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Induction motors are one of the most used machines because they provide the necessary traction force for many industrial applications. Their easy operation, installation, maintenance, and reliability make them preferred over other electrical motors. Mechanical and electrical failures, as with other machines, can appear at any stage of their service life, making the stator intern-turn short-circuit fault (ITSC) stand out. Hence, its detection is necessary in order to extend and save useful life, avoiding a breakdown and unprogrammed maintenance processes as well as, in the worst circumstances, a total loss of the machine. Nonetheless, the challenge lies in detecting this type of fault, which has made the analysis and diagnosis processes easier. Such is the case with convolutional neural networks (CNNs), which facilitate the development of methodologies for pattern recognition in several areas of knowledge. Unfortunately, these techniques require a large amount of data for an adequate training process, which is not always available. In this sense, this paper presents a new methodology for the detection of incipient ITSC faults employing a modified cumulative distribution function (CDF) of the current stator signal. Then, these are converted to images and fed into a fast and compact CNN model, trained with a small data set, reaching up to 99.16% accuracy for seven conditions (0, 5, 10, 15, 20, 30, and 40 short-circuited turns) and four mechanical load conditions. [ABSTRACT FROM AUTHOR]

Induction motors are indispensable, robust, and reliable machines for industry; however, as with any machine, they are susceptible to diverse faults. Among the faults that a motor can suffer, broken rotor bars (BRBs) have become one of the most studied ones because the motor under this fault condition can continue operating with apparent normality, yet the fault severity can quickly increase and, consequently, generate the whole collapse of the motor, raising repair costs and the risk to people or other machines around it. This work proposes an expert system to detect BRB early, i.e., half-BRB, 1-BRB, and 2-BRB, from the current signal analysis by considering the following two operating regimes: start-up transient and steady-state. The method can diagnose the BRB condition by using either one regime or both regimes, where the objective is to somehow increase the reliability of the result. Regarding the proposed expert system, it consists of the application of two autoencoders, i.e., one per regime, to diagnose the BRB condition. To automatically separate the regimes of analysis and obtain the envelope of the current signal, the Hilbert transform is applied. Then, the particle swarm optimization method is implemented to compute the separation point of both regimes in the current signal. Once the signal is separated, the two autoencoders and a simple set of if-else rules are employed to automatically determine the BRB condition. The proposed expert system proved to be an effective tool, with 100% accuracy in diagnosing all BRB conditions. [ABSTRACT FROM AUTHOR]

Time-frequency analysis is commonly used for fault detection in induction motors. A variety of signal decomposition techniques have been proposed in the literature, such as Wavelet transform, Empirical Mode Decomposition (EMD), Multiple Signal Classification (MUSIC), among others. They have been successfully used in many works related with the topic. Nevertheless, the studied signals present amplitude changes and chirp-type frequency components that are difficult to track and isolate with the aforementioned techniques. The contribution of this work is the introduction of a novel technique for time-frequency signal decomposition that is based on an adaptive band-pass filter and the Short Time Fourier Transform (STFT), namely Fourier-Based Adaptive Signal Decomposition (FBASD) method. This method is capable of tracking and extracting nonstationary time-frequency components within a user-selected frequency band. With these components, a methodology for detecting and classifying broken rotor bars in induction motors using the startup transient current is also proposed. [ABSTRACT FROM AUTHOR]

Voltage unbalance and mechanical overload generate negative effects on induction motors, producing thermal damages in the stator insulation and a reduction of the motor lifetime. In this regard, the development of protection devices is crucial as they can help to maintain the integrity of the motor and avoid irreversible damages such as insulation system breakdown, short-circuits, and so on. In this work, a methodology to obtain a time-protection model for induction motors from voltage unbalance is presented. The methodology is based on the thermal impact on the motor produced by mechanical overload and voltage unbalance conditions. In general, it consists of the following steps: i) induce in the motor different overloads and voltage unbalance levels and monitor their thermal profiles at the stator winding, ii) obtain the time–overload curve of the motor, iii) determine both the thermal level according to both the time-overload curve and the overload thermal profile, and iv) estimate the parameters of the time-unbalance model using both the thermal level obtained and the unbalance thermal profiles. The model is validated through its implementation in an online protection scheme, the results show a similar behavior between overload and unbalance protections in the safe thermal level defined by the protection models. The protection scheme is carried out over a 750 W three-phase induction motor under different overload and voltage unbalance operating conditions. [ABSTRACT FROM AUTHOR]

Overheating is a negative effect that induction motors suffer under either mechanical or electrical anomalous conditions, which can decrement the motors' useful life or produce catastrophic damage. In this sense, a new approach for assisting in the analysis and solution of problems related to overheating in induction motors is the correlation of thermal effects between the abovementioned conditions. In this study, a methodology for the experimental extraction of mathematical models that correlate the thermal effect on the induction motor stator due to voltage unbalance and mechanical overload is developed. In general, the proposed methodology is based on thermal gradients at different points of an induction motor stator. These thermal gradients are generated from either voltage unbalance or mechanical overload. For voltage unbalance estimation, the definitions of IEEE standard 141, IEEE standard 1159, and NEMA norm are considered. The correlation models are carried out and validated experimentally on a three-phase induction motor of 746 W (1 hp). Results demonstrate effectiveness in the correlation at different stator points of both variables: unbalance voltage and mechanical overload. [ABSTRACT FROM AUTHOR]

