Motor Current Signature Analysis Research Articles

A Review of Condition Monitoring of Permanent Magnet Synchronous Machines: Techniques, Challenges and Future Directions

This paper focuses on the latest advancements in diagnosing faults in Permanent Magnet Synchronous Machines (PMSMs), with particular attention paid to demagnetization, inter-turn short circuits (ITSCs), and eccentricity faults. As PMSMs play an important role in electric vehicles, renewable energy systems and aerospace applications, ensuring their reliability is more important than ever. This work examines widely applied methods like Motor Current Signature Analysis (MCSA) and flux monitoring, alongside more recent approaches such as time-frequency analysis, observer-based techniques and machine learning strategies. These methods are discussed in terms of strengths/weaknesses, challenges and suitability for different operating conditions. The review also highlights the importance of experimental validations to connect theoretical research with real-world applications. By exploring potential synergies between these diagnostic methods, the paper outlines ways to improve fault detection accuracy and machine reliability. It concludes by identifying future research directions, such as developing real-time diagnostics, enhancing predictive maintenance and refining sensor and computational technologies, aiming to make PMSMs more robust and fault-tolerant in demanding environments. In addition, the discussion highlights how partial demagnetization or ITSC faults may propagate if not diagnosed promptly, necessitating scalable and efficient multi-physics approaches. Finally, emphasis is placed on bridging theoretical advancements with industrial-scale implementations to ensure seamless integration into existing machine drive systems.

An Autoregressive-Based Motor Current Signature Analysis Approach for Fault Diagnosis of Electric Motor-Driven Mechanisms.

Maintenance strategies such as condition-based maintenance and predictive maintenance of machines have gained importance in industrial automation firms as key concepts in Industry 4.0. As a result, online condition monitoring of electromechanical systems has become a crucial task in many industrial applications. Motor current signature analysis (MCSA) is an interesting noninvasive alternative to vibration analysis for the condition monitoring and fault diagnosis of mechanical systems driven by electric motors. The MCSA approach is based on the premise that faults in the mechanical load driven by the motor manifest as changes in the motor's current behavior. This paper presents a novel data-driven, MCSA-based CM approach that exploits autoregressive (AR) spectral estimation. A multiresolution analysis of the raw motor currents is first performed using the discrete wavelet transform with Daubechies filters, enabling the separation of noise, disturbances, and variable torque effects from the current signals. AR spectral estimation is then applied to selected wavelet details to extract relevant features for fault diagnosis. In particular, a reference AR power spectral density (PSD) is estimated using data collected under healthy conditions. The AR PSD is then continuously or periodically updated with new data frames and compared to the reference PSD through the Symmetric Itakura-Saito spectral distance (SISSD). The SISSD, which serves as the health indicator, has proven capable of detecting fault occurrences through changes in the AR spectrum. The proposed procedure is tested on real data from two different scenarios: (i) an experimental in-house setup where data are collected during the execution of electric cam motion tasks (imbalance faults are emulated); (ii) the Korea Advanced Institute of Science and Technology testbed, whose data set is publicly available (bearing faults are considered). The results demonstrate the effectiveness of the method in both fault detection and isolation. In particular, the proposed health indicator exhibits strong detection capabilities, as its values under fault conditions exceed those under healthy conditions by one order of magnitude.

Research on the soft measurement of the flow by reactor coolant pumps based on the motor current signature analysis

Research on the soft measurement of the flow by reactor coolant pumps based on the motor current signature analysis

Broken Rotor Bar Fault Detection in Induction Motor Based on Spectral Analysis

Three-phase induction motors are among the most frequently used motors in industrial areas due to their simple structure, low cost, power specifications, etc. Electric energy consumption from these motors accounts for 68% of the energy used by all motors. The faults that occur in these motors over time decrease motor efficiency and result in significant energy consumption. In this study, a new method based on spectral subtraction (SS) was suggested for determining broken rotor bar (BRB) faults in these motors. The traditional method of motor current signature analysis via Fast Fourier Transform (FFT) is hard to use to diagnose BRB faults because the sideband characteristics used as a fault indicator are also seen in the healthy state at low amplitude levels. In the proposed fault detection method, the FFT of both healthy case and faulty case current signals were calculated and then the SS signal is obtained by subtracting the FFT of the healthy motor from the faulty motor for each time step. In the residual SS signal, fault detection was performed by examining the amplitude levels of the harmonic component of the BRB fault. Experimental results indicate that BRB faults can be successfully detected in squirrel-cage rotor induction motors using the suggested method.

