Motor Current Signature Research Articles

Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors

Trains used in urban mass passenger transit often have side entrance doors through which passengers can rapidly enter and exit the train. These doors are typically electrically powered and automated. Many incidents have occurred in which a passenger is trapped and injured while passing through the doors as they are closing. Existing solutions rely on sensitive-edge sensors and current signal peak detection in the time domain to detect door obstructions. However, these methods have notable limitations: sensors are expensive, and sensor failure can result in safety risks, while time-domain signal analysis is prone to noise, potentially leading to false peak detection. The proposed efficient and cost-effective method enhances safety by implementing the torque control of a DC motor which limits the door closing force to prevent potential injuries. In addition, it reduces reliance on traditional edge sensors, which are prone to failure and may result in undetected obstructions. By using a robust time–frequency domain approach, the system ensures more accurate detection, minimizing potential injury risks. An obstruction of the door causes a corresponding change in the motor current. These changes can be detected by using the discrete wavelet transform to decompose the current signal. The norm and peak of the current are used as obstacle detection features, and appropriate threshold values are obtained from a simulation. The simulation results were validated through an experiment. The proposed novel system effectively detects forces between 100 and 200 N (indicating the presence of an object) within 0.3 s and complies with EN14752 safety standard. It can also differentiate between soft and hard objects trapped in train doors.

Computational intelligence to detect bearing faults using optimal features from motor current signals

In recent times, there has been a notable growth in research investigations into the fault diagnosis of electrical machines. The effective detection of permanent magnet synchronous motor bearing faults is a significant challenge; however, it is crucial for ensuring safety and cost-effectiveness in industries. The data referring to faults needs to be studied under distinct operating conditions, with effective features. Consequently, a meticulous choice of features is required before fault identification. The study aims to find the fewest and most reliable features in the dataset from Paderborn University so that a simple and accurate way can be found to diagnose faults using current signals. The selection of optimal features is initially performed using three algorithms: the equilibrium optimizer, the emperor penguin optimizer (EPO), and the butterfly optimization method. The k nearest neighbour (kNN) and random forest classifiers are used for classification. The results are performed based on the metrics of sensitivity, specificity, accuracy, precision, F1-score, and Matthew's Correlation Coefficient. The results indicate that machine learning models employing different feature selection techniques exhibit superior performance across different feature dimensions. Specifically, the model utilizing the kNN classifier and features selected through the EPO method achieved the maximum accuracy of 100%. The model's efficacy was also compared to the similar work presented in the literature. The efficacy of the optimal features is experimentally confirmed by analyzing current data from squirrel cage induction motors and has shown a high accuracy of 95.2%.

Eccentricity Fault in Induction Motors Using Statistical Process Control Method

Induction motors are the most commonly used electric motors in the industry. The main reasons for choosing induction motors are their robust structure and low maintenance requirements. However, the harsh working conditions of the industry cause motor faults. Predicting motor faults in advance or determining the cause of fault is very important for businesses. In this study, an attempt was made to detect the eccentricity fault of the induction motor with a cheap and easy method. The eccentricity fault, which is a mechanical fault and is frequently encountered, was tried to be determined by monitoring the motor current signals. The motor current signals were analyzed with the statistical process control method from statistical methods. For the first time, with this study, the eccentricity fault occurring in an induction motor operating under different speed conditions was successfully detected with the statistical process control method.

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.

Load recognition of connecting-shaft rotor system under complex working conditions

A method for qualitatively recognizing the load of the rolling equipment's connecting-shaft rotor system is proposed in this paper due to the complexity of rolling production conditions and the limitations of single source response signals. The method is oriented towards fusing the vibration and motor's current information. First, singular value decomposition and wavelet packet analysis are used to preprocess the two types of response signals. Then, the Bayesian estimation method in feature-level fusion achieves qualitative recognition and analysis of rotor system load types. Corresponding load experiments are completed on a load recognition test platform based on vibration and the motor's current signals. The research results show that the load recognition method based on fusion information can recognize the type of load excitation with a recognition accuracy of 91.7 %, higher than other single-source response signal methods. Therefore, the feasibility of the aforementioned theoretical methods is verified.

Fault Diagnosis Method for Marine Electric Propulsion Systems Based on Zero-Crossing Tacholess Order Tracking

In marine electric propulsion systems (MEPS) driven by variable-frequency drives, motor current signals often exhibit complex modulation components, ambiguous spectra, and severe noise interference, rendering it challenging to extract fault-related modulation components. To address this issue, we propose a zero-crossing tacholess order tracking method based on motor current signals. This method utilizes zero-crossing estimation of the instantaneous frequency to perform angular resampling of stator current signals and demodulates the envelope spectrum to extract fault characteristic spectra, enabling the diagnosis of mechanical faults in MEPS. Given the synchronization of the synchronous motor speed with the inverter fundamental frequency, this method estimates instantaneous frequencies in the time domain without requiring integration or time–frequency representation, which is simple and computationally efficient. Data validation on a small-scale marine electric propulsion test platform demonstrates that the proposed method exhibits good robustness under variable-speed conditions and effectively detects imbalance faults caused by propeller breakages and gear faults resulting from bevel gear tooth defects. Therefore, the proposed method can be applied to diagnose faults in downstream mechanical equipment driven by motors.

