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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. [ABSTRACT FROM AUTHOR]

Maintaining exceptional product quality and boosting processing efficiency requires precise evaluation of various aspects of the turning process, including the cutting depth, feed rate, and size of the workpiece. This article presents a novel approach for observing the turning process state using modulation signal bispectrum (MSB) and motor current signals. A nonlinear model was established that clarifies the load torque oscillations during turning, which in turn affects the amplitude and phase modulation of the motor stator current. Random noise can be efficiently minimized using the MSB algorithm, allowing the extraction of the current-modulation characteristic sideband phase and amplitude from the collected current signal. This technique enables clear representation and enhanced monitoring of load torque changes throughout the turning process. The proposed method was validated via mathematical simulations and universal lathe tests, with the results indicating that the MSB phase and amplitude values effectively capture both dynamic and static torque alterations during the turning operation, making this approach a valuable tool for overseeing the turning process. [ABSTRACT FROM AUTHOR]

Recently, the permanent magnet synchronous motors (PMSMs) are considered to be one of the best options for electrical motor due to their high power density and efficiency for various applications including industrial robot and smart mobility. However, the safety and reliability of the PMSM have not been verified sufficiently when compared to the conventional induction motor. The failure of electric motor can lead to catastrophic failure of entire system, so it is important to detect potential failure modes or signs in advance. In this paper, an accelerated life test was carried out to induce and investigate the failure modes of PMSM and various signals were monitored to detect the types of failure modes during the test. The shaft of the motor was radially loaded to accelerate the failure of PMSM. The phase current, temperature, displacement of the shaft, and vibration were monitored to estimate the health state of the motor. As a result, the bearing and the shaft were the most vulnerable components under radially loaded condition. Also, it is proved that the different failure modes can be successfully detected and classified by monitoring the phase current and vibration signal. [ABSTRACT FROM AUTHOR]

This study introduces an innovative approach to diagnostics, employing a unique combination of techniques including a stratified group K-fold cross-validation method and a sparse stacked autoencoder (SSAE) alongside LightGBM. By examining signatures derived from motor current, voltage, speed, and torque, the framework aims to effectively detect and classify broken rotor bars (BRBs) within inverter-fed induction machines. In this kind of cross-validation method, class labels and grouping factors are spread out across folds by distributing motor operational data attributes equally over target label stratification and extra grouping information. By integrating SSAE and LightGBM, a gradient-boosting framework, we elevate the precision and efficacy of defect diagnosis. The SSAE feature extraction algorithm proves to be particularly effective in identifying small BRB signatures within motor operational data. Our approach relies on comprehensive datasets collected from motor systems operating under diverse loading conditions, ranging from 0% to 100%. Using a sparse stacked autoencoder, the model lowers the dimensionality and noise of the motor fault data. It then sends the cleaned data to the LightGBM network for fault diagnosis. LightGBM leverages the attributes of the sparse stacked autoencoder to showcase the distinctive qualities associated with BRBs. This integration offers the potential to improve defect identification by furnishing input representations that are both more precise and more concise. The proposed model (SSAE with LightGBM) was trained using 80% of the data, while the remaining 20% was used for testing. To validate the proposed architecture, we evaluate the accuracy, precision, recall, and F1-scores of the results using motor global signals, with the help of confusion matrices with receiver operating characteristic (ROC) curves. Following the training of a new LightGBM model with refined hyperparameters through Bayesian optimization, we proceed to conduct the final classification utilizing the optimal feature subset. Evaluation of the test dataset indicates that the BRBs diagnostic framework facilitates the detection and classification of issues with induction motor BRBs, achieving accuracy rates of up to 99% across all loading conditions. [ABSTRACT FROM AUTHOR]

In this article, a novel hybrid model of an induction machine is proposed that can emulate the response of a machine with a faulty bearing. The idea behind developing such a topology is to have the response quite close to that from a real asset while keeping it computationally efficient. The aim is to develop an accurate and efficient model, akin to digital twins, which have the potential for real-time operation. Therefore, the model is divided into two parts. One is a physics-based model that takes fundamental equations and motor construction parameters to yield an intermediate response. All the major parameters are taken into account such that the fundamental component comes quite close to that of the real asset and the bearing fault signature comes in the same order. These signatures are quite small and some small parasitic effects or the assumptions taken while simplifying the model might not impact the fundamental component that much but they alter the signature's amplitude quite significantly. One way is to model all the parasitic effects, which might increase the computation effort significantly. Another way is to take all the parasitic effects altogether and bridge the difference using a statistical approach which is developed using experimental data. Therefore, the current measurements were taken for several bearings with different fault severity. These measurements are processed and quantified such that the net outcome can express the evolution of the signature with increasing fault severity. The same is done for the data generated using the physics-based model. Finally, the difference in the responses is reduced using the neural network such that it can mimic real-world machine behavior closely. The analytical model followed by statistical adjustment overall is considered a hybrid model. The ultimate goal of this methodology is to generate extensive datasets encompassing diverse operating conditions that can be used further to estimate the health of the bearing and possibly be used for training predictive algorithms to estimate bearing RUL in motors. The proposed methodology is developed for the machine operating at 1000 and 1500 RPM and is validated for three different operating speeds.