Cavitation Detection Method for Pumps Based on Motor Current Signature Analysis

Cavitation Detection Method for Pumps Based on Motor Current Signature Analysis

Development of A Low-Cost Analyzer for Misalignment Identification Based on Vibration and Current Analysis

This paper proposes a low-cost analyzer based on vibration and motor current signature analysis (MCSA) using the Arduino microcontroller. Misalignment identification on induction motors with disc coupling is considered a case study. Several methods for misalignment identification have already been conducted in previous research. However, there are still issues with making the identification more reliable and well-known for further investigation. This paper describes the design and development of an analyzer for misalignment identification that is easy to use and fast utilizing a low-cost Arduino microcontroller. Furthermore, this experimental rig also consists of several additional components such as; an induction motor, shaft, and bearing dan disk coupling. In this paper, misalignment distance variables were set in 0 mm, 0.5 mm, 1 mm, and 1.5 mm, and the motor speed was varied from 500 rpm to 1500 rpm with an increment of 100 rpm. The misalignment characteristic was experimentally achieved using an analyzer with two sensors: an accelerometer for vibration analysis and a current sensor for MCSA. As a result, a low-cost analyzer for misalignment has been successfully developed. The results show that misalignment was explicitly defined by dominant peak frequencies at 3X rpm for vibration analysis and side bands around the main frequency for MCSA. Moreover, side-band frequency increases by increasing the misalignment distance.

Bearing fault diagnosis using combined complete empirical mode decomposition and TKEO methods

This paper proposes a new method for bearing fault detection in induction motors by combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and the Teager-Kaiser Energy Operator (TKEO). The proposed method addresses the challenges of analyzing non-stationary and non-linear signals in motor current signature analysis. CEEMD is employed to decompose the stator current signals into Intrinsic Mode Functions (IMFs), effectively reducing mode mixing issues common in traditional EMD. The TKEO is then applied to extract amplitude and frequency modulations with high temporal resolution, enabling precise identification of fault-specific frequencies. Experimental validation was conducted on a test bench for various bearing fault conditions: inner race, outer race, and ball defects. Results demonstrate that the combination of CEEMD-TKEO successfully detects characteristic fault frequencies with improved accuracy compared to conventional methods. The method identified specific frequency components and their sidebands, correlating with theoretical calculations. Furthermore, correlation coefficient analysis between original signals and their IMFs provided effective mode selection for fault diagnosis. The proposed approach shows promising results for industrial applications, offering both computational efficiency and reliable fault detection capabilities.

Recognition of Impact Load on Connecting-Shaft Rotor System Based on Motor Current Signal Analysis.

Impact loads affect the operational performance and safety life of rolling equipment's connecting-shaft rotor system, even causing faults and accidents. Therefore, recognizing and investigating impact loads is of great significance. Hence, a load recognition method based on motor current information is proposed in this paper to recognize impact loads on the connecting-shaft rotor system. First, the fast Fourier transform is used to obtain the frequency domain information for the motor's current response signal from the rotor system load recognition test. Consequently, the required load response information can be presented more clearly using the singular value decomposition method to remove the power frequency components in the current signal. Then, wavelet packet decomposition is performed on the signal to generate energy analysis feature vectors. A qualitative recognition of the impact load on the system is achieved by learning vector quantization neural networks; the resulting load recognition results are good. These findings indicate that using the motor current as the analysis signal can solve the problem of the difficult layout for traditional vibration sensors in rolling sites. The preprocessing and recognition method of the current response signal can recognize the impact load, confirming the applicability and feasibility of the proposed method.

Bearing fault detection in adjustable speed drives via self-organized operational neural networks

AbstractAdjustable speed drives (ASDs) are widely used in industry for controlling electric motors in applications such as rolling mills, compressors, fans, and pumps. Condition monitoring of ASD-fed induction machines is very critical for preventing failures. Motor current signature analysis offers a non-invasive approach to assess motor condition. Application of conventional convolutional neural networks provides good results in detecting and classifying fault types for utility line-fed motors, but the accuracy drops considerably in the case of ASD-fed motors. This work introduces the use of self-organized operational neural networks to enhance the accuracy of detecting and classifying bearing faults in ASD-fed induction machines. Our approach leverages the nonlinear neurons and self-organizing capabilities of self-organized operational neural networks to better handle the non-stationary nature of ASD operations, providing more reliable fault detection and classification with minimal preprocessing and low complexity, using raw motor current data.

