Fault Detection In Induction Motors Research Articles

Stator Winding Interturn Short-Circuit Fault Modeling and Detection of Squirrel-Cage Induction Motors

Stator Winding Interturn Short-Circuit Fault Modeling and Detection of Squirrel-Cage Induction Motors

Revolutionizing motor maintenance: a comprehensive survey of state-of-the-art fault detection in three-phase induction motors

Revolutionizing motor maintenance: a comprehensive survey of state-of-the-art fault detection in three-phase induction motors

Fault Diagnosis in Induction Motors through Infrared Thermal Images Using Convolutional Neural Network Feature Extraction

The development of diagnostic systems for rotating machines such as induction motors (IMs) is a task of utmost importance for the industrial sector. Reliable diagnostic systems allow for the accurate detection of different faults. Different methods based on the acquisition of thermal images (TIs) have emerged as diagnosis systems for the detection of IM faults to prevent the further generation of faults. However, these methods are based on artisanal feature selection, so obtaining high accuracy rates is usually challenging. For this reason, in this work, a new system for fault detection in IMs based on convolutional neural networks (CNNs) and thermal images (TIs) is presented. The system is based on the training of a CNN using TIs to select and extract the most salient features of each fault present in the IM. Subsequently, a classifier based on a decision tree (DT) algorithm is trained using the features learned by the CNN to infer the motor conditions. The results of this methodology show an improvement in the accuracy, precision, recall, and F1-score metrics for 11 different conditions.

Comprehensive Survey on Fault Detection and Classification in Three-Phase Induction Motors

Electric motors have revolutionized the way of human living and resulted in the modern lifestyle. These motors often operate in corrosive and dusty places and are exposed to a variety of undesirable conditions and situations that result in the failure of the motor. The faults occurring in Induction Motors (IM) need to be detected at a proper time for avoiding losses and further consequences. A well-designed fault detection scheme not only reduces motor failure but also increases productivity and even sometimes avoids accidents. This paper presents a review of fault detection and classification techniques in three-phase induction motors (TPIM). The main theme of this paper is to revisit the conventional methods for fault detection in TPIM and compare them with recently published methods based on parameters to be sensed, and the type of fault that can be detected, with their advantages and drawbacks. Around a hundred papers are critically reviewed and studied from old and new regimes. Attention is also given to fault detection methods based on artificial intelligence (AI) and machine learning (ML). This paper concludes with brief remarks which will be very useful for new researchers who are willing to research in the domain of fault detection and classification.

FPGA-Microprocessor Based Sensor for Faults Detection in Induction Motors Using Time-Frequency and Machine Learning Methods.

Induction motors (IM) play a fundamental role in the industrial sector because they are robust, efficient, and low-cost machines. Changes in the environment, installation errors, or modifications to working conditions can generate faults in induction motors. The trend on IM fault detection is focused on the design techniques and sensors capable of evaluating multiple faults with various signals using non-invasive analysis. The methodology is based on processing electric current signals by applying the short-time Fourier transform (STFT). Additionally, the computation of the mean and standard deviation of infrared thermograms is proposed as main indicators. The proposed system combines both parameters by means of Support Vector Machine and k-nearest-neighbor classifiers. The development of the diagnostic system was done with digital hardware implementations using a Xilinx PYNQ Z2 card that integrates an FPGA with a microprocessor, thus taking advantage of the acquisition and processing of digital signals and images in hardware. The proposed method has proved to be effective for the classification of healthy (HLT), misalignment (MAMT), unbalance (UNB), damaged bearing (BDF), and broken rotor bar (BRB) faults with an accuracy close to 99%.

Fault Detection and Condition Monitoring in Induction Motors Utilizing Machine Learning Algorithms

Fault Detection and Condition Monitoring in Induction Motors Utilizing Machine Learning Algorithms

Induction Motor Fault Detection and Classification using RCNN and SURF Based Machine Learning Algorithms and Infrared Thermography

Induction Motor Fault Detection and Classification using RCNN and SURF Based Machine Learning Algorithms and Infrared Thermography

Mixed Eccentricity Fault Detection for Induction Motors Based on Time Synchronous Averaging of Vibration Signals

This paper presents a method based on vibration signals to diagnose rotor mixed eccentricity faults in three-phase squirrel cage induction motors (SCIMs). Rotor eccentricity is a typical fault in SCIMs that significantly affects the performance of electrical machines and results in unwanted vibrations. The time synchronous averaging (TSA) signal, which consists of the harmonics components generated by the fault, is obtained via the characteristic frequency of the eccentricity fault. TSA is decomposed into two signals: TSA-regular and TSA-difference, which are respectively similar to approximate and details of wavelet transform. Based on the fault index, which is the maximum FFT value of the TSA-difference signal, the healthy and faulty motors can be distinguished. The proposed approach is evaluated on an experimental test rig equipped with a 1.5 kW SCIM supplied directly from the power grid under various load conditions. The results illustrate the effectiveness of the proposed approach in detecting the rotor mixed eccentricity faults.

