Detection Of Faults In Induction Machines Research Articles

A Modified EMD Technique for Broken Rotor Bar Fault Detection in Induction Machines.

Induction machines (IMs) are commonly used in various industrial sectors. It is essential to recognize IM defects at their earliest stage so as to prevent machine performance degradation and improve production quality and safety. This work will focus on IM broken rotor bar (BRB) fault detection, as BRB fault could generate extra heating, vibration, acoustic noise, or even sparks in IMs. In this paper, a modified empirical mode decomposition (EMD) technique, or MEMD, is proposed for BRB fault detection using motor current signature analysis. A smart sensor-based data acquisition (DAQ) system is developed by our research team and is used to collect current signals wirelessly. The MEMD takes several processing steps. Firstly, correlation-based EMD analysis is undertaken to select the most representative intrinsic mode function (IMF). Secondly, an adaptive window function is suggested for spectral operation and analysis to detect the BRB fault. Thirdly, a new reference function is proposed to generate the fault index for fault severity diagnosis analytically. The effectiveness of the proposed MEMD technique is verified experimentally.

Multi-Rate Vibration Signal Analysis for Bearing Fault Detection in Induction Machines Using Supervised Learning Classifiers

Vibration signals carry important information about the health state of a ball bearing and have proven their efficiency in training machine learning models for fault diagnosis. However, the sampling rate and frequency resolution of these acquired signals play a key role in the detection analysis. Industrial organizations often seek cost-effective and qualitative measurements, while reducing sensor resolution to optimize their resource allocation. This paper compares the performance of supervised learning classifiers for the fault detection of bearing faults in induction machines using vibration signals sampled at various frequencies. Three classes of algorithms are tested: linear models, tree-based models, and neural networks. These algorithms are trained and evaluated on vibration data collected experimentally and then downsampled to various intermediate levels of sampling, from 48 kHz to 1 kHz, using a fractional downsampling method. The study highlights the trade-off between fault detection accuracy and sampling frequency. It shows that, depending on the machine learning algorithm used, better training accuracies are not systematically achieved when training with vibration signals sampled at a relatively high frequency.

Interturn Fault Detection in Induction Machines Based on High-Frequency Injection

An interturn short-circuit in the stator windings can lead to the breakdown of electrical machines. In the case of induction machines, several fault detection methods and faulted models have been developed in the recent decades. These models differ mainly in how the leakage inductances of the faulted winding are modeled. This work provides a generalized model for interturn short-circuit faults, using different assumptions for the leakage inductances. The model is validated with experimental results for an exhaustive set of fault parameters, and the leakage inductances influence is analyzed. Moreover, the model is used to analyze the drawbacks of the negative-sequence fundamental current as a traditional fault signature. A high-frequency injection method for converter-fed machines is presented to overcome these limits. The proposed fault signature is the negative-sequence current at the injection frequency and it is evaluated experimentally at different operating conditions. The fault severity and its location are proved to be related to the proposed fault signature.

Classification of Fault Severity in Induction Machine Systems Based on Temporal Convolutions and Recurrent Networks

Detection and severity identification of mechanical and electrical faults by means of noninvasive methods such as electrical signatures of induction machine have attracted much attention in recent years. Since operating conditions of machines and severity of faults in incipient stages influence the amplitude of fault index in the fault detection process, diagnosing fault occurrence and severity can be more complicated. In this study, an efficient method for fault detection and classification in induction machine based on deep neural networks is introduced. The introduced method applies the long short-term memory (LSTM) and fully convolutional neural networks (FCNs) in a conjoined manner. The authors use the FCN architecture for feature extraction from the time-series signal and augment it with LSTM to improve classification performance. This structure has not been previously applied for fault severity detection in induction machine systems. The authors avoid manual feature engineering and, by eliminating the preprocessing phase, directly use time series of electrical signals for fault detection and classifications. The experimental results have been carried out in different fault severities and loads. The analysis of the results and comparison with other deep and classical methods show that the faulty cases can be separated based on severity and load levels with a high accuracy (98.92%), which shows that the adopted architecture is successful in automatically extracting discriminative features from the signal.