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.

Induction motor driven pumps are a staple in many sectors of industry, and crucial equipment in naval ships. Such machines can suffer from a wide variety of issues, which may cause it to not perform its function. This can either be due to degradation of components over time, or due to incorrect installation or usage. Unexpected failure of the machine causes downtime and lowers the availability. In some cases, it can even lead to collateral damage. To prevent collateral damage and optimise the availability, many asset owners apply condition monitoring, measuring the dynamic response of the system while in operation. Two high-frequency measurement methods are widely accepted for the detection of faults in rotating machinery at an early stage: vibration measurements, and motor current and voltage measurements. These methods can also distinguish between different failure mechanisms and severities. The dataset described in this article presents experimental data of two centrifugal pumps, driven by induction motors through a variable frequency drive. Besides measurements of behaviour that is considered healthy (new bearings, well aligned), the machines have also been subjected to a variety of (simulated) faults. These faults include bearing defects, loose foot, impeller damage, stator winding short, broken rotor bar, soft foot, misalignment, unbalance, coupling degradation, cavitation and bent shaft. Most faults were implemented at multiple levels of severity for multiple motor speeds. Both vibration measurements, and current and voltage measurements were recorded for all cases. The dataset holds value for a wide range of engineers and researchers working on the development and validation of methods for damage detection, identification and diagnostics. Due to the extensive documentation of the presented data, labelling of the data is close to perfect, which makes the data particularly suitable for developing and training machine learning and other AI algorithms.

Three-phase induction motors (IMs) are considered an essential part of electromechanical systems. Despite the fact that IMs operate efficiently under harsh environments, there are many cases where they indicate deterioration. A crucial type of fault that must be diagnosed early is stator winding faults as a consequence of short circuits. Motor current signature analysis is a promising method for the failure diagnosis of power systems. Wavelets are ideal for both time- and frequency-domain analyses of the electrical current of nonstationary signals. In this paper, the signal data are obtained from simulations of an induction motor for various stator winding fault conditions and one normal operating condition. Our main contribution is the presentation of a fault diagnostic system based on a hybrid discrete wavelet–CNN method. First, the time series of the currents are processed with discrete wavelet analysis. In this way, the harmonic frequencies of the faults are successfully captured, and features can be extracted that comprise valuable information. Next, the features are fed into a convolutional neural network (CNN) model that achieves competitive accuracy and needs significantly reduced training time. The motivations for integrating CNNs into wavelet analysis results for fault diagnosis are as follows: (1) the monitoring is automated, as no human operators are needed to examine the results; (2) deep learning algorithms have the potential to identify even more indistinguishable and complex faults than those that human eyes could.

In rotary-percussion drilling, the impact frequency is a crucial variable that is closely linked to operational factors that determine the efficacy of the drilling process, such as the rate of penetration, bit wear, and rock mass characteristics. Typical identification methods rely on complex simulation models or the analysis of different sensor signals installed on specially adapted setups, which are difficult to be implemented in the field. This paper presents a novel study where the impact frequency is identified by motor current signature analysis (MCSA) applied to an induction motor driving a DTH drilling setup. The analysis of the case study begins with the definition of characteristic drilling stages where the pressure and sound signals allow the detection of an impact frequency of 14.10 Hz, which is then used as a reference to validate three MCSA identification approaches. As a result of the analysis, the envelope approach is the most robust for nearly real-time implementations considering its simplicity and range of coverage. Experimental results provide evidence about the feasibility of the proposed MCSA methods to be integrated into Measurement-While-Drilling (MWD) systems to improve drilling condition monitoring and rock mass characterization. [ABSTRACT FROM AUTHOR]