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.

A Critical Analysis of Design for Reduction in Vibrations of Centrifugal Impellers

Centrifugal pumps are widely used for various applications. Numerous technical practices use centrifugal pumps, particularly when consistent and adaptable pump performance is required. Still, these can have several issues, including low efficiency when operated under off-design conditions and poor capitalization performance. These frequent transport mixtures, such as liquid and gas, and pure liquids. It is a well-known fact that several problems encountered by pumps in a pumping station are related to structural instability or impeller/blade failure. Vibrations can cause catastrophic failures in the impeller, so these must be kept to a minimum while designing the impeller. Vibration, cavitation, rough running, lower-than-expected efficiency, and shorter pump life can all be traced back to unfavorable process flow conditions. Although there are some design guidelines for pump configuration, the effects of fluctuating discharge on structural stability have yet to be investigated. An additional pressing problem is the identification of time cracks. Failure can occur due to cracks much before the design loads. In this work, we aim to investigate and analyze the centrifugal impeller design consideration. Using different methods for designing and modeling, we compared significant results and found the optimal solutions in which a centrifugal impellor can be designed. This work enables us to determine the best techniques and methods to overcome the problem of vibrations produced in the centrifugal impeller. This also helps us to understand the centrifugal pump's behavior and performance to improve its efficiency. Centrifugal impellors are compared across conditions, including free, forced, damped, and undamped systems. Three successful methods for designing and modeling a centrifugal impellor are proposed using these parameters. The methods of design and analysis provide predictions of the flow fields that are highly reliable. Regarding fluid distribution and pump efficiency, computational fluid dynamics provides various solutions that may be applied to impeller design. Using fluent software, we can better comprehend the pump's resonant operation. Blade mistuning is a severe problem that can be handled using the blade tip timing and strain gauge technique. Vibration and motor current signature analysis (MCSA) is also mentioned to investigate vibrational problems with the centrifugal impeller. Several strategies are discussed to lessen or eliminate the issue of vibrations in impellers. The limitations and disadvantages of using these techniques are discussed. The summary of results provides significant design and improvement in centrifugal pumps in the recent past.

A non-intrusive technique for estimating the efficiency of low voltage three-phase induction motors

A non-intrusive technique for estimating the efficiency of low voltage three-phase induction motors

Signal enhancement method for gearboxes fault diagnosis in robotic flexible joint

Abstract Motor Current Signal Analysis (MCSA) provides a non-intrusive approach to fault diagnosis. However, the fault impact reacting in the current is reduced due to the presence of flexible structures in the transmission path from the fault source to the motor. Therefore, this paper proposes a method to enhance the frequency domain of the current signal of a single mechanical fault through a transfer model between motor torque and link vibration. First, the joint system dynamics model was developed based on a three-inertia simplified model. The transfer model of motor torque and link vibration was defined based on the system dynamics. The link vibration is then estimated based on the transfer model and electromagnetic torque. Link vibration signal is considered as an enhancement of the torque signal. Finally, the link vibration signature analysis is performed instead of MCSA. The experimental results show that the method is effective in enhancing the features of individual mechanical faults and improving the fault diagnosis performance.

An intelligent fault diagnosis method for rolling bearing using motor stator current signals

Abstract In the diagnosis of rolling bearing faults, the Motor Current Signature Analysis (MCSA) method offers advantages such as low cost, simplicity, and convenience compared to using vibration signals, temperature information, and other diagnostic objects. However, owing to the interference of high-frequency noise, power frequency, and its harmonics in current signals, which can severely affect the accuracy of bearing fault diagnosis, it is extremely challenging to use the original current signals during bearing faults directly for diagnostic purposes. Therefore, this paper proposes an intelligent fault diagnosis method based on the feature reconstruction (FR) method and convolutional neural networks (CNN). This method can achieve high-precision fault diagnosis using single-phase stator current signals from motors as the diagnostic objects. First, the FR method effectively removes the impact of high-frequency noise, supply frequency, and its harmonics from the current signals, while also highlighting subtle fault feature signals to a certain extent. Second, a CNN suitable for learning the characteristics of the current signals was constructed. Through feature extraction, learning, and classification of the current signal samples processed by the FR method, a diagnostic method with a high classification accuracy was obtained. Visualization techniques were used to present the final diagnosis results intuitively. The experimental results demonstrated the highest diagnostic accuracy and average diagnostic accuracy of the proposed method in diagnosing rolling bearing fault types, with an average diagnostic accuracy of approximately 99% for actual faulty bearing samples.