Short Circuit and Broken Rotor Faults Severity Discrimination in Induction Machines Using Non-invasive Optical Fiber Technology

Multiple techniques continue to be simultaneously utilized in the condition monitoring and fault detection of electric machines, as there is still no single technique that provides an all-round solution to fault finding in these machines. Having various machine fault-detection techniques is useful in allowing the ability to combine two or more in a manner that will provide a more comprehensive application-dependent condition-monitoring solution; especially, given the increasing role these machines are expected to play in man’s transition to a more sustainable environment, where many more electric machines will be required. This paper presents a novel non-invasive optical fiber using a stray flux technique for the condition monitoring and fault detection of induction machines. A giant magnetostrictive transducer, made of terfenol-D, was bonded onto a fiber Bragg grating, to form a composite FBG-T sensor, which utilizes the machines’ stray flux to determine the internal condition of the machine. Three machine conditions were investigated: healthy, broken rotor, and short circuit inter-turn fault. A tri-axial auto-data-logging flux meter was used to obtain stray magnetic flux measurements, and the numerical results obtained with LabView were analyzed in MATLAB. The optimal positioning and sensitivity of the FBG-T sensor were found to be transverse and 19.3810 pm/μT, respectively. The experimental results showed that the FBG-T sensor accurately distinguished each of the three machine conditions using a different order of magnitude of Bragg wavelength shifts, with the most severe fault reaching wavelength shifts of hundreds of picometres (pm) compared to the healthy and broken rotor conditions, which were in the low-to-mid-hundred and high-hundred picometre (pm) range, respectively. A fast Fourier transform (FFT) analysis, performed on the measured stray flux, revealed that the spectral content of the stray flux affected the magnetostrictive behavior of the magnetic dipoles of the terfenol-D transducer, which translated into strain on the fiber gratings.

Detection of Broken Rotor Bars in a Cage Induction Machine Using DC Injection Braking

In this paper, an effective procedure for broken rotor bar (BRB) fault detection in a three-phase squirrel-cage induction machine (SCIM) is proposed. This approach relies on a motor current signature analysis (MCSA) by observing the specific fault-related current component generated by applying the DC injection braking method. Unlike the traditional MCSA, which is commonly focused on the detection of BRB sidebands around the fundamental current component, the proposed methodology introduces a new BRB feature in the current spectrum which makes it much easier for identification. The distinctive time-frequency evolution pattern of this feature provides the reliable identification of BRBs, even under no-load operating conditions, thus overcoming the major drawback of traditional MCSA-based methods. Fault severity classification is easily performed through the magnitude inspection of the BRB fault-related current component. In addition, the proposed approach does not require high-complexity signal processing algorithms to achieve reliable results. The proposed concept is presented theoretically, assisted by a magnetically coupled multiple circuit model of the SCIM, both with healthy and faulty rotor bars. Finally, the experimental tests validate the proposed methodology and demonstrate its effectiveness and usefulness.

Bearing Fault Detection in ASD-Powered Induction Machine Using MODWT and Image Edge Detection

Today the industry depends on various types of three-phase induction machines, requiring operating at variable speeds to perform more complex processes. Therefore, it is vital to monitor their operation conditions to maintain the optimal efficiency of the processes they perform and avoid significant economic losses. The proposed work presents the design and development of a method for bearing damage detection based on Maximal Overlap Discrete Wavelet Transform and image processing for edge detection. Accuracies achieved with three types of damage exceed 90%. The signals for the test are acquired from seven different operating conditions for each type of damage. Supply comes from a power grid source and an adjustable speed drive. The Maximal Overlap Discrete Wavelet Transform is applied with different filtering levels to the three phases of the stator current, the magnitude of the filtered signals is acquired, a periodic two-dimensional array is generated and further smoothed by a Gaussian filter allowing the observation of patterns at the edges. Finally, the obtained images are scanned with a 2-D mask aiming to detect and count patterns associated to the fault detection process. Statistical analysis is performed over characteristic signatures obtained from the current magnitude of the three phases at different classes of damage and several mechanical load conditions.

Supervised Classification Of induction Motors faults

Currently, the environmental challenges have been considered as a strategic issue for most industrial companies around the world, threatening their sustainability and profit; This leads to taking the environmental dimensions seriously and preserving natural resources well, since they are a key criterion for sustainable development. In this context, this work calls for innovative solution and new technologies to support the development and integration of environmental considerations through the implementation of an automated fault detection and diagnosis system in induction machines in order to minimize downtime, increase machine utilization rate, get an idea of remaining machine life based on artificial intelligence (AI) and the analysis of collected data. Using the Pattern Recognition methods, this system aims to support decision making in terms of defect classification, through the following process: the collection of relevant data about the stator currents of two induction machines, powered by a converter, one healthy and the other defective, through the CompactRIO device, then the analysis of the data, using programs developed under LabVIEW software, and the extraction of the indicators to form a database. Based on analysis results, several intelligent methods by classification algorithms can organize the acquired data in order to automate the diagnostic process. Ultimately, the set-up of an alert system to prevent rather than cure. The outcomes showed that the integration of predictive maintenance could help achieve an energy cost recovery equal to10% of the total costs of an electric motor system. Hence, the premature detection of faults helps to minimize energy expenditure and achieve overall cost savings, which implies energy optimization.