IoT-based health monitoring and fault detection of industrial AC induction motor for efficient predictive maintenance

This research paper presents the development and implementation of an integrated condition monitoring and fault detection system for AC induction motors using a combination of sensors, GSM communication, and a cloud-based Internet of Things (IoT) platform. The proposed system aims to enhance industrial motors’ operational reliability and efficiency by providing real-time data monitoring and early fault detection. The key components of the system include temperature, vibration, current, voltage, and speed sensors, which are strategically placed to gather critical motor performance data. These sensors feed data to an Arduino-based control unit responsible for sensor data acquisition and processing. To ensure timely response to anomalies, the system is equipped with an alarm system and GSM alerts, which notify designated personnel in case of abnormal motor behavior. Moreover, the paper incorporates remote monitoring capabilities, enabling users to access motor health data and real-time status from a distance. Historical data is also stored for analysis and comparison through the integration of a cloud-based Blynk-IoT platform. Additionally, the system facilitates RPM control and utilizes relay modules for seamless motor control and protection. The proposed system was tested and validated using Proteus for circuit diagram simulation and Arduino for sensor coding. The results demonstrate its effectiveness in detecting abnormal motor behavior and its potential to prevent catastrophic failures by enabling predictive maintenance. The proposed system successfully detects and displays abnormalities in important parameters like vibration, temperature, speed, three-phase currents, and voltages with 99% accuracy.

Fault Diagnosis of Three-Phase Induction Motor (IM) Using a Hybrid ELSE-RNN Technique

This manuscript proposes a hybrid technique for fault detection and classification in a three-phase induction motor (IM). The proposed hybrid technique combines enhanced ladder spherical evaluation (ELSE) and recurrent neural networks (RNN); hence, it is called the ELSE–RNN method. The main goal of the manuscript is to detect and categorize the faults that occur in the IM. The key objective of the proposed method is to enhance the performance of supercapacitor (SC) storage technology and fuzzy-tuned proportional–integral (PI) supervision over conventional control. The main contribution of this paper is developing an effective fault diagnosis method for three-phase IMs. The presence of stator, rotor, winding, and bearing faults is employed using signal processing techniques, such as the Hilbert transform and SIFT. The proposed ELSE–RNN technique is utilized with the end goal of detecting and classifying faults. Here, the proposed ELSE–RNN technique recognizes motors’ healthy or unhealthy conditions in many situations to distinguish faults for protection. The proposed ELSE–RNN technique reduces the complexity of detecting and classifying faults with a validated system and increases the system's accuracy. The ELSE–RNN technique is implemented in MATLAB, and its performance is compared to existing techniques.

Fault detection and identification in induction motor using weightless neural network.

This paper presents a novel multiclass, multiparameter real-valued weightless neural network-based classifier for fault detection and identification. In contrast to primitive variants of weightless neural nets, the proposed method is capable of multiclass identification with real-valued input features and has improved recognition and generalization capabilities. The major accomplishments of the proposed method include an improved input-to-address mapping strategy that is suitable for address assignment within discriminator units and an effective memory expansion scheme based on similarity metric-based membership value. The developed classifier is utilized for fault detection and identification in single-phase and three-phase induction motors. The sensitivity analysis of the method is investigated for design parameter variation and the fault diagnosis is performed for multiple faults of the motor. The proposed method achieves the highest accuracy of 99.6% and 89.25% for single-phase and three-phase induction motor fault identification, respectively.

Machine Learning-Based Severity Assessment and Incipient Turn-to-Turn Fault Detection in Induction Motors

Machine Learning-Based Severity Assessment and Incipient Turn-to-Turn Fault Detection in Induction Motors

Broken Rotor Bars Detection in Inverter-Fed Induction Motors Under Continuous Switching of Different Speed Modes

Broken rotor bars (BRBs) fault detection of inverter-fed induction motors operating in transient regime is difficult, and even more challenging under continuous switching of constant speed, acceleration, and deceleration modes (CADM). In this condition, the BRBs caused amplitude-modulation of fundamental harmonic can be used as a fault indicator. However, the amplitude and frequency of the fundamental harmonic vary widely in continuous switching of CADM, which causes tremendous difficulty in the fault feature extraction. In this paper, a novel approach called Adaptive Window Short-time Esprit (AWSTE) is developed to overcome the described problem. It can track the fundamental frequency and extract the envelope of fundamental harmonic under continuous switching of CADM. Experimental results demonstrate the validity of the proposed approach at different control modes, different load levels, and different noise levels.

