Motor Current Signature Research Articles

Stator winding fault prediction of induction motors using multiscale entropy and grey fuzzy optimization methods

In the present work, stator winding fault prediction is studied using a multiscale entropy (MSE) algorithm combined with a grey-based fuzzy algorithm. Experiments were performed with a normal motor and a motor with faulty stator winding. Real time, motor current and vibration signals were acquired at different operating speeds and were used for the diagnosis of faults. The obtained signals were denoised by wavelet transform. Grey relational analysis (GRA) coupled with fuzzy logic was used to model the stator winding fault and to predict the optimal setting for running the induction motor within its parameters range. Analysis of variance (ANOVA) was performed to determine the effect of each individual parameter on the response. The results indicate that the proposed novel approach is very effective in predicting the stator winding fault. Furthermore, the best running parameters for the induction motor are also reported.

A new method of accurate broken rotor bar diagnosis based on modulation signal bispectrum analysis of motor current signals

Motor current signature analysis (MCSA) has been an effective way of monitoring electrical machines for many years. However, inadequate accuracy in diagnosing incipient broken rotor bars (BRB) has motivated many studies into improving this method. In this paper a modulation signal bispectrum (MSB) analysis is applied to motor currents from different broken bar cases and a new MSB based sideband estimator (MSB-SE) and sideband amplitude estimator are introduced for obtaining the amplitude at (1±2s)fs (s is the rotor slip and fs is the fundamental supply frequency) with high accuracy. As the MSB-SE has a good performance of noise suppression, the new estimator produces more accurate results in predicting the number of BRB, compared with conventional power spectrum analysis. Moreover, the paper has also developed an improved model for motor current signals under rotor fault conditions and an effective method to decouple the BRB current which interferes with that of speed oscillations associated with BRB. These provide theoretical supports for the new estimators and clarify the issues in using conventional bispectrum analysis.

Face Milling Tool Wear Condition Monitoring Based on Wavelet Transform and Shannon Entropy

Tool condition monitoring is an important issue in the advanced machining process. Existing methods of tool wear monitoring is hardly suitable for mass production of cutting parameters fluctuation. In this paper, a new method for milling tool wear condition monitoring base on tunable Q-factor wavelet transform and Shannon entropy is presented. Spindle motor current signals were recorded during the face milling process. The wavelet energy entropy of the current signals carries information about the change of energy distribution associated with different tool wear conditions. Experiment results showed that the new method could successfully extract significant signature from the spindle-motor current signals to effectively estimate tool wear condition during face milling.

Experimental Investigation of Motor Current Signal Signature for Developing an Operator Assistance System for a Food Processor

Perturbations in the drawn current due to the fluctuation in load on the blade during the processing of food in a food processor has been used to design an operator assistance system. A robust signal processing scheme has been proposed in order to analyze the extremely feeble current fluctuations in the motor controller circuit. An enveloping technique followed by a wavelet based denoising filter has been proposed for extracting useful information from the acquired current signal. Output of this scheme is fed into an intelligent decision making algorithm, which primarily is able to take two kinds of decisions, finally assists the user in making certain informed judgments. One of these decisions deals with the completion of the mixing or grinding operation whereas the other deals with cautioning the user about the presence of undesirable impurities in the substance being processed. Effectiveness of the developed method has been demonstrated, first on an appropriately formulated simulation, and then on the acquired signal from the experimental setup.

Combined intelligent methods based on wireless sensor networks for condition monitoring and fault diagnosis

This study presents new combined methods based on multiple wireless sensor system for real-time condition monitoring of electric machines. The established experimental setup measures multiple signals such as current and vibration on a common wireless node. The proposed methods are low-cost, intelligent, and non-intrusive. The proposed wireless network based framework is useful for analyzing and monitoring of signals from multiple induction motors. Motor current and vibration signals are simultaneously read from multiple motors through wireless nodes and the faults are estimated using two combined methods. Phase space analysis of vibration data and amplitudes of three phase current signals are used as features in combined intelligent classifiers. Stator related faults are diagnosed by analyzing the magnitudes of read current signals with fuzz logic. The vibration signal taken from the two-axis acceleration meter is normalized and phase space of this signal is constructed. The change in phase spaces are analyzed with machine learning techniques based on Gaussian Mixture Models and Bayesian classification to detect bearing faults. The phase space of vibration signals is constructed by using non-linear time series analysis and Gaussian mixtures are obtained for healthy and each faulty conditions. The constructed mixture models are classified according to their distribution on phase space by using Bayesian classification method. Four motor operating conditions- stator open phase fault, one and two bearing imbalance faults, and healthy condition are considered and related signals are obtained to evaluate the proposed system. The accuracy of the proposed system is confirmed by experimental data.

