Condition Monitoring Of Induction Motors Research Articles

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.

Comparison Between Two Offline Artificial Intelligence Methods for an Efficiency Estimation of In-Service Induction Motors

Various algorithms have been put forth for an efficiency estimation of an in-service induction motor with varying intrusion levels and accuracy. In the field, it is hard to reach the terminals of an in-service motor because of its location. This paper helps a field engineer to choose properly an efficiency estimation method for condition monitoring of an in-service induction motor. In this paper, the author put forth an approach based on symmetrical components to assess the efficiency of induction machine working on unbalanced power supplies at any loading condition. A comparison is done between two offline intelligence techniques like gravitational search (GS) algorithm and cuckoo search (CS) algorithm for estimating the efficiency of an induction machine by using limited measurements. The merit of these techniques is that the motor is not taken out from its place of installation for testing. Also, the highly intrusive no-load test is avoided. Simulations and experimental results support and validate the carried-out research.

Inverter-Fed Motor Drive System: A Systematic Analysis of Condition Monitoring and Practical Diagnostic Techniques

Due to their efficiency and control capabilities, induction motors fed with inverters have become prevalent in various industrial applications. However, ensuring the reliable operation of the motor and diagnosing faults on time are crucial for preventing unexpected failures and minimizing downtime. This paper systematically analyzes condition monitoring and practical diagnostic techniques for inverter-fed motor drive systems. This study encompasses a thorough evaluation of different methods used for condition monitoring and diagnostics of induction motors, with the most crucial faults in their stator, rotor, bearings, eccentricity, shaft currents, and partial discharges. It also includes an assessment of their applicability. The presented analysis includes a focus on the challenges associated with inverter-fed systems, such as high-frequency harmonics, common-mode voltages causing the bearing currents, and high voltage gradients (dv/dt) due to fast switching frequency, which can impact the motor operation, as well as its faults analysis. Furthermore, this research explores the usefulness and efficiency of various available diagnostic methods, such as motor current signature analysis and other useful analyses using advanced signal processing techniques. This study aims to present findings that provide valuable insights for developing comprehensive condition monitoring strategies, and practical diagnostic techniques that enable proactive maintenance, enhanced system performance, and improved operational reliability of inverter-fed motor drive systems.

A transfer learning‐based deep convolutional neural network approach for induction machine multiple faults detection

SummaryThe condition monitoring of squirrel cage induction motors (SCIMs) is vital for uninterrupted production and minimum downtime. Early fault detection can boost output with minimum effort. This article combines the application of transfer learning and convolution neural network (TL‐CNN) for developing an efficient model for bearing and rotor broken bars damage identification in SCIMs. A simple technique for the 1‐D current signal‐to‐image conversion is also proposed to provide input to the proposed deep learning‐based TL‐CNN technique. The proposed approach embodies the advantages of TL and CNN for effective fault identification in SCIMs. The developed technique has classified faults efficiently with an average accuracy of 99.40%. The complete analysis and data collection have been done on the experimental set‐up with a 5 kW SCIM and LabVIEW‐based data acquisition system. The propounded fault detection model has been created in python with the help of packages like Keras and TensorFlow.

A Novel approach using WGAN-GP and Conditional WGAN-GP for Generating Artificial Thermal Images of Induction Motor Faults

This paper proposes a novel approach for generating artificial thermal images for induction motor faults using Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (cWGAN-GP) frameworks. Traditional fault classification methods based on vibration signals often require extensive preprocessing and are more susceptible to noise. In contrast, thermal images offer easier classification and require less preprocessing. However, challenges arise due to the limited availability of thermal images representing different fault conditions and data confidentiality. To overcome these challenges, this paper introduces the utilisation of WGAN-GP and cWGAN-GP with health condition labels to create high-quality thermal images artificially. The results demonstrate that the cWGAN-GP approach is superior in generating thermal images that closely resemble real images of induction motors under various health conditions with a Maximum Mean Discrepancy (MMD) score of 1.023 compared to 1.078 using WGAN-GP. Furthermore, cWGAN-GP requires less training time (7.25 hours to train all health conditions classes) compared to WGAN-GP (12 hours to train the Inner fault class only) using NVIDIA V100. In addition to using EMD and MMD metrics for quantitative analysis of the GAN model, the evaluation process incorporated the expertise of a pre-trained CNN model, namely AlexNet, to assess cWGAN-GP's discriminative capabilities of the generated samples and their alignment with the real thermal images, which resulted in an overall accuracy of 98.41%. Therefore, these proposed approaches offer a promising solution to address the lack of public datasets containing induction motor thermal images representing different health states. By leveraging these models, it will be feasible to enhance induction motor condition monitoring systems and improve the process of fault diagnosis.