Ball-Bearing Fault Detection of Squirrel-Cage Induction Motors Based on Single-Phase Stator Current Using Wavelet Packet Decomposition and Statistical Features

Ball-Bearing Fault Detection of Squirrel-Cage Induction Motors Based on Single-Phase Stator Current Using Wavelet Packet Decomposition and Statistical Features

A hybrid method based on deep learning and ensemble learning for induction motor fault detection using sound signals

A hybrid method based on deep learning and ensemble learning for induction motor fault detection using sound signals

Automatic Methodology for Multiple Fault Detection in Induction Motor Under Periodic Low-Frequency Fluctuating Load Based on Stray Flux Signals

Automatic Methodology for Multiple Fault Detection in Induction Motor Under Periodic Low-Frequency Fluctuating Load Based on Stray Flux Signals

Stator winding fault detection of induction motors using fast Fourier transform on rotor slot harmonics and least square analysis of the Park's vectors

AbstractAn efficient method for the detection of the stator winding faults (SWFs) of induction motors was dealt based on Park's vector of rotor slot harmonics (RSHs). The induced harmonic voltages of the RSHs in the stator windings are the supreme signatures for fault detection, which can be monitored in the motor currents. Using such a signature, unbalanced supply voltage and stator windings fault situations could be segregated straightforwardly. On the other hand, despite some merits, Park's vector analysis‐based methods suffer from low sensitivity and unreliability in the case of low‐severity faults. To have the advantages of both methods while tackling the shortcomings, Park's vector approach and the RSH signatures are combined in the proposed method. The RSHs are extracted using the fast Fourier transform method and the least squared approach is employed for pattern recognition. Different experimental results under various scenarios, even in the case of low severity faults, confirm that the locus of Park's vector is an elliptical shape in SWFs, while it is shapeless in other cases, such as healthy and unbalanced supply voltage cases.

Feature-Based Bearing Fault Classification Using Taylor–Fourier Transform

This paper proposes a feature-based methodology for early bearing fault detection and classification in induction motors through current signals using the digital Taylor–Fourier transform (DTFT) and statistical methods. The DTFT allows the application of narrow bandwidth digital filters located in the spurious current signal components, wherewith it is possible to gain information to detect bearing issues and classify them using statistical methods. The methodology was implemented in MATLAB using the digital Taylor–Fourier transform for three fault types (bearing ball damage, outer-race damage, and corrosion damage) at different powering conditions: power grid source at 60 Hz and adjustable speed drive applied (60 Hz, 50 Hz, 40 Hz, 30 Hz, 20 Hz, and 10 Hz) in loading and unloading conditions. Results demonstrate a classification accuracy between 93–99% for bearing ball damage, 91–99% for outer-race damage, and 94–99% for corrosion damage.

Induction Motor Stator Winding Inter-Tern Short Circuit Fault Detection Based on Start-Up Current Envelope Energy.

Inter-turn short circuit (ITSC) is a common fault in induction motors. However, it is challenging to detect the early stage of ITSC fault. To address this issue, this paper proposes an ITSC fault detection method for three-phase induction motors based on start-up current envelope energy. This approach uses Akima interpolation to calculate the envelope of the measured start-up current of the induction motor. A Gaussian window weighting is applied to eliminate endpoint effects caused by the initial phase angle, and the enveloping energy is obtained using the energy formula as the fault feature. Finally, by combining this with the support vector machine (SVM) classification learner, fault detection of ITSC in induction motors is achieved. The experimental results show that the average accuracy of this method reaches 96.9%, which can quickly and accurately detect ITSC faults in asynchronous motors and determine the severity of the faults. Furthermore, the average accuracy of SVM in detecting early ITSC faults under no-load conditions is 98.8%, which is higher than other classification learners, including LR, KNN, and NN. This study provides a new idea for induction motor fault detection and can contribute to induction motor maintenance.

Asymmetrical Fault Detection in Induction Motors Through Elimination of Load Torque Oscillations Effects in the Slight Speed Variations and Steady-State Conditions

Recently, motor current signature analysis (MCSA) has been widely considered for fault detections due to its simplicity, availability, and cost-effectiveness. The rotor asymmetry fault (RAF) is the most common fault that occurs in induction machines. It is extremely challenging to detect RAF indices occurred at the vicinity of the supply frequency. Moreover, load torque oscillations (LTOs) <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">which</b> appear as a sinusoidal phase modulation of the supply frequency, exhibit similar indications as the RAF, especially in gearbox-based electromechanical systems. Since LTOs lead to false alarm of RAF, separation of LTOs indices from RAF characteristic components is inevitable. In this paper, normalized frequency domain energy operator (NFDEO) is evaluated for demodulation and separation of RAF and LTOs in steady-state and transient conditions. Moreover, the proposed method is tested in both with-and-without speed control strategies. In this regard, the effects of RAF in the presence of LTOs investigated through both synthetic signals and experimental data in steady-state and transient conditions with slight variation in rotor speed. The obtained results show that NFDEO along with Winger-Ville can effectively detect RAF in the presence of LTOs even with speed control strategy.