Fault Diagnosis of Induction Machine for Rotor Cage Damage Using MCSA for Industrial Application

The induction machine plays a vital role in industries. Induction machine failure causes significant process delays, higher maintenance expenses, and income loss. The machine’s predictive maintenance becomes crucial to find the primary cause of the defect. This paper focuses on the experimental results of induction machines used in the cement industry. Motor Current Signature Analysis is used to analyze the condition of rotor bars due to distorted input supply causing harmonics. The significance of this test is to determine the primary cause of the unexpected failure. The experimental results of the machine reveal distorted input with harmonics resulting in torque ripples. This might lead to broken rotor bars in the rotor cage with sidebands resulting in poor efficiency of the machine. Motor Current Signature Analysis predicts the lifespan of the machine to avoid unexpected failure. The Finite Element Method is used to design the machine for conditions one, two adjacent, and two non-adjacent broken rotor bars to observe the dynamics of rotor bar currents with numerical results. After a substantial overhaul of the machine, the proposed method is again performed, resulting in improved machine efficiency of 88.83%.

MCSA-based fault diagnosis for rotary parts in servo motion system: Approach and experimental study

Motor current signature analysis (MCSA), as a non-invasive diagnostic method, is robust to environmental noise and of less sensor cost than vibration-based monitoring. Although large amount of progress has been achieved, little attention is paid to the MCSA-based diagnosis task in the Servo motion systems (SMS). An approach using Park vector demodulation, comb filtering and the ensemble empirical mode decomposition (EEMD) is proposed for cyclic fault event detection in this paper. Experiment studies reveal that the MCSA approach could extract the fault signature under extremely low-speed or noisy working condition, which has potential in the scenarios where the accelerometers placement is limited or affected by interferences.

Real-Time Speed Estimation for an Induction Motor: An Automated Tuning of an Extended Kalman Filter Using Voltage-Current Sensors.

This paper aims at achieving real-time optimal speed estimation for an induction motor using the Extended Kalman filter (EKF). Speed estimation is essential for fault diagnosis in Motor Current Signature Analysis (MCSA). The estimation accuracy is obtained by exploring the noise covariance matrices estimation of the EKF algorithm. The noise covariance matrices are determined using a modified subspace model identification approach. In order to reach this goal, this method compares an estimated model of a deterministic system, derived from available input-output datasets (using voltage-current sensors), with the discrete-time state-space representation used in the Kalman filter equations. This comparison leads to the determination of model uncertainties, which are subsequently represented as noise covariance matrices. Based on the fifth-order nonlinear model of the induction motor, the rotor speed is estimated with the optimized EKF algorithm, and the algorithm is tested experimentally.

Cavitation detection via motor current signal analysis for a centrifugal pump in the pumped storage pump station

Cavitation detection via motor current signal analysis for a centrifugal pump in the pumped storage pump station

Research on Local Fault Diagnosis of Rotary Vector Reducer Based on Motor Current Signature Analysis

The rotary vector (RV) reducer, as a highly precise transmission mechanism, can lead to a series of consequential hazards when experiencing local faults. Conducting corresponding research on performance monitoring and fault diagnosis holds significant importance. In order to achieve higher diagnostic accuracy, better classification, prediction effect, and less use of sensors, this paper proposes a local fault diagnosis method of RV reducer based on Motor current signature analysis (MCSA). Firstly, the centre gear local fault electromechanical coupling model is created by combining the working principle of the servo motor, the working characteristics of the RV reducer, and the influencing factors of motor current. Then, according to the actual operating conditions of industrial robots (IRs), the corresponding experimental platform is designed and constructed, and current signals in four different modes are collected. Fast Fourier transform (FFT) is performed on the current signals to obtain frequency domain features, and the correctness of the local fault coupling model of the centre gear is experimentally verified. Finally, the time‐domain statistical features, time–frequency domain features, and CNN features of servo‐feedback current signals under different rotational speeds are extracted and used in the implementation of local fault diagnosis for the RV reducer, respectively. In this paper, it is confirmed that MCSA can be used for localized fault diagnosis of the RV reducer, and combined with a deep learning network, it can effectively predict the fault modes with an average accuracy of more than 96%.

Enhancing bearing fault diagnosis using motor current signals: A novel approach combining time shifting and CausalConvNets

Enhancing bearing fault diagnosis using motor current signals: A novel approach combining time shifting and CausalConvNets