A robust principal component analysis-based approach for detection of a stator inter-turn fault in induction motors

Health condition monitoring of induction motors is important because of their vital role and wide us in a variety of industries. A stator inter-turn fault (SITF) is considered to be the most common electrical failure according to statistical studies. In this paper, an algorithm for the detection of an SITF is presented. It is based on one of the blind source separation techniques called principal component analysis (PCA). The proposed algorithm uses PCA to discriminate between the faulty components of motor current signatures and motor voltage signatures from other components. The standard deviation of one of the decomposed vectors is used as a statistical SITF criterion. The proposed criterion is robust to non-fault conditions including voltage quality problems and large mechanical load changes as well as harmonic contaminants in the voltage supply. In addition, with a straightforward and low computational burden in the fault detection process, the proposed method is computationally efficient. To evaluate the performance of the proposed method, large numbers of practical and simulation scenarios are considered, and the results confirm the good performance, high degree of accuracy, and good convergence speed of the proposed method.

A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors

This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of electrical drives in detail, especially the ones that are common in Industry 4.0. After giving an overview on fault statistics, standard methods for the FD and CM of rotating machines are first visited, and then its orientation towards intelligent approaches is discussed. Major diagnostic procedures are addressed in detail together with their advancements to date. In particular, the emphasis is given to motor current signature analysis (MCSA) and digital signal processing techniques (DSPTs) mostly used for feature engineering. Consequently, the statistical procedures and machine learning techniques (stemming from artificial intelligence—AI) are also visited to describe how FD is carried out in various systems. The effectiveness of the amalgamation of the model, signal, and data-based techniques for the FD and CM of inductions motors (IMs) is also highlighted in this review. It is worth mentioning that a variety of neural- and non-neural-based approaches are discussed concerning major faults in rotating machines. Finally, after a thorough survey of the diagnostic techniques based on specific faults for electrical drives, several open problems are identified and discussed. The paper concludes with important recommendations on where to divert the research focus considering the current advancements in the FD and CM of rotating machines.

Condition Monitoring and Fault Diagnosis of Induction Motor using DWT and ANN

Condition Monitoring and Fault Diagnosis of Induction Motor using DWT and ANN

Accurate Detection of Bearing Faults Using Difference Visibility Graph and Bi-Directional Long Short-Term Memory Network Classifier

This article proposes a novel bearing fault detection framework for the real-time condition monitoring of induction motors based on difference visibility graph (DVG) theory. In this regard, the vibration signals of healthy as well as different rolling bearing defects were acquired from both fan-end and drive-end accelerometers. These data were recorded for three different bearing defects and under four loading conditions. The acquired vibration time series were converted to a topological network using DVG. From the transformed vibration data in the graph domain, degree distribution (DD) was selected as feature to discriminate different fault networks. Using analysis of variance test and false discovery rate correction, most discriminative DD features were selected. These features were subsequently fed as inputs to a deep learning model, i.e., a bidirectional long short-term memory network classifier for fault classification. In this study, 112 classification problems have been addressed, and for all of them, the proposed approach delivered very high fault detection accuracy. Finally, the classification performance of the proposed framework is compared with other well-known deep-learning classifiers all of which delivered satisfactory results.

Application of Convolutional Neural Network for Fault Diagnosis of Bearing Scratch of an Induction Motor

The demand for the condition monitoring of induction motors is increasing in various fields, such as industry, transportation, and daily life. Bearing faults are the most common faults, and many fault diagnosis methods have been proposed using artificial pitting as the fault factor in most cases. However, the validity of a fault diagnosis method for other kinds of faults does not seem to be evaluated. Considering onsite scenarios and other possibilities of faults, this paper introduces scratches on the outer raceways of bearings. A study was performed on the detection of several kinds of bearing scratches using a proposed method that was based on an auto-tuning convolutional neural network. The developed approach was also compared with other diagnostic methods for validation. The results showed that the proposed technique provides the possibility of diagnosing several kinds of scratches with acceptable accuracy rates.

