic identification of the time-frequency patterns created by the fault components under starting as well as the subsequent determination of the fault severity level. Two faults are considered in the work: rotor problems and misalignments. Also, different positions of the external coil sensor are studied. The results prove the potential of the intelligent algorithm for the reliable diagnosis of electromechanical faults.

Tool condition monitoring (TCM) is one of the most relevant tasks during a machining process. The latest high-quality productivity standards make it essential to monitor the cutting tool wearing. Current TCM methodologies demand the installation of sensors near the working area, which in practical terms, it is not the most optimal solution since the final diagnosis can be disturbed by noisy signals and direct interferences with the machining process. This article proposes a novel noninvasive methodology based on the time–frequency analysis of the stray flux captured around the spindle-motor to detect and estimate the wearing level in cutting tools. Moreover, a new fault indicator based on this quantity is introduced through the application of the discrete wavelet transform. The results obtained are promising and demonstrates the effectiveness of the proposal to become a complementary source of information to classical approaches. This is validated with a Fanuc Oi mate computer numeric control turning machine for three different cutting tool wearing levels and different cutting depths.

Bearings are the elements that allow the rotatory movement in induction motors, and the fault occurrence in these elements is due to excessive working conditions. In induction motors, electrical erosion remains the most common phenomenon that damages bearings, leading to incipient faults that gradually increase to irreparable damages. Thus, condition monitoring strategies capable of assessing bearing fault severities are mandatory to overcome this critical issue. The contribution of this work lies in the proposal of a condition monitoring strategy that is focused on the analysis and identification of different fault severities of the outer race bearing fault in an induction motor. The proposed approach is supported by fusion information of different physical magnitudes and the use of Machine Learning and Artificial Intelligence. An important aspect of this proposal is the calculation of a hybrid-set of statistical features that are obtained to characterize vibration and stator current signals by its processing through domain analysis, i.e., time-domain and frequency-domain; also, the fusion of information of both signals by means of the Linear Discriminant Analysis is important due to the most discriminative and meaningful information is retained resulting in a high-performance condition characterization. Besides, a Neural Network-based classifier allows validating the effectiveness of fusion information from different physical magnitudes to face the diagnosis of multiple fault severities that appear in the bearing outer race. The method is validated under an experimental data set that includes information related to a healthy condition and five different severities that appear in the outer race of bearings.