Power inverters are essential components in modern electrical distribution systems. Switches are key elements of a power inverter, and they are the more affected components when a fault occurs. Breakdown of power-semiconductor devices represents around 38% of a power inverter fault. An open-circuit fault will keep the inverter working, jeopardizing its components and degrading its performance. The diagnosis and classification of power inverter faults have become important research subjects to avoid malfunctioning or damage of other components in an electric system. The Fourier transform and the wavelet-based multiresolution analysis (MRA) are the most applied signal processing techniques for identifying open-circuit faults in these devices. They are usually combined with other methods for increasing its effectiveness, which demands a considerable use of computational resources. This work introduces a novel low-computational-cost technique that directly analyzes the electric phase currents from a power inverter, in the time domain, utilizing three rotatory reference systems and basic arithmetic operations to identify open-circuit faults. The proposed method allows determining the specific power supply phase and the faulty switch, or switches, with high certainty. The obtained results are validated through exhaustive analyses against the Fourier transform and the wavelet-based MRA, demonstrating the proposed method suitability for online-monitoring applications, ensuring high sensitivity and accuracy on detecting and isolating open-circuit faults with low computation cost.

Mechanical failures are the most common defects in induction motors (IMs). They derive from the continuous starts and stops with high-load torque. They can cause interruptions in production lines with great economic losses. Therefore, online monitoring systems, allowing a continuous monitoring for fault diagnosis in early stages, have been intended for many researches in last years. Broken rotor bars (BRBs) are common failures, which are among the most difficult to detect since the induction motor works with apparent normality without giving any signal about the defect. Several methodologies have been proposed to detect BRBs in IMs. However, they require high-computational-complexity algorithms for parameterizing the current signal, making it difficult to achieve an online implementation of the corresponding monitoring system. Hence, aiming to overcome the limitations provoked by complex parameterizations of the current signal and to improve the fault classification accuracy, in this work, a novel methodology, based on a hardware processing system implemented on a field-programmable gate array (FPGA), for electric current signal analysis in the Walsh–Hadamard domain is proposed to achieve online intelligent BRB diagnosis. The $k$ -nearest neighbors ( $k$ -NN) algorithm is used for classifying different operational conditions from the Walsh–Hadamard transform (WHT) of the electric-current signal. Experimentally obtained results from applying the proposed method on acquired real data demonstrate that the introduced approach reaches a remarkable high efficiency on detecting and classifying BRBs.

Online monitoring of induction motors has increased significantly in recent years because these devices are essential components of any industrial process. Incipient fault detection in induction motors avoids interruptions in manufacturing processes and facilitates maintenance tasks to reduce induction motor timeout. Therefore, the proposal of novel approaches to assist in the detection and classification of induction motor faults is in order. In this work, a reliable and noninvasive novel technique that does not require computational demanding operations, since it just performs arithmetic calculations, is introduced for detecting and locating short-circuit faults in the stator windings of an induction motor. This method relies on phasor analysis and the RMS values of line currents, followed by a small set of simple if-then rules to perform the diagnosis and identification of stator winding faults. Obtained results from different experimental tests on a rewound induction motor stator to induce short-circuit faults demonstrate that the proposed approach is capable of identifying and locating incipient and advanced deficiencies in the windings’ insulation with high effectiveness.

An induction motor is an electric machine widely used in various industrial and commercial applications due to its efficiency and simple design. In this regard, a methodology based on the electric phasor analysis of line currents and the variations in the phase angles among these line currents is proposed. The values in degrees of the angles between every pair of line currents were introduced to a fuzzy logic algorithm based on the Mamdani model, developed using the Matlab toolbox for detection and isolation of the inter-turn short-circuit faults on the windings of an induction motor. To carry out the analysis, the induction motor was modified in its stator windings to artificially induce short-circuit faults of different magnitudes. The current signals are acquired in real time using a digital platform developed in the Delphi 7 high-level language communicating with a float point unit Digital Signal Processor (DSP) TMS320F28335 by Texas Instruments. The proposed method not only detects the short circuit faults but also isolates the faulty winding.

Electric fault diagnosis is an important subject for ensuring the operational efficiency and reliability of induction machines, which are widely used in the industrial sector. Motor current signature analysis (MCSA) is an effective, non-invasive technique that has been useful for diagnosing faults in these machines. MCSA is applied on the acquired stator currents during the induction machine operation to detect and identify specific characteristics related to distinct faulty conditions. In this work, different methodologies for electric current analysis as instantaneous space phasor (ISP) module, spectral examination through Fourier transform, multiresolution inspection utilizing wavelet transform, and current phasor observation with fuzzy logic, are proposed for detecting and classifying short-circuit faults among coils of a stator winding in an induction motor, which has been modified to induce short-circuit faults with different severity degrees on its windings.