In the last decade, research centered around the fault diagnosis of rotating machinery using non-contact techniques has been significantly on the rise. For the first time worldwide, innovative techniques for the diagnosis of rotating machinery, based on electrical motors, including generic, nonlinear, higher-order cross-correlations of spectral moduli of the third and fourth order (CCSM3 and CCSM4, respectively), have been comprehensively validated by modeling and experiments. The existing higher-order cross-correlations of complex spectra are not sufficiently effective for the fault diagnosis of rotating machinery. The novel technology CCSM3 was comprehensively experimentally validated for induction motor bearing diagnosis via motor current signals. Experimental results, provided by the validated technology, confirmed high overall probabilities of correct diagnosis for bearings at early stages of damage development. The novel diagnosis technologies were compared with existing diagnosis technologies, based on triple and fourth cross-correlations of the complex spectra. The comprehensive validation and comparison of the novel cross-correlation technologies confirmed an important non-traditional novel outcome: the technologies based on cross-correlations of spectral moduli were more effective for damage diagnosis than the technologies based on cross-correlations of the complex spectra. Experimental and simulation validations confirmed a high probability of correct diagnosis via the CCSM at the early stage of fault development. The average total probability of incorrect diagnosis for the CCSM3 for all experimental results of 8 tested bearings, estimated via 6528 diagnostic features, was 1.475%. The effectiveness gains in the total probability of incorrect diagnosis for the CCSM3 in comparison with the CCCS3 were 26.8 for the experimental validation and 18.9 for the simulation validation. The effectiveness gains in the Fisher criterion for the CCSM3 in comparison with the CCCS3 were 50.7 for the simulation validation and 104.7 for the experimental validation. [ABSTRACT FROM AUTHOR]

Simulation and emulation of electric motor faults are particularly useful to study, test, and evaluate the motor condition monitoring products based on the motor current signature and vibration analysis. Many researchers have proposed and developed several DAQ & computer-based simulation tools. This paper aims to develop a system to simulate and emulate the maximum types of faults which can occur in a three-phase induction motor. In this paper, an embedded system based induction motor fault simulator and the emulator is proposed and developed. The focus of this paper is to model the induction motor faults mathematically based on the motor current signature and vibration frequency spectrum, which are theoretically derived and proved by the early generation researchers and engineers. The novelty of the proposed approach is a cost-effective, portable, and handy tool for field evaluation of the motor condition monitoring products. This paper presents the hardware and software development for making a stand-alone motor faults simulator and emulator. The system employs a powerful high-end and powerful embedded system INTEL ATOM Minnow Board, which is built around E3826, Dual Core, and 1.46 GHz processor. A dedicated 4-channel Direct Digital Synthesizer (DDS) with 12-bit resolution along with software generates all three-phase induction motor faults for both MCSA and vibration analysis. The simulated and emulated faults signals are compared and evaluated against the Standards with the help of a digital storage oscilloscope. [ABSTRACT FROM AUTHOR]

The advent of digitization has brought about new technologies that enable advanced condition monitoring and fault diagnosis under the Industry 4.0 paradigm. While vibration signal analysis is a commonly used method for fault detection in literature, it often involves the use of expensive equipment in difficult-to-reach locations. This paper presents a solution for fault diagnosis of electrical machines by utilizing machine learning techniques on the edge, classifying information coming from motor current signature analysis (MCSA) for broken rotor bar detection. The paper covers the process of feature extraction, classification, and model training and testing for three different machine learning methods using a public dataset to then export the results to diagnose a different machine. An edge computing approach is adopted for the data acquisition, signal processing and model implementation on an affordable platform, the Arduino. This makes it accessible for small and medium-sized companies, albeit with the limitations of a resource-constrained platform. The proposed solution has been tested on electrical machines in the Mining and Industrial Engineering School of Almadén (UCLM) with positive results. [ABSTRACT FROM AUTHOR]

Inter-turn short circuit (ITSC) is a common fault in electrical machines, which may result in further devastation to the whole winding and magnets. In this article, a method for real-time residual insulation capacity monitoring of ITSC fault in permanent magnet synchronous machines (PMSMs) is proposed, which is robust to speed/torque variation and independent of machine parameters and extra sensors. It is based on the theory that the ITSC fault initiates as an insulation deterioration among adjacent turns and the fault severity can be described by the residual insulation capacity being relevant to the 2nd harmonic component in the dq-axis currents. Thus, a short-time Adaline-based harmonic extraction method is proposed for the separation of the needed 2nd harmonic component from time-varying current signals, being used as the fault indicator afterward. Finally, the performance of the proposed method is experimentally evaluated on a PMSM and shows quite good performance in fast-tracking and detection of faults in various stages. [ABSTRACT FROM AUTHOR]

A robot is essential in many industrial and manufacturing facilities due to its efficiency, accuracy, and durability. However, continuous use of the robotic system can result in various component failures. The servo motor is one of the critical components, and its bearing is one of the vulnerable parts, hence failure analysis is required. Some previous prognostics and health management (PHM) methods are very limited in considering the realistic operating conditions of industrial robots based on various operating speeds, loading conditions, and motions, because they consider constant speed data with unloading conditions. This paper implements a PHM for the servo motor of a robotic arm based on variable operating conditions. Principal component analysis-based dimensionality reduction and correlation analysis-based feature selection are compared. Two machine learning algorithms have been used to detect fault features under various operating conditions. This method is proposed as a robust fault-detection model for industrial robots under various operating conditions. Features from different domains not only improved the generalization of the model's performance but also improved the computational efficiency of massive data by reducing the total number of features. The results showed more than 90% accuracy under various operating conditions. As a result, the proposed method shows the possibility of robust failure diagnosis under various operating conditions similar to the actual industrial environment. [ABSTRACT FROM AUTHOR]