Improved Sensorless Operation of a CSI-Based Induction Motor Drive: Long Feeder Case

Various applications, like in underground mines and oil and gas industries, require remote operation of vector-controlled medium-voltage variable speed drives via a long motor feeder. The use of voltage source inverters in such cases leads to motor overvoltage and harmonic quality problems. The current source inverter (CSI) is ideally matched to these applications because of its motor-friendly voltage output. Speed sensorless operation is mandatory due to the long motor feeder. Although the model reference adaptive system (MRAS) is a powerful and proven speed estimation tool, its implementation in long motor feeder drives faces many challenges. Among them, and addressed in this paper, are inherent dc offset in its stator model, the need for actual motor voltage and current values, and oscillations in the estimated speed due to errors in the motor current measurement signals. In this paper, a sensorless CSI vector-controlled drive, suitable for long motor feeder applications, is studied. Improved speed estimation is achieved by proposing 1) a modified dc-offset eliminator for an MRAS speed estimation and 2) a compensation technique for motor current's measurement errors. Intensive experimental results, for a low-voltage scaled model, along with simulations validate the effectiveness of the proposed technique.

Utilizing Data from a Sensorless AC Variable Speed Drive for Detecting Mechanical Misalignments

Conventional condition monitoring techniques such as vibration, acoustic, ultrasonic and thermal techniques require additional equipment such as sensors, data acquisition and data processing systems which are expensive and complicated. In the meantime modern sensorless flux vector controlled drives can provide many different data accessible for machine control which has not been explored fully for the purpose of condition monitoring. In this paper polynomial models are employed to describe nonlinear relationships of variables available from such drives and to generate residuals for real time fault detection and performance comparisons. Both transient and steady state system behaviours have been investigated for optimal detection performance. Amongst 27 variables available from the drive, the torque related variables including motor current, Id, Iq currents and torque signals show changes due to mechanical misalignments. So only these variables are explored for developing and optimising detection schemes. Preliminary results obtained based on a motor gearbox system show that the torque feedback signal, in both the steady and transient operation, has the highest detection capability whereas the field current signal shows the least sensitivity to such faults.

Modeling Load Parameters of Ball Mill in Grinding Process Based on Selective Ensemble Multisensor Information

Due to complex dynamic characteristics of the ball mill system, it is difficult to measure load parameters inside the ball mill. It has been noticed that the traditional single-model and ensemble-model based soft sensor approaches demonstrate weak generalization power. Also, mill motor current, feature subsets of the shell vibration and acoustical frequency spectra contain different useful information. To achieve better solutions and overcome these problems mentioned above, a selective ensemble multisource information approach is proposed in this paper. Only the useful feature subsets of vibration and acoustical frequency spectra are portioned and selected. Some modeling techniques, such as fast Fourier transform (FFT), mutual information (MI), kernel partial least square (KPLS), brand and band (BB), and adaptive weighting fusion (AWF), are combined effectively to model the mill load parameters. The simulation is conducted using real data from a laboratory-scale ball mill. The results show that our proposed approach can effectively fusion the shell vibration, acoustical and mill motor current signals with improved model generalization.

A Study of Motor Bearing Fault Diagnosis using Modulation Signal Bispectrum Analysis of Motor Current Signals

Failure of induction motors are a large concern due to its influence over industrial production. Motor current signature analysis (MCSA) is common practice in industry to find motor faults. This paper presents a new approach to detection and diagnosis of motor bearing faults based on induction motor stator current analysis. Tests were performed with three bearing conditions: baseline, outer race fault and inner race fault. Because the signals associated with faults produce small modulations to supply component and high nose levels, a modulation signal bispectrum (MSB) is used in this paper to detect and diagnose different motor bearing defects. The results show that bearing faults can induced a detestable amplitude increases at its characteristic frequencies. MSB peaks show a clear difference at these frequencies whereas conventional power spectrum provides change evidences only at some of the frequencies. This shows that MSB has a better and reliable performance in extract small changes from the faulty bearing for fault detection and diagnosis. In addition, the study also show that current signals from motors with variable frequency drive controller have too much noise and it is unlikely to discriminate the small bearing fault component.

Detection of Rotor Bar Faults in Field Oriented Controlled Induction Motors

In this study, a new method has been presented for the detection of broken rotor bar (BRB) faults in inverter driven induction motors controlled via Field Oriented Control (FOC). To this end, a FOC controlled induction motor with a BRB fault was modeled using the Matlab/Simulink program. Experiments were carried out using the prepared simulation model at various loads and operating speeds. The motor current and speeds were monitored for healthy, 1, 2 and 3 BRB faults. The Resampling Based Order Tracking Analysis (RB-OTA) method was applied to the monitored signals. The obtained results were compared by using the classic Fast Fourier Transform (FFT) method. When the obtained results were analyzed via the FFT method no information regarding any faults was determined in the run up or run down regions of the motor and the presented method gave very good results. The reliability of the proposed method was validated with experimental results. The main innovative part of this study is that the RB-OTA method was implemented on the induction motor current signal for detecting BRB faults.