Bearing Fault Detection in Adjustable Speed Drive-Powered Induction Machine by Using Motor Current Signature Analysis and Goodness-of-Fit Tests

Induction machines are widely used in several industries around the world; their robust design allows them to operate even under nonoptimal conditions; the nonoptimal operation can reduce the machine lifetime depending on the anomaly magnitude; this leads to a loss of process efficiency, which eventually generates a considerable operational costs increment. Monitoring methods, that allow an early fault detection, are getting developed currently; these methods are focused on the fault detection of the main components of the machine; one of them is the bearing fault detection that can be obtained through the phase current signal analysis. In this article, three types of goodness-of-fit test are studied; in these methods, the motor current signature and the motor square current signature are analyzed. Furthermore, three types of bearing damage are presented and studied; the damages studied are: single point damage (bearing outer-race damage and bearing ball damage), and distributed damage (corrosion damage). The induction machine signals, when working with the damages mentioned before, are measured at two powering conditions: power grid sourced (at 60 Hz constant frequency), and adjustable speed drive (at six operating frequencies).

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Open-switch fault detection and isolation of DC-AC static inverter feeding induction machine

In the aim to ensure system reliability and monitoring of latent faults that decrease the installation's life and damage them in the short-term, this paper presents an online open-circuit switch fault diagnosis of a DC-AC inverter. The fault detection and isolation (FDI) employed method is articulated on two techniques based practically on stator currents analysis. For the first technique, the developed decision algorithm is based on d-q Park's vector components. In the second technique, fault diagnostic is achieved using normalised quantities of residues from the currents phases in ABC frame. When the open circuit switch fault occurs, the residual current vector between the stator currents with and without fault is calculated. After that, this vector will be evaluated by a decision algorithm. The drive chain modelling (power inverter-machine) and the monitoring algorithms are developed and established under the MATLAB/Simulink environment, where the results are presented and analysed. The feasibility and the effectiveness of the proposed techniques have been validated by simulation under different operating conditions.

Open-switch fault detection and isolation of DC-AC static inverter feeding induction machine

In the aim to ensure system reliability and monitoring of latent faults that decrease the installation's life and damage them in the short-term, this paper presents an online open-circuit switch fault diagnosis of a DC-AC inverter. The fault detection and isolation (FDI) employed method is articulated on two techniques based practically on stator currents analysis. For the first technique, the developed decision algorithm is based on d-q Park's vector components. In the second technique, fault diagnostic is achieved using normalised quantities of residues from the currents phases in ABC frame. When the open circuit switch fault occurs, the residual current vector between the stator currents with and without fault is calculated. After that, this vector will be evaluated by a decision algorithm. The drive chain modelling (power inverter-machine) and the monitoring algorithms are developed and established under the MATLAB/Simulink environment, where the results are presented and analysed. The feasibility and the effectiveness of the proposed techniques have been validated by simulation under different operating conditions.

Induction Motor Fault Diagnosis Based on Zero-Sequence Current Analysis

This paper presents some considerations regarding the application of the stator zero-sequence current component (ZSC) in the fault detection of cage induction machines, including the effects of magnetic core saturation. Faults such as rotor cage asymmetry and static, dynamic, and mixed eccentricity were considered. The research started by developing a harmonic motor model, which allowed us to obtain a voltage equation for the zero-sequence current component. The equation allowed us to extract formulas of typical frequencies for particular fault types. Next, in order to verify the effectiveness of ZSC in induction motor fault diagnosis, finite element calculations and laboratory tests were carried out for the previously mentioned faults for delta and wye connections with neutral wire stator winding configurations.

Stator and rotor turn‐to‐turn fault detection in wound rotor induction machines based on the air‐gap magnetic field distortion

An efficient online flux-based diagnostic approach is proposed to detect turn-to-turn faults in the stator and rotor windings of wound rotor induction machines, at an early stage of development, which can be applied in the online mode. Some flux sensors are installed in the stator slots to detect the rotational magnetic field at various positions along the air-gap circumference. These sensors measure the flux linked to all winding coil groups, in the three phases. Any fault occurring either in the stator or rotor windings will disturb the air-gap flux and create an asymmetry in the rotational magnetic field, which is detectable through the difference of the induced voltages in the corresponding flux sensors. The proposed diagnostic technique is evaluated by finite element analysis as well as by multiple experimental tests. The obtained results show that such invasive technique is able to detect and discriminate various faults in the stator and rotor windings with high accuracy, low calculation burden, proper sensitivity and appropriate robustness in the face of machine load level variations.