Centrifugal pumps are an integral part of many industrial processes and are used extensively in water supply, sewage, heating and cooling systems. While there are several review papers on machine learning-based fault diagnosis on induction motors, its application to centrifugal pumps has received relatively little attention. This work attempts to summarize and review recent research and development in machine learning-based pump condition monitoring and fault diagnosis. The paper starts with a brief explanation of pump operation including common pump faults and the main principles of the motor current signature analysis (MCSA) method. This is followed by a detailed explanation of various machine learning-based methods including the types of detected faults, experimental details and reported accuracies. The performances of different approaches are then presented systematically in a unified table. Finally, the authors discuss practical aspects and challenges related to data collection, storage and real-world implementation.

Due to the wide use of gearmotor systems in industry, many diagnostic techniques have been developed/employed to prevent their failures. An insufficient lubrication of gearboxes of these machines could shorten their life and lead to catastrophic failures and losses, making it important to ensure a required lubrication level. For the first time in worldwide terms, this paper proposed to diagnose a lack of gearbox oil lubrication using motor current signature analysis (MCSA). This study proposed, investigated, and experimentally validated two new technologies to diagnose a lack of lubrication of gear motor systems based on MCSA. Two new diagnostic features were extracted from the current signals of a three-phase induction motor. The effectiveness of the proposed technologies was evaluated for different gear lubrication levels and was compared for three phases of motor current signals and for a case of averaging the proposed diagnostic features over three phases. The results confirmed a high effectiveness of the proposed technologies for diagnosing a lack of oil lubrication in gearmotor systems. Other contributions were as follows: (i) it was shown for the first time in worldwide terms, that the motor current nonlinearity level increases with the reduction of the sgearbox oil level; (ii) novel experimental validations of the proposed two diagnostic technologies via comprehensive experimental trials (iii) novel experimental comparisons of the diagnosis effectiveness of the proposed two diagnostic technologies. [ABSTRACT FROM AUTHOR]

In the industry, continuous health monitoring of electric motors is considered as, an essential requirement. The continuous operation of the electric motor may cause malfunctions and addressing them timely is a critical challenge. The development of an efficient health monitoring system based on the identification of electrical motor faults is in great demand. This paper addresses the fault detection technique using Discrete Wavelet Transform (DWT) algorithm for continuous health monitoring of electric motor-based systems. The faults have been detected through Motor Current Signature Analysis (MCSA) series procedures using the proposed method. Concurrently, the wavelet transform algorithm produces a frequency-based spectrum related to the stator current parameters to accomplish the fault classification. This study provides an analysis of three motor faults i.e., Phase Imbalance, Rotor Misalignment, and High Contact Resistance (HCR). DWT has the ability to categorize the input signals into an approximate coefficient state for low-frequency signals and a detailed coefficient state for high-frequency signals. In this research, this technique is used to detect faults because it is able of processing signals of very low frequency, and effectively deal with intermittent sharp signals that appear frequently during processing. DWT technique is based on conditional monitoring of an induction motor with precisely detailed coefficients and more skilled at light loads given on a motor shaft with relatively fast execution time compared to Fast Fourier Transform (FFT). Furthermore, the comparison of healthy and faulty induction motors has been compiled by the Linear Discriminant Analysis (LDA) technique, a sub-application of MATLAB, and used for faults management purposes. LDA in comparison with Principal Component Analysis (PCA) gives more perfect results. In this research, different faults have been detected with 100% accuracy using the LDA classifier. The implementation of the proposed scheme will be beneficial in avoiding faults by ensuring that preemptive measures are taken timely against these faults, and the production of industries is protected from revenue losses. [ABSTRACT FROM AUTHOR]

For the first time worldwide, innovative techniques, generic non-linear higher-order unnormalized cross-correlations of spectral moduli, for the diagnosis of complex assets, are proposed. The normalization of the proposed techniques is based on the absolute central moments, that have been proposed and widely investigated in mathematical works. The existing higher-order, cross-covariances of complex spectral components are not sufficiently effective. The novel technology is comprehensively experimentally validated for induction motor bearing diagnosis via motor current signals. Experimental results, provided by the proposed technique, confirmed high overall probabilities of correct diagnoses for bearings at early stages of damage development. The proposed diagnosis technology is compared with existing diagnosis technology, based on the triple cross-covariance of complex spectral components. [ABSTRACT FROM AUTHOR]

This paper presents modelling and simulation of the inter-turn short circuit fault in stator winding of a three-phase induction motor. The modeling of the induction motor with the inter-turn short circuit fault was efficiently implemented with developed MATLAB software. The investigation results were obtained from test setup of two 0.37 kW and 2.2 kW, 380/220 V squirrel-cage induction motors. The simulation results of the induction motor model were in good agreement with experimental results in the laboratory. The generated fault signals were used to train an artificial neural network (ANN) for inter-turn short circuit fault detection. From the experimental results, the ANN can detect the faults with up to 96 % accuracy. Based on the proposed model, various applications can be developed for fault monitoring and fault detection of induction motors.

