Vanesa Fernandez-Cavero

In the field of electric mobility, induction motors (IMs) are key components in the powertrain of electric vehicles (EVs). They must be continuously monitored to avoid catastrophic faults and vehicle downtime. Faults in IM can be detected by measuring their physical quantities in the time domain (TD), frequency domain (FD) or time-frequency domain (TFD). The current supply operates in two states: transient and steady-state. To diagnose the IM's condition during the transients, it is common to apply TFD techniques such as the Short-Time Fourier Transform (STFT), Continuous Wavelet transform (CWT), Discrete Wavelet transform (DWT) or the Hilbert-Huang Transform (HHT). Focused on real-time broken rotor bars (BRB) diagnosis of IM status, this study analyzes the impact of sampling frequency (Fs), sampling period (Ws) and the TFD method utilized on the accuracy of four types of machine learning (ML): Decision Trees (DT), Random Forests (RF), k-Nearest Neighbor (kNN) and Support Vector Machine (SVM). A use case is introduced using an experimental dataset comprising five IM conditions (healthy, and with 1 to 4 BRB). The study evaluates energy patterns in the TFD and the resulting classification accuracy. The results demonstrate that, under certain diagnostic conditions, both Fs and Ws can be significantly reduced without jeopardizing the ML final accuracy.

The early detection and diagnosis of faults in induction motors is vital in today’s industry, since these are the motors used for the largest number of applications in the industrial environment and failure to detect a fault early can lead to significant losses. Bearing faults are the main problems detected in induction motors and several techniques have been developed to detect them. The use of the information contained in the motor vibrations is the main traditional source for its diagnosis, although there are also proposals that use the supply current, or the sound of the motor. Furthermore, these variables can be used in the time domain or in the frequency domain. The purpose of this work is to use explainable artificial intelligence (XAI) to determine which of these variables, and in which domain, contributes most to a correct diagnosis and how much can be gained in diagnosis by using multisensor data fusion. To carry out this comparison in the most objective way possible, a model selection procedure is proposed and boosting techniques are considered that prove to give a very precise diagnosis. The obtained diagnostic rules are then interpreted using SHAP values, a recent interpretation technique for complex classification procedures.

In traction, automotive, and some industrial processes, the operation of the induction motor is set using speed. This creates an additional control loop that is external to the current controller that inverters already possess. While the impact of the latter control loop on fault detection in induction motors has already been studied, the influence of closed-loop speed control has not been explored enough. This paper presents the findings on the impact of PID speed control on the broken rotor bar fault detection. The study reveals that the constant corrective actions of the PID controller to correct the speed cause an increase in the noise level and spectral leakage around the first harmonic in the stator current spectrum that prevents fault detection. It is concluded that it is necessary to use time-frequency analysis techniques with excellent spectral resolution, such as the Dragon Transform or the Min-Norm technique.

This paper aims to use and explore odd triple harmonics to perform fault detection in delta-connected induction motors, demonstrating their suitability to detect broken rotor bars. Harmonic components arise around the main odd triple components, generated by the interaction of the fault and other effects, such as saturation and voltage unbalance. The paper presents the mathematical modelling introducing the harmonic components, along with experimental results under several load levels, voltage unbalanced, and under inverter-fed power supply, demonstrating the effectiveness of the detection of the odd triple harmonics. The results compare line and phase currents, demonstrating that the proposed harmonics perform satisfactorily in phase current, suitable for fault detection in a wide operating range, including low-load cases.

Induction motors work under steady-state in many applications. Nevertheless, in some cases they experience periodic load fluctuations, which generate constant frequency harmonics close to variable frequency bar breakage harmonics. In these cases, time-frequency (t-f) transforms are better suited than steady-state analysis since the fault harmonic frequencies change in time. Even if the healthy and faulty frequencies do not overlap in the spectrum, if the speed is unknown, it is difficult to distinguish the constant frequency healthy harmonic from the variable frequency bar breakage harmonic. On the other hand, transient techniques present in technical literature are not precise enough to deal with both the changing frequency of the bar breakage harmonic and a close constant frequency (as the one generated by most of the periodic load fluctuations). To achieve reliable results under these challenging situations, a very precise time-frequency transform must be used, enabling to simultaneously draw the constant and variable frequencies, even if they are very close in the t-f plane. The Dragon-Transform is here proposed to address the problem. It is shown through simulation and experimental results, how it enables to very accurately plot up to five faulty harmonics evolutions, distinguishing at the same time the constant frequency of the load oscillation, traced as a very thin horizontal line. Precision is so high that even the oscillations caused by ripple effect can be observed for the first time in technical literature, enhancing the reliability of the diagnosis performed, and opening the path for a true solution of the problem.

There is an increasing interest in improving energy efficiency and reducing operational costs of induction motors in the industry. These costs can be significantly reduced, and the efficiency of the motor can be improved if the condition of the machine is monitored regularly and if monitoring techniques are able to detect failures at an incipient stage. An early fault detection makes the elimination of costly standstills, unscheduled downtime, unplanned breakdowns, and industrial injuries possible. Furthermore, maintaining a proper motor operation by reducing incipient failures can reduce motor losses and extend its operating life. There are many review papers in which analyses of fault detection techniques in induction motors can be found. However, all these reviewed techniques can detect failures only at developed or advanced stages. To our knowledge, no review exists that assesses works able to detect failures at incipient stages. This paper presents a review of techniques and methodologies that can detect faults at early stages. The review presents an analysis of the existing techniques focusing on the following principal motor components: stator, rotor, and rolling bearings. For steady-state and transient operating modes of the motor, the methodologies are discussed and recommendations for future research in this area are also presented.

A proper diagnosis of the state of an induction motor is of great interest to industry given the great importance of the extended use of this motor. Presently, the use of this motor driven by a frequency converter is very widespread. However, operation by means of an inverter introduces certain difficulties for a correct diagnosis, which results in a signal with higher harmonic content and noise level, which makes it difficult to perform a correct diagnosis. To solve these problems, this article proposes the use of a time-frequency technique known as Dragon Transform together with the functional ANOVA statistical technique to carry out a proper diagnosis of the state of the motor by working directly with the curves obtained from the application of the transform. A case study is presented showing the good results obtained by applying the methodology in which the state of the rotor bars of an inverter-fed motor is diagnosed considering three failure states and operating at different load levels.