Induction Machine Fault Detection and Classification Using Non-Parametric, Statistical-Frequency Features and Shallow Neural Networks

This article presents a two-stage fault detection and classification scheme specifically designed for rotating electrical machines. The approach involves the use of new condition indicators that are specific to the frequency domain. The paper proposes two distinct features: one based on the extraction of peaks by using the prominence measure, a technique originating from the topology of mountains, and other based on the calculation of the occupied band power ratio for specific characteristic fault frequencies. A linear based feature reduction technique, the principal component analysis (PCA) has been employed to represent all the data. Afterwards, shallow neural networks have been used to detect and classify the three-phase current signals online. The effectiveness of the proposed scheme has been validated experimentally by using signals obtained with grid and inverter fed induction motors.

Rotor/Stator Internal Phase to Phase Fault Detection in Three-Phase Wound Rotor Induction Machines

This paper presents a novel method based on Magnetic Equivalent Circuit (MEC) to model the wound rotor three-phase induction machine under healthy and faulty conditions. Different parameters of machine such as the number of poles, number of slots, winding configuration and dimension can be selected based on designed properties of a given machine. Internal phase to phase fault is also detected by stator current signature analysis in dq frame. Saturation effect is modeled by a tunable function as well as the core nonlinearity characteristic is considered. Finally, Finite Element Method (FEM) is used to evaluate the accuracy of the proposed MEC model. Short computational time of MEC method shows that this model is suitable for electrical machine modeling and analysis.

A Symmetrical Component Feature Extraction Method for Fault Detection in Induction Machines

Induction motors (IMs) are among the fully developed electromechanical technologies that are still in use today. Over the course of the last century, their structure, control, and operation have been undergone through several stages of development. Among stages of development, the automated control and continuous monitoring of IMs has benefited from the emergence of modern artificial intelligence (AI) methods. IM automation schemes have demonstrated the ability to provide machine fault detection and diagnosis (FDD) function. AI-based FDD methods in IMs have employed frequency-domain, time-frequency, and time-domain analyses as the basis of their feature extraction schemes. A promising feature extraction scheme is one that uses symmetrical components (SCs) in time-domain FDD systems. Current SC-based approaches, however, are limited in their generalizability to different fault classes, may require detailed machine models, and can suffer from computational limitations. In this paper, an improved feature extraction method that uses SCs for a pattern recognition based FDD scheme for three-phase (3φ) IMs will be presented. This novel feature extraction method will be implemented and verified experimentally to demonstrate high classification performance, increased generalizability, and low computational cost.

Permanent magnet synchronous machine stator windings fault detection by Hilbert–Huang transform

The Hilbert–Huang transform (HHT) is a time-frequency signal analysis method based on empirical mode decomposition and the Hilbert transform. It is well suited for reliable fault detection since it is unaffected by transient conditions which might cause false alarms. The method has been demonstrated in recent years for bearing fault detection of induction machines (IM). This study explores the possibility of applying the technique to the detection of stator short-circuit faults in permanent magnet synchronous machine (PMSM). A method based on the online statistical analysis of the instantaneous frequency calculated by the HHT is proposed and demonstrated through real-time hardware-in-the-loop simulation and experimental results.

Induction machine fault detection using smartphone recorded audible noise

This study presents induction machine fault detection possibilities using smartphone recorded audible noise. Acoustic and audible noise analysis for fault detection is a well-established technique; however, specialised equipment for diagnostic purposes is often very expensive and difficult to operate. To overcome this obstacle, a simple pre-diagnostic procedure, using hand-held smartphones is proposed. Different faults of the three-phase squirrel cage induction machine such as various numbers of broken rotor bars and dynamic rotor eccentricity are inflicted to the machine and the resulting audible signals are recorded in laboratory circumstances using two widely available commercial smartphones. The analysis is performed on audible noise and compared with the results of mechanical vibrations measurements, recorded by vibration sensors. Rotational speed frequency and twice-line frequency are used as diagnostic indicators of faults. A simple neural network is composed and probabilities of fault detection using such diagnostic measures are presented. The necessity for further study as well as further implementation and method refinement necessity is pointed out.

Bearing Fault Detection in Induction Machine Based on Stator Current Spectrum Monitoring

Bearing Fault Detection in Induction Machine Based on Stator Current Spectrum Monitoring