The concept of a digital twin is increasingly appearing in industrial applications, including the field of predictive maintenance. A digital twin is a virtual representation of a physical system containing all data available on site. This paper presents condition monitoring of ventilation systems through the digital twin approach. A literature review regarding the most popular system faults is covered. The motor current signature analysis is used in this research to detect system faults. The physical system is further described. Then, based on the free body diagram concept and Newton's second law, the equations of motion are obtained. Matlab/Simulink software is used to build the digital twin. The Concordia method and the Fast Fourier Transform analysis are used to process the current signal, and physical and numerical system current measurements are obtained and compared. In the final step of the modeling, specific frequencies were adjusted in the twin to achieve the best simulation. In addition, a statistical approach is used to create a complete diagnostic protocol. [ABSTRACT FROM AUTHOR]

Condition Monitoring is essential for the early fault detection and the enhancement of operational efficiency in centrifugal pumps. Motor current signature analysis (MCSA) is a well-established non-intrusive technique for monitoring motors and driven equipment. However, the monitoring results of the MCSA can be affected by both system faults and variations in supply voltage. In this study, a novel model-based voltage and current analysis methodology is proposed for the torque oscillation detection and condition monitoring of centrifugal pumps. The interconnections between the torque oscillation and stator current response of centrifugal pumps are mathematically described, and the hydraulic torque characteristics related to the pump operation conditions are studied through CFD simulations. The CNN-LSTM-attention framework is utilized to describe the relationship between the current signature and the supply voltage variations of a centrifugal pump operating under healthy conditions. Improved detection of torque oscillation related to pump anomalies is achieved using a monitoring indicator, which is generated based on the comparison between the predicted dynamic threshold and the measured spectrum. Off-design operation, cavitation and impeller damage tests were conducted on a single-stage, single-suction centrifugal pump to validate the proposed methodology. The results demonstrate that the proposed methodology effectively detects signatures when the centrifugal pump deviates from its preferred operation range. The proposed methodology offers easy installation and remote monitoring advantages as it only requires non-intrusive voltage and current transducers. Additionally, the proposed methodology separates the disturbances from the supply voltage, thus providing more sensitive and reliable detection results compared to conventional MCSA techniques. [ABSTRACT FROM AUTHOR]

Detecting stator failure is crucial for maintaining reliability in manufacturing processes. The diagnosis in the early stages is challenging, and the industrial environment imposes even more significant difficulties on this task. The voltage unbalances in the power supply are one of the most significant obstacles to correctly identifying stator faults since they cause effects similar to failures. Also, different mechanical torque levels may confuse the diagnosis. This combination of adverse conditions is often neglected in motor health monitoring studies. Therefore, this work develops a new approach for induction motor short-circuit classification. Here, the predictive power, a predictability measure based on relative entropy, is used to extract relevant features from wavelet components. Experiments show that multi-layer perceptron learned better the patterns extracted from the predictive power than root mean square, mainly for incipient faults. The results demonstrated that the predictive power is a reliable stator fault indicator, considering up to 1% of short-circuited turns and a wide range of voltage unbalances and load levels. [ABSTRACT FROM AUTHOR]

In rotary-percussion drilling, the impact frequency is a crucial variable that is closely linked to operational factors that determine the efficacy of the drilling process, such as the rate of penetration, bit wear, and rock mass characteristics. Typical identification methods rely on complex simulation models or the analysis of different sensor signals installed on specially adapted setups, which are difficult to be implemented in the field. This paper presents a novel study where the impact frequency is identified by motor current signature analysis (MCSA) applied to an induction motor driving a DTH drilling setup. The analysis of the case study begins with the definition of characteristic drilling stages where the pressure and sound signals allow the detection of an impact frequency of 14.10 Hz, which is then used as a reference to validate three MCSA identification approaches. As a result of the analysis, the envelope approach is the most robust for nearly real-time implementations considering its simplicity and range of coverage. Experimental results provide evidence about the feasibility of the proposed MCSA methods to be integrated into Measurement-While-Drilling (MWD) systems to improve drilling condition monitoring and rock mass characterization.