Condition monitoring and fault detection of induction motor based on wavelet denoising with ensemble learning

Condition monitoring and fault detection of induction motor based on wavelet denoising with ensemble learning

Mechanical Failures Detection by Vibration Analysis in Rotary Machines Using Wavelet and Artificial Neural Network in the Gera Maranhão Plant.

This article presents aspects of a tool to assist in predictive maintenance based on vibration analysis in rotating machines using wavelet transform and artificial neural networks. The work analyzed the experimental results of applying a methodology based on the combination of the discrete wavelet transform using a Gaussian window with an artificial neural network for condition monitoring of three-phase induction motors. This approach consisted of simulating faulty and flawless signals using software developed in LabVIEW, their processing, appropriate choice of signals, establishing statistical measures of the chosen signs, and forming the input vectors presented to the artificial neural network. The input vectors are constituted based on statistical measures involving measures of central tendency (mean and centroid), measures of dispersion (RMS value and standard deviation), and a measure of asymmetry (Kurtosis). The most promising configuration was the Multiple Perceptron Layer (MPL) network with four hidden layers containing 256 neurons. Such network showed satisfactory performance for both mechanical failures, with a correct range of around 97%. These results proved to be very effective for detecting mechanical failures, thus being an auxiliary instrument in predictive maintenance.

Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques

In this paper, a novel noncontact and nonintrusive framework experimental method is used for the monitoring and the diagnosis of a three phase’s induction motor faults based on an infrared thermography technique (IRT). The basic structure of this work begins with this applying IRT to obtain a thermograph of the considered machine. Then, bag-of-visual-word (BoVW) is used to extract the fault features with Speeded-Up Robust Features (SURF) detector and descriptor from the IRT images. Finally, various faults patterns in the induction motor are automatically identified using an ensemble learning called Extremely Randomized Tree (ERT). The proposed method effectiveness is evaluated based on the experimental IRT images, and the diagnosis results show its capacity and that it can be considered as a powerful diagnostic tool with a high classification accuracy and stability compared to other previously used methods.

Fault Injection Model of Induction Motor for Stator Interturn Fault Diagnosis Research Based on HILS

Recently, in order to ensure the reliability and safety of trains, online condition monitoring and fault diagnosis of traction induction motors have become active issues in the area of rail transportation. The fault diagnosis algorithm can be developed and debugged in a real-time environment based on hardware-in-the-loop simulation (HILS). However, the dynamic space model of induction motors with stator interturn short-circuit faults faces the problem that the faulty state and the healthy state are not compatible, which is inconvenient for the HILS. In this paper, a fault injection model is proposed for the first time, which can realize the online switching between the healthy state and the faulty state of the motor. The feasibility and effectiveness of the proposed model are verified by simulation experiments the based on MATLAB/Simulink and dSPACE HILS platforms.

Stray Flux Analysis for the Detection and Severity Categorization of Rotor Failures in Induction Machines Driven by Soft-Starters

The condition monitoring of induction motors (IM), is an important concern for industry due to the widespread use of these machines. Magnetic Flux Analysis, has been proven to be a reliable method of diagnosing these motors. Among the IM types, squirrel-cage motors (SCIM) are one of the most commonly used. In many industrial applications, the IM are driven by different types of starters, quite often by soft-starters. Despite rotor damages are more prone to occur in line-started motors, these kind of failures have been also reported in those ones driven by soft-starters. Related to this, the use of these type of starters may introduce some harmonic components, that could veil the magnetic flux signature of the different rotor faults. So, the aim of this study is to confirm if the Stray Flux Analysis technique maintains its reliability in these cases. Thus, this article presents the results of soft-started induction motors start-up tests, both in healthy and faulty motors. The fault components are detected by analyzing the stray flux during the starting and the study is complemented by analyzing the stray flux during the steady-state. In addition to the failure patterns, numerical indicators have been found so the identification of the failures is not only qualitative, but also quantitative. The results confirm the potential of the technique for detecting electromechanical failures in soft-started SCIMs.

