Neural Network Used to Stator Winding Interturn Short-Circuit Fault Detection in an Induction Motor Driven by Frequency Converter

Abstract

This work is the application of a Multilayer Perceptron Artificial Neural Network (MLP ANN) to detect early interturn short-circuit faults in a three-phase converter-fed induction motor. The quantity used to analyze the problem is the stator current or, more specifically, the harmonic content of its frequency spectrum, also called current signature. The analysis through the current signature is a non-invasive method and may be embedded in the frequency converter, what is a great advantage. The dataset used for training and validating the ANN is obtained using a test bench that allows applying different levels of interturn short-circuits in the machine. It is observed that the fault motor dataset and healthy motor dataset are difficult to separate, which demands a large computational effort to choose a proper MLP topology. The MLP is trained by two different algorithms (the classical error Backpropagation -BP -and an adaptation of the newer Extreme Learning Machine -ELM) and the results are thoroughly explored, including after the application of a pruning method called CAPE. Then it is slightly compared with the results of a Self-Organized Map ANN [1] obtained by using the same dataset.

Data Mining Applications to Fault Diagnosis in Power Electronic Systems: A Systematic Review

“…The proper performance of machine learning techniques in various studies led to the rapid development of the use of these methods in the science and industry related to power electronics. For the first time in 2013, a new machine learning technique called Extreme Learning Machine (ELM) has been proposed to predict the inter-turn SC fault in a three-phase converter-fed induction motor [33]. In the same study, in order to express the effectiveness of the suggested procedure, one of the ANN methods called MLP is also utilized to identify faults, which the results emphasize the superiority of the ELM method.…”

“…𝜃(𝑥) denotes the agent of a nonlinear mapping function. Computation SC Converter-fed induction motors [31] Rotor fault Converter-fed induction motor and changeable rotors [148] OC switch fault Three-parallel converters in a wind turbine [149] OC Proton exchange membrane fuel cell and DC-DC Converter [33] Intern-turn SC Three-phase converter-fed induction motor [ Mainly in solving most problems, it has a high dependence on the type of inputs. It is highly sensitive to complex and noisy data.…”

“…Not able to extract features from input data. [32] Faults in a single thyristor and the faults happening in two thyristors at the same time Three-phase full-bridge controlled rectifier [33] Intern-turn SC Three-phase converter-fed induction motor [52] OC switch fault Cascaded H-bridge seven-level converter [152] Diode OC Three-phase full-bridge rectifier [7] OC, SC, component degradation of power MOSFET, inductor, diode, and capacitor DC-DC Converter (closed-loop single-ended primary inductance converter) [56] Eight kinds of faults related to DC bus capacitor and energy storage inductor…”

“…Improper performance in the face of time-series data is considered as one of the problems of this method. [32] Faults in a single thyristor and the faults happening in two thyristors at the same time Three-phase full-bridge controlled rectifier [34] valve SC, valve pulse loss, single-phase grounding, two-phase grounding, three-phase grounding, DC line grounding HVDC converter [35] SC incipient fault Three-phase squirrel-cage induction motor fed by a sinusoidal PWM converter [37] Switch OC Cascaded H-bridge multi-level (5-level) inverter [40] thyristors OC fault Three-phase full-bridge rectifier [162] Switch OC Three-level NPC inverter [152] Diode OC Three-phase full-bridge rectifier [155] Structural and functional faults Analog to digital converter [7] OC, SC, component degradation of power MOSFET, inductor, diode, and capacitor DC-DC Converter (closed-loop single-ended primary inductance converter) [56] Eight kinds of faults related to DC bus capacitor and energy storage inductor Dual-buck bidirectional DC-AC converter [59] Switch OC and SC MMC [60] Switch OC MMC [61] IGBT OC Traction inverters [153] Multiple OC switch fault A back-to-back converter in doubly-fed induction generatorbased wind turbine systems ELM [33] Intern-turn SC Three-phase converter-fed induction motor…”

Intelligent Incipient Fault Detection in Wind Turbines based on Industrial IoT Environment

“…However, when the generator, some frequency components appear in the Fourier spectrum that may make it difficult to identify the normal conditions from the faults. De Oliveira [12] used the FT, based on the frequency spectrum theory of Penman [42], and managed to identify 67% of short circuit failures, in 1.4% of turns, in an electric motor, using a current sensor.…”