In the last decade, research centered around the fault diagnosis of rotating machinery using non-contact techniques has been significantly on the rise. For the first time worldwide, innovative techniques for the diagnosis of rotating machinery, based on electrical motors, including generic, nonlinear, higher-order cross-correlations of spectral moduli of the third and fourth order (CCSM3 and CCSM4, respectively), have been comprehensively validated by modeling and experiments. The existing higher-order cross-correlations of complex spectra are not sufficiently effective for the fault diagnosis of rotating machinery. The novel technology CCSM3 was comprehensively experimentally validated for induction motor bearing diagnosis via motor current signals. Experimental results, provided by the validated technology, confirmed high overall probabilities of correct diagnosis for bearings at early stages of damage development. The novel diagnosis technologies were compared with existing diagnosis technologies, based on triple and fourth cross-correlations of the complex spectra. The comprehensive validation and comparison of the novel cross-correlation technologies confirmed an important non-traditional novel outcome: the technologies based on cross-correlations of spectral moduli were more effective for damage diagnosis than the technologies based on cross-correlations of the complex spectra. Experimental and simulation validations confirmed a high probability of correct diagnosis via the CCSM at the early stage of fault development. The average total probability of incorrect diagnosis for the CCSM3 for all experimental results of 8 tested bearings, estimated via 6528 diagnostic features, was 1.475%. The effectiveness gains in the total probability of incorrect diagnosis for the CCSM3 in comparison with the CCCS3 were 26.8 for the experimental validation and 18.9 for the simulation validation. The effectiveness gains in the Fisher criterion for the CCSM3 in comparison with the CCCS3 were 50.7 for the simulation validation and 104.7 for the experimental validation.

The advent of digitization has brought about new technologies that enable advanced condition monitoring and fault diagnosis under the Industry 4.0 paradigm. While vibration signal analysis is a commonly used method for fault detection in literature, it often involves the use of expensive equipment in difficult-to-reach locations. This paper presents a solution for fault diagnosis of electrical machines by utilizing machine learning techniques on the edge, classifying information coming from motor current signature analysis (MCSA) for broken rotor bar detection. The paper covers the process of feature extraction, classification, and model training and testing for three different machine learning methods using a public dataset to then export the results to diagnose a different machine. An edge computing approach is adopted for the data acquisition, signal processing and model implementation on an affordable platform, the Arduino. This makes it accessible for small and medium-sized companies, albeit with the limitations of a resource-constrained platform. The proposed solution has been tested on electrical machines in the Mining and Industrial Engineering School of Almadén (UCLM) with positive results.

A robot is essential in many industrial and manufacturing facilities due to its efficiency, accuracy, and durability. However, continuous use of the robotic system can result in various component failures. The servo motor is one of the critical components, and its bearing is one of the vulnerable parts, hence failure analysis is required. Some previous prognostics and health management (PHM) methods are very limited in considering the realistic operating conditions of industrial robots based on various operating speeds, loading conditions, and motions, because they consider constant speed data with unloading conditions. This paper implements a PHM for the servo motor of a robotic arm based on variable operating conditions. Principal component analysis-based dimensionality reduction and correlation analysis-based feature selection are compared. Two machine learning algorithms have been used to detect fault features under various operating conditions. This method is proposed as a robust fault-detection model for industrial robots under various operating conditions. Features from different domains not only improved the generalization of the model’s performance but also improved the computational efficiency of massive data by reducing the total number of features. The results showed more than 90% accuracy under various operating conditions. As a result, the proposed method shows the possibility of robust failure diagnosis under various operating conditions similar to the actual industrial environment.

This paper presents modelling and simulation of the inter-turn short circuit fault in stator winding of a three-phase induction motor. The modeling of the induction motor with the inter-turn short circuit fault was efficiently implemented with developed MATLAB software. The investigation results were obtained from test setup of two 0.37 kW and 2.2 kW, 380/220 V squirrel-cage induction motors. The simulation results of the induction motor model were in good agreement with experimental results in the laboratory. The generated fault signals were used to train an artificial neural network (ANN) for inter-turn short circuit fault detection. From the experimental results, the ANN can detect the faults with up to 96 % accuracy. Based on the proposed model, various applications can be developed for fault monitoring and fault detection of induction motors. [ABSTRACT FROM AUTHOR]

An accurate means of non-invasive condition monitoring of the popular industrial drive, three-phase squirrel-cage induction motor, can help to avoid unscheduled maintenance downtime and loss. Faults like air-gap eccentricity can exist even in a newly assembled drive and hence may co-exist with other internal defects. Despite it being a possible situation, the occurrence of simultaneous faults has seldom been studied. Therefore, there is a need for identifying fault signatures of combined fault conditions in a non-invasive manner. This paper presents a detailed model-based study on a three-phase squirrel-cage induction motor with the simultaneous existence of broken rotor-bar and air-gap mixed eccentricity faults using spectral analysis of stator current, instantaneous power, and estimated air-gap torque signals. The modelling of the machine is done using the Multiple Coupled Circuit method and modified to model the presence of the combined fault conditions. A comparative evaluation with various fault conditions and their severity is carried out by spectral analysis, and unique slip-dependent frequency components are identified in the spectra of diagnostic signals. This fault characterization is the most significant contribution of this paper. [ABSTRACT FROM AUTHOR]