A review of intelligent methods for condition monitoring and fault diagnosis of stator and rotor faults of induction machines

Nowadays, induction motor (IM) is extensively used in industry, including mechanical and electrical applications. However, three main types of IM faults have been discussed in the literature, bearing, stator, and rotor. Importantly, stator and rotor faults represent approximately 50%. Traditional condition monitoring (CM) and fault diagnosis (FD) methods require a high processing cost and much experience knowledge. To tackle this challenge, artificial intelligent (AI) based CM and FD techniques are extensively developed. However, there have been many review research papers for intelligent CM and FD machine learning methods of rolling elements bearings of IM in the literature. Whereas there is a lack in the literature, and there are not many review papers for both stator and rotor intelligent CM and FD. Thus, the proposed study's main contribution is in reviewing the CM and FD of IM, especially for the stator and the rotor, based on AI methods. The paper also provides discussions on the main challenges and possible future works.

Genetic Algorithm & Fuzzy Logic-based Condition Monitoring of Induction Motor Through Estimated Motor Losses

ABSTRACT In this paper, a novel approach for condition and performance monitoring of induction motor through estimated motor losses is proposed and explained. The novelty in the proposed approach is minimal non-intrusive measurements and does not require disconnection of motors from the load. In many systems, the losses measured directly or indirectly in a motor are of the lump sum in nature. The main advantage of the system prescribed in this paper gives losses in the segregated form, which is directly used to assess the internal condition of the motor. This approach will assess the condition of induction motors for internal faults such as stator faults, rotor faults, core faults, bearings faults, etc. The equivalent circuit parameters and motor losses are estimated using the developed Genetic Algorithm (GA). The estimated motor loss data are compared with the NEMA & IE Standards and if any deviation found will be reported as faults. The reported information is linked to the developed fuzzy algorithm to identify the faults exactly along with its severity level. The proposed approach was tested and evaluated with the help of LT motors ranging from 5 to 200 HP in real-time from a selected industry.

Transient Modeling and Recovery of Non-Stationary Fault Signature for Condition Monitoring of Induction Motors

This paper presents the modeling and the broken rotor bar fault diagnostics by time–frequency analysis of the motor current under an extended startup transient time. The transient current-based nonstationary signal is retrieved and investigated for its time–frequency response to segregate the rotor faults and spatial harmonics. For studying the effect of reduced voltage on various parameters and the theoretical definition of the fault patterns, the winding function analysis (WFA)-based model is presented first. Moreover, an algorithm to improve the spectrum legibility is proposed. It is shown that by efficient utilization of the attenuation filter and consideration of the area containing the maximum power spectral density, the diagnostic algorithm gives promising results. The results are based on the machine’s analytical model and the measurements taken from the laboratory setup.

Design of Smart Device for Induction Motor Condition Monitoring

In industrial field, induction motors are used as a driving tools that requires a relatively constant speed. Power consumption in an induction motor with constant speed can cause a waste of electrical energy. A good performance of an induction motor will determine the quality of production in the industry. Every induction motor must be monitored regularly. This study aims to design a device for detecting the vibrations that occur in an induction motor. Besides, it would be an indicator of the maximum load limit on an induction motor which can later be used as protection on an induction motor. The result showed that the design could be used for providing a diagnosis of the vibration conditions that occur in an induction motor. On the other hand, the design was useful for monitoring and controlling through a simple control circuit designed based on the Internet of Things.

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating machinery (RM) has a vital role in the modern industrial world. However, the remaining useful life (RUL) of machinery is crucial for continuous monitoring and timely maintenance. Moreover, reduced maintenance costs, enhanced safety, efficiency, reliability, and availability are the main important industrial issues to maintain valuable and high-cost machinery. Undoubtedly, induction motor (IM) is considered to be a pivotal component in industrial machines. Recently, acoustic emission (AE) becomes a very accurate and efficient method for fault, leaks and fatigue detection and monitoring techniques. Moreover, CM and FD based on the AE of IM have been growing over recent years. The proposed research study aims to review condition monitoring (CM) and fault diagnosis (FD) studies based on sound and AE for four types of faults: bearings, rotor, stator, and compound. The study also points out the advantages and limitations of using sound and AE analysis in CM and FD. Existing public datasets for AE based analysis for CM and FD of IM are also mentioned. Finally, challenges facing AE based CM and FD for RM, especially for IM, and possible future works are addressed in this study.