Modified Indirect Vector Control Technique for Current-Source Induction Motor Drive

Medium-voltage (MV) drives are generally based on either voltage-source inverters or current-source inverters (CSIs). CSIs feature simple topology, motor-friendly waveforms, power reversal capability, and short-circuit-proof protection; hence, they are widely used as high-power MV drives. Direct vector control (DVC) CSI drives ensure improved performance by decoupled control of the machine flux and torque using two independent control loops. Despite the excellent performance of DVC, this scheme faces practical challenges, like dc offset in the stator model and machine parameter dependence. Conventional indirect vector control (IVC) CSI drives are known for reduced computational burden and less machine dependence. However, conventional-IVC CSI drives exhibit poor dynamic response and transient field misorientation due to the absence of a dedicated flux control loop. In this paper, a modified IVC technique is proposed featuring superior decoupling and field orientation using only two extra proportional-integral current controllers with additional motor current feedback signals. The proposed-technique effectiveness is examined experimentally, on a scaled low-voltage prototype, as well as using simulation results.

Feature selection of the armature winding broken coils in synchronous motor using genetic algorithm and mahalanobis distance

The paper describes a procedure for automatic selection of symptoms accompanying the break in the synchronous motor armature winding coils. This procedure, called the feature selection, leads to choosing from a full set of features describing the problem, such a subset that would allow the best distinguishing between healthy and damaged states. As the features the spectra components amplitudes of the motor current signals were used. The full spectra of current signals are considered as the multidimensional feature spaces and their subspaces are tested. Particular subspaces are chosen with the aid of genetic algorithm and their goodness is tested using Mahalanobis distance measure. The algorithm searches for such a subspaces for which this distance is the greatest. The algorithm is very efficient and, as it was confirmed by research, leads to good results. The proposed technique is successfully applied in many other fields of science and technology, including medical diagnostics.

Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency component as that of the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time domain in comparison with conventional FT based methods.

Current envelope analysis for defect identification and diagnosis in induction motors

Increasing demand in reliable manufacturing systems has been accelerating research in condition monitoring and defect diagnosis of vital machine components. This paper investigates defect diagnosis of induction motors, which are widely used in manufacturing systems as a source of actuation. A new approach, based on feature extraction from the envelope of the motor current instead of the motor current itself, has been investigated. This is based on the consideration that motor current envelope is effective in revealing the amplitude-modulated nature of the motor current signal. Three pattern classifiers – Naïve Bayes, k-nearest neighbor, and support vector machine, have been investigated for defect classification. Experimental results have demonstrated that the new feature extraction and selection method yields a higher degree of accuracy than the traditional method for motor defect classification.

The technique of entropy optimization in motor current signature analysis and its application in the fault diagnosis of gear transmission

Nowadays, Motor Current Signature Analysis (MCSA) is widely used in the fault diagnosis and condition monitoring of machine tools. However, although the current signal has lower SNR (Signal Noise Ratio), it is difficult to identify the feature frequencies of machine tools from complex current spectrum that the feature frequencies are often dense and overlapping by traditional signal processing method such as FFT transformation. With the study in the Motor Current Signature Analysis (MCSA), it is found that the entropy is of importance for frequency identification, which is associated with the probability distribution of any random variable. Therefore, it plays an important role in the signal processing. In order to solve the problem that the feature frequencies are difficult to be identified, an entropy optimization technique based on motor current signal is presented in this paper for extracting the typical feature frequencies of machine tools which can effectively suppress the disturbances. Some simulated current signals were made by MATLAB, and a current signal was obtained from a complex gearbox of an iron works made in Luxembourg. In diagnosis the MCSA is combined with entropy optimization. Both simulated and experimental results show that this technique is efficient, accurate and reliable enough to extract the feature frequencies of current signal, which provides a new strategy for the fault diagnosis and the condition monitoring of machine tools.

Electrical Motor Current Signal Analysis using a Modulation Signal Bispectrum for the Fault Diagnosis of a Gearbox Downstream

Motor current signal analysis has been an effective way for many years of monitoring electrical machines themselves. However, little work has been carried out in using this technique for monitoring their downstream equipment because of difficulties in extracting small fault components in the measured current signals. This paper investigates the characteristics of electrical current signals for monitoring the faults from a downstream gearbox using a modulation signal bispectrum (MSB), including phase effects in extracting small modulating components in a noisy measurement. An analytical study is firstly performed to understand amplitude, frequency and phase characteristics of current signals due to faults. It then explores the performance of MSB analysis in detecting weak modulating components in current signals. Experimental study based on a 10kw two stage gearbox, driven by a three phase induction motor, shows that MSB peaks at different rotational frequencies can be based to quantify the severity of gear tooth breakage and the degrees of shaft misalignment. In addition, the type and location of a fault can be recognized based on the frequency at which the change of MSB peak is the highest among different frequencies.