Due to the wide use of gearmotor systems in industry, many diagnostic techniques have been developed/employed to prevent their failures. An insufficient lubrication of gearboxes of these machines could shorten their life and lead to catastrophic failures and losses, making it important to ensure a required lubrication level. For the first time in worldwide terms, this paper proposed to diagnose a lack of gearbox oil lubrication using motor current signature analysis (MCSA). This study proposed, investigated, and experimentally validated two new technologies to diagnose a lack of lubrication of gear motor systems based on MCSA. Two new diagnostic features were extracted from the current signals of a three-phase induction motor. The effectiveness of the proposed technologies was evaluated for different gear lubrication levels and was compared for three phases of motor current signals and for a case of averaging the proposed diagnostic features over three phases. The results confirmed a high effectiveness of the proposed technologies for diagnosing a lack of oil lubrication in gearmotor systems. Other contributions were as follows: (i) it was shown for the first time in worldwide terms, that the motor current nonlinearity level increases with the reduction of the sgearbox oil level; (ii) novel experimental validations of the proposed two diagnostic technologies via comprehensive experimental trials (iii) novel experimental comparisons of the diagnosis effectiveness of the proposed two diagnostic technologies.

The concept of a digital twin is increasingly appearing in industrial applications, including the field of predictive maintenance. A digital twin is a virtual representation of a physical system containing all data available on site. This paper presents condition monitoring of ventilation systems through the digital twin approach. A literature review regarding the most popular system faults is covered. The motor current signature analysis is used in this research to detect system faults. The physical system is further described. Then, based on the free body diagram concept and Newton’s second law, the equations of motion are obtained. Matlab/Simulink software is used to build the digital twin. The Concordia method and the Fast Fourier Transform analysis are used to process the current signal, and physical and numerical system current measurements are obtained and compared. In the final step of the modeling, specific frequencies were adjusted in the twin to achieve the best simulation. In addition, a statistical approach is used to create a complete diagnostic protocol.

A centrifugal pump operates under off-design operation conditions would lead to flow instability, additional energy loss or mechanical damage. Early detection is of great importance to its operation efficiency and safety. Sensorless monitoring technology based on motor current signature analysis (MCSA) is a cost-effective and non-intrusive technology to monitor motor-driven devices. However, existing researches on MCSA for centrifugal pump usually realize their monitoring function through complicated time-frequency or energy analysis, they stop short of an indicator that has less calculation cost and definite physical significance compatible with the pump's operation condition. In response to the limitations, this paper aimed at establishing such indicator for centrifugal pump based on MCSA. Theoretical analysis, CFD simulation and experiments were conducted to study the characteristics of pump torque and motor stator current, whose results suggest that the torque fluctuation frequency characteristics would be transmitted to motor stator current by electromagnetic coupling effect, and distribute as sidebands in current spectrum through power frequency modulation. As a result, torque disturbance caused by off-design operation would be reflected as stronger harmonic and noise distortion of motor stator current, and the distortion intensity could be quantified and employed as the operation condition indicator (OCI) for centrifugal pump. Experimental results show that the OCI has its lowest value under the design operation condition, and gradually increases as the pump's operation condition getting worse. Such change law agrees well with the efficiency characteristics of centrifugal pump, which provides a new thought for the operation monitoring of centrifugal pump based on MCSA. The proposed indicator could be directly obtained from the current signal and easily calculated, which is more appropriate for application and probably more preferred for industry compared to the former operation monitoring methods. [ABSTRACT FROM AUTHOR]

In this study, a cost-effective and non-invasive detection strategy is proposed for three-phase line start permanent magnet synchronous motor (LSPMSM) under mixed eccentricity fault using motor current signature analysis. In this respect, theory of air gap magnetic field in eccentric LSPMSM is presented. The detection strategy is examined through modeling and experimental studies. Two-dimensional time stepping finite element method is employed to calculate the motor parameters. Effects of mechanical load and fault severity on eccentric LSPMSM are also investigated. Different fault-related components are scrutinized and the most effective features are identified for this new type of electrical motor. The results indicate that amplitudes of fault-related components at rotor frequency are precise for early detection of mixed eccentricity in LSPMSM. Finally, an efficient frequency pattern as well as detection criterion is specified. [ABSTRACT FROM AUTHOR]