Although induction motors (IMs) are robust and reliable electrical machines, they can suffer different faults due to usual operating conditions such as abrupt changes in the mechanical load, voltage, and current power quality problems, as well as due to extended operating conditions. In the literature, different faults have been investigated; however, the broken rotor bar has become one of the most studied faults since the IM can operate with apparent normality but the consequences can be catastrophic if the fault is not detected in low-severity stages. In this work, a methodology based on convolutional neural networks (CNNs) for automatic detection of broken rotor bars by considering different severity levels is proposed. To exploit the capabilities of CNNs to carry out automatic image classification, the short-time Fourier transform-based time–frequency plane and the motor current signature analysis (MCSA) approach for current signals in the transient state are first used. In the experimentation, four IM conditions were considered: half-broken rotor bar, one broken rotor bar, two broken rotor bars, and a healthy rotor. The results demonstrate the effectiveness of the proposal, achieving 100% of accuracy in the diagnosis task for all the study cases. [ABSTRACT FROM AUTHOR]

Empirical mode decomposition (EMD)-based methods are powerful digital signal processing techniques because they do not need a priori information of the target signal due to their intrinsic adaptive behavior. Moreover, they can deal with non-linear and non-stationary signals. This paper presents the field programmable gate array (FPGA) implementation for the complete ensemble empirical mode decomposition (CEEMD) method, which is applied to the condition monitoring of an induction motor. The CEEMD method is chosen since it overcomes the performance of EMD and EEMD (ensemble empirical mode decomposition) methods. As a first application of the proposed FPGA-based system, the proposal is used as a processing technique for feature extraction in order to detect and classify broken rotor bar faults in induction motors. In order to obtain a complete online monitoring system, the feature extraction and classification modules are also implemented on the FPGA. Results show that an average effectiveness of 96% is obtained during the fault detection. [ABSTRACT FROM AUTHOR]

The induction motor (IM) is one of the most important elements in industry. Although IMs are robust machines, they are susceptible to faults, where the stator winding short-circuit fault is one of the most common ones. In this work, the Shannon entropy (SE) index and a fuzzy logic (FL) system are proposed to diagnose short-circuit faults, considering both different severity levels and different load conditions. In the proposed methodology, a filtering stage based on brick-wall band-pass filters is firstly carried out. After this stage, the SE index is computed to quantify the fault severity and a FL system is applied to diagnose the IM condition in an automatic way. Unlike other works that propose some types of space transformations, the proposal is only based on a filtering stage and a time domain index, requiring low computational resources. The obtained results demonstrate the effectiveness of the proposal, i.e., the SE index quantifies the fault severity, regardless of the mechanical load, and the proposed FL system achieves a positive classification rate of 98%. [ABSTRACT FROM AUTHOR]

Due to the relevance and severity of damages caused in induction motors by broken rotor bars (BRBs), the development and application of new detection methods that offer an efficient and reliable diagnosis in terms of processing and performance are still demanding tasks. This paper presents a methodology based on the Synchrosqueezing transform for detection of BRBs during the startup transient. In order to validate the proposal, a synthetic signal with different noise levels and real current signals of an induction motor are analyzed. Three severities of damage are considered: half broken, one broken, and two broken rotor bars. For automatic diagnosis, a threshold-based stage using the Pearson product-moment correlation coefficient is presented. Both synthetic and real experiment results demonstrate the proposal effectiveness. [ABSTRACT FROM AUTHOR]

Motor current signals analysis (MCSA) is a widely used approach for fault diagnostics in induction motors (IMs). It consists of detecting a specific signature or pattern associated to a fault condition from current signals. In particular, the fault of broken rotor bars (BRBs) is featured by a V-shaped pattern in the time-frequency domain during the startup transient. Although many techniques and methodologies have been presented in literature, most of them have been focused on analyzing consolidated faults such as one- BRB and multiple BRBs; in contrast, the BRB incipient detection, such as half BRB, has been rarely investigated. Hence, a methodology based on a new technique named Tooth-fast Fourier transform (FFT) to detect both incipient and consolidated BRB conditions is presented in this work. It consists of two windows moving along the analyzed current signal, where the FFT is performed for each window. Next, the spectra are subtracted for minimizing the stationary frequencies and maximizing the moving-ones. The signature of the moving frequencies in the resulting spectrogram has a “teeth” shape, giving the name to the proposed technique. Next, a weight function and a classification stage employing four indicators are presented for automatic diagnostics. The proposal is validated and tested using both synthetic and real signals. For the latter, different levels of BRB, i.e., half BRB, one BRB, and two BRBs, are considered. Results demonstrate the effectiveness and usefulness of the proposal to detect both incipient and consolidated BRB faults in IMs. [ABSTRACT FROM AUTHOR]

Induction motors (IMs) are one of the most commonly used rotating machines in industry. In order to avoid downtimes and economical losses, development of condition monitoring systems is of paramount importance. Inter-turn short circuit (ITSC) faults are one of the most commonly occurring faults in IMs. In this regard, motor current signature analysis (MCSA) is a low cost and noninvasive technique, requiring only the current signal to perform the condition monitoring. Also, the development of tools that emulate IM faults, allow the design, calibration and validation of MCSA based techniques. In this work, a multilayer neural network-based model to reproduce the current signatures associated with ITSC fault conditions is presented. The model considers both five severities of ITSC faults and four torque levels. This model is also implemented on a power hardware in the loop scheme to provide a novel tool to test condition monitoring systems of IMs. [Display omitted] [ABSTRACT FROM AUTHOR]