Detection of a static eccentricity fault in a closed loop driven induction motor by using the angular domain order tracking analysis method

In this study, a new method was presented for the detection of a static eccentricity fault in a closed loop operating induction motor driven by inverter. Contrary to the motors supplied by the line, if the speed and load, and therefore the amplitude and frequency, of the current constantly change then this also causes a continuous change in the location of fault harmonics in the frequency spectrum. Angular Domain Order Tracking analysis (AD-OT) is one of the most frequently used fault diagnosis methods in the monitoring of rotating machines and the analysis of dynamic vibration signals. In the presented experimental study, motor phase current and rotor speed were monitored at various speeds and load levels with a healthy and static eccentricity fault in the closed loop driven induction motor with vector control. The AD-OT method was applied to the motor current and the results were compared with the traditional FFT and Fourier Transform based Order Tracking (FT-OT) methods. The experimental results demonstrate that AD-OT method is more efficient than the FFT and FT-OT methods for fault diagnosis, especially while the motor is operating run-up and run-down. Also the AD-OT does not incur any additional cost for the user because in inverter driven systems, current and speed sensor coexist in the system. The main innovative parts of this study are that AD-OT method was implemented on the motor current signal for the first time.

Phase-compensation-based dynamic time warping for fault diagnosis using the motor current signal

Dynamic time warping (DTW) is a time-domain-based method and widely used in various similar recognition and data mining applications. This paper presents a phase-compensation-based DTW to process the motor current signals for detecting and quantifying various faults in a two-stage reciprocating compressor under different operating conditions. DTW is an effective method to align two signals for dissimilarity analysis. However, it has drawbacks such as singularities and high computational demands that limit its application in processing motor current signals for obtaining modulation characteristics accurately in diagnosing compressor faults. Therefore, a phase compensation approach is developed to reduce the singularity effect and a sliding window is designed to improve computing efficiency. Based on the proposed method, the motor current signals measured from the compressor induced with different common faults are analysed for fault diagnosis. Results show that residual signal analysis using the phase-compensation-based DTW allows the fault-related sideband features to be resolved more accurately for obtaining reliable fault detection and diagnosis. It provides an effective and easy approach to the analysis of motor current signals for better diagnosis in the time domain in comparison with conventional Fourier-transform-based methods.

Gear Transmission Fault Diagnosis Based on the Bispectrum Analysis of Induction Motor Current Signatures

Motor current signal analysis has been an effective way for many years to monitor electrical machines. However, little research work has been reported in using this technique for monitoring their downstream equipment. The research investigating the characteristics of electrical current signals measured from an induction motor for monitoring faults from a downstream gear transmission system by a novel modulation signal (MS) bispectrum method. Both analytic analysis and experimental study are conducted based on a motor drive system and have found that the increase of bispectral peaks can be the basis of mechanical fault diagnosis. Particularly, a fault on a gear will causes a larger increase of the bispectral peak at both the gear shaft frequency and accompany with a smaller increase in the relating shaft frequency. However, a shaft misalignment only leads to a bispectrum peak increase at the shaft bifrequency along.

Broken rotor bar fault detection in inverter-fed squirrel cage induction motors using stator current analysis and fuzzy logic

This paper presents the implementation of broken rotor bar fault detection in an inverter-fed induction motor using motor current signal analysis (MCSA) and prognosis with fuzzy logic. Recently, inverter-fed induction motors have become very popular because of their adjustable speed drive. They have been used in many vital control applications such as rolling mills, variable speed compressors, pumps, and fans. The condition monitoring of these motors can significantly reduce the cost of maintenance in the early detection of faults. In this study, MCSA is applied to an inverter-fed induction motor to detect broken rotor bar faults. The diagnosis of a broken rotor bar fault in the squirrel cage induction motor, driven by an inverter, has been studied for stable, full load condition and has been carried out experimentally by analyzing the power spectrum density of the motor stator current. Motor stator currents are uploaded to a PC with the software of the inverter used and the current harmonics are obtained using LabVIEW for every fault condition. After extracting the characteristic frequencies of the broken rotor bar failure, a fuzzy logic algorithm is implemented for classifying the fault. Although there is much research on rotor bar faults for line-connected induction motors, there are no studies on the inverter-fed induction motor and fault diagnosis with fuzzy logic. The implementation results showed that the method is very efficient and useful for prognosis of the rotor faults.