Fault detection in an induction motor, particularly at premature stage has become necessary to avoid unexpected damage in industrial process. In this paper, an approach to detect the early stage faults in induction machine using motor current signature analysis (MCSA) is presented. It is proposed to estimate the fault severity from stator current using noise cancelation by an adaptive filter (Wiener filter). Wavelet De-noising technique is implemented to reduce the effect of noise floor in noise canceled stator current. Different categories of bearing faults, broken rotor fault and stator inter turn faults in induction motor are estimated with and without de-nosing using pre-fault component cancelation (Noise cancelation). In addition, fault index based on standard deviation (SD) and simple square integral (SSI) value of noise canceled stator current are proposed. The proposed fault detection topology is examined using simulations and experiments on a 3HP, 1HP and 0.5HP induction motors for bearing, broken rotor and stator inter turn faults respectively. [ABSTRACT FROM AUTHOR]

Pumps have a wide range of applications. Methods for fault detection of motors are increasingly being used for pumps. In the context of this paper, a test bench is built to investigate circulation pumps for faults. As a use case, the fault of impeller clogging was first measured and then examined with the help of motor current signature analysis. It can be seen that there are four frequencies at which there is an increase in amplitude in case of a fault. The sidebands around the supply frequency are in particular focus. The clogging of three and four of a total of seven channels leads to the highest amplitudes at the fault frequencies. The efficiency is reduced by 9 to 15% in case of faulty operation. These results indicate that the implementation of fault detection algorithms on the pump electronics represents added value for the pump operator. Furthermore, the results can be transferred to other applications. [ABSTRACT FROM AUTHOR]

Induction motors are mainstay power components in industrial equipment. Fault diagnosis technology of induction motors can detect the incipient fault and avoid the unplanned shutdown. The broken rotor bar is a significant fault mode of induction motors. Classical fault diagnosis methods always have complex principles and high computational costs, which leads to difficulties in understanding and calculation. In this paper, a method of broken rotor bar diagnosis based on the sum-of-squares of current signals is proposed. This method can eliminate the fundamental frequency and extract the signature frequency components by calculating the sum-of-squares of three-phase current signals. The signature frequency components are more apparent in the spectrum of the sum-of-squares of current signals. The effectiveness of the proposed method under different load levels and rotation motor speeds has been validated by two experiments. Compared with the classical diagnostic methods, the proposed method has better effectiveness and lower computation cost. [ABSTRACT FROM AUTHOR]

This article proposes and demonstrates a fault-detection method to locate interturn short circuits (ITSCs) in the stator windings of a brushless doubly fed induction machine (BDFIM). The detection of ITSC is important in machine health prognostics as ITSC is an early stage fault that may lead to other faults such as coil-to-coil and coil-to-ground. BDFIM consists of power and control windings in the stator that are magnetically coupled through a nested-loop rotor winding. Existing ITSC detection algorithms use rotor slot harmonics in stator current spectra as fault indicators for only conventional doubly fed induction machines. However, these algorithms cannot be applied to BDFIM due to its different stator/rotor winding structure. This article primarily proposes a new analytical formulation for the nested-loop rotor slot harmonics (NRSHs) as ITSC fault indicators in BDFIMs. Using NRSHs, a detection algorithm is also proposed to detect and locate ITSC in power/control windings of BDFIMs. The proposed algorithm is verified based on numerical analysis of a BDFIM using the finite-element method. The accuracy of the proposed fault-detection algorithm is also experimentally investigated and demonstrated using a BDFIM test rig. Both numerical and experimental test results confirm effectiveness of the proposed ITSC fault-detection algorithm. [ABSTRACT FROM AUTHOR]

Featured Application: The method proposed in our paper can be used in the field of gear fault diagnosis of low-speed and heavy-duty machines. This method uses the stator current signal of the motor for fault diagnosis, and preprocesses the original current to filter the interference of fundamental frequency and the load impact, so as to enhance the gear failure characteristic frequency. The gear failure characteristic frequency can be easily extracted from the preprocessed current. This diagnostic method does not require the installation of sensors and is not affected by environmental factors. Vibration signal is often used in traditional gear fault diagnosis techniques. However, the working face of the scraper conveyor is narrow, harsh and easily explosive, so it is inconvenient to obtain vibration signals by installing sensors. Motor current signature analysis (MCSA) is a fault-diagnosis method without sensor installation, which is easier to realize in the mine. Therefore, a fault diagnosis method for local gear fault, which is based on bispectral analysis (BA) of analytical signal envelope obtained by processing a stator current under time-varying load condition, is proposed in our paper. In this method, the fault frequency component is enhanced by eliminating the interference of fundamental frequency and coal flow impact. Then, the enhanced fault frequency component is extracted by BA, and a quantitative analysis of the fault strength under time-varying load is carried out from the perspective of energy. Finally, the proposed method is verified on the number HB-kpl-75 scraper conveyor reducer, and the results show that this method can successfully diagnose the failure of the scraper conveyor gear under time-varying load conditions. [ABSTRACT FROM AUTHOR]