Multicavitation States Diagnosis of the Vortex Pump Using a Combined DT-CWT-VMD and BO-LW-KNN Based on Motor Current Signals

Multicavitation States Diagnosis of the Vortex Pump Using a Combined DT-CWT-VMD and BO-LW-KNN Based on Motor Current Signals

Tool wear feature extraction in BTA deep hole drilling process based on maximum probability multi-synchrosqueezing transform of spindle current signal

Multi-synchrosqueezing transform (MSST) enhances the concentration of the time–frequency representation (TFR) of time-varying signals by introducing multi-instantaneous frequency (IF) estimation operator. However, the existence of estimation bias may lead to uncertainty in the discrete values of IF during computer computations, causing issues with non-reassigned points. In this paper, a maximum probability multi-synchrosqueezing transform (MPMSST) is proposed, which incorporates distribution probability during the discretization process of the IF, taking the frequency corresponding to the maximum distribution probability as the determined value of IF. Through verification with multi-component time-varying simulation signals, MPMSST not only effectively eliminates non-reassigned points but also accurately estimates the theoretical time–frequency curve. By applying MPMSST to analyze spindle motor current signals during deep hole machining, TFRs under different tool wear states are obtained. The singular value decomposition (SVD) method is utilized to extract the principal component of the TFR, and a tool wear state feature index is constructed based on the sum of squares of the first 10 singular values. The correlation coefficient and covariance between the proposed feature index and the tool wear curve are 0.835 and 5.704 respectively, indicating a good consistency of the index with tool wear changes, and a high sensitivity to variations in tool wear state.

A Self-Supervised Representation Learner for Bearing Fault Diagnosis Based on Motor Current Signals

A Self-Supervised Representation Learner for Bearing Fault Diagnosis Based on Motor Current Signals

A gear fault diagnosis method based on reactive power and semi-supervised learning

Abstract In gearbox gear fault diagnosis based on motor current signals, the gear fault characteristic frequency component is often overshadowed by the fundamental frequency component of the current. In addition, the complex working conditions during actual production and use make it difficult to collect gear operation monitoring data containing labeled feature information. To address the above problems, a semi-supervised learning method based on reactive power signals is proposed for gear fault diagnosis of gearboxes. First, the method utilizes the Hilbert transform to process the current signal of the drive motor in the mechanical system, from which the reactive power is constructed. Then, the reactive power signal is analyzed by spectral analysis as a basis for gear fault diagnosis. Subsequently, the GAF-CNN-MTDL(Gramian angular field—convolutional neural network-mean teacher deep learning) fault diagnosis model is proposed to convert the reactive power signal into a two-dimensional image by using the GAF, and the semi-supervised training method of the average teacher is applied to input the fault dataset into the gear fault diagnosis model which is based on the CNN as the main backbone after the fault dataset has been divided into the labeled and the unlabeled dataset in accordance with a certain ratio. Finally, the gear fault dataset is used for method validation. The experimental outcomes demonstrate the method’s proficiency in effectively emphasizing the fault feature information pertaining to the gear part, and the introduced GAF-CNN-MTDL fault diagnosis model enables the utilization of a minimal number of labeled samples to achieve highly accurate gear fault diagnosis.

Fault diagnosis of axial movement for harmonic drive based on deep belief network by using current data of driving servomotor

PurposeHarmonic drives are used widely in aviation, robotics and instrumentation due to their benefits including high transmission ratio, compact structure and zero backlash. One of the common faults of a harmonic drive is the axial movement of the input shaft. In such a case, its input shaft moves in the axial direction relative to the body of the harmonic drive. The purpose of this study is to propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives.Design/methodology/approachIn the two proposed fault diagnosis methods, the wavelet threshold algorithm is firstly used for filtering noises of the motor current signal. Then, the feature of the denoised current signal is extracted by the empirical mode decomposition (EMD) method and the wavelet packet energy-entropy (WPEE) theory, respectively, obtaining two kinds of feature sets. After a deep learning model based on the deep belief network (DBN) is constructed and trained by using these feature sets, we finally identify the normal harmonic drives and the ones with the axial movement fault.FindingsIn contrast to the traditional back propagation (BP) neural network model and support vector machine (SVM) model, the fault diagnosis methods based on the combination of the EMD (as well as the WPEE) and the DBN model can obtain higher accuracy rates of fault diagnosis for axial movement of harmonic drives, which can be greater than or equal to 97% based on the data of the performed experiment.Originality/valueThe authors propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives, which are verified by the experiment. The presented study may be beneficial for the development of self-diagnosis and self-repair systems of different robots and precision machines using harmonic drives.

Motor fault diagnosis based on multisensor-driven visual information fusion

To need of accurate motor fault diagnosis in industrial system, we propose a fault diagnosis framework that utilizes motor current and electromagnetic signals, combining them with a self-attention-enhanced capsule network for enhanced signal analysis and accuracy. Firstly, the original signal extracted by multiple sensors is constructed into a symmetric point mode (SDP) image, and the visual fault information of different sensors and fusion signals of different motion health states are obtained by the proposed multi-channel image fusion method. Then, the capsule network, combined with self-attention, extracts spatial features from the high-dimensional tensor of the multi-channel fused image for adaptive recognition and extraction. Subsequently, advanced feature vector information is obtained through softmax for diagnosis. Diagnosis results of several datasets indicate that the developed diagnosis framework with compressed image information can availably identify 8 kinds of motor fault states under various loads, and the fault diagnosis rate is as high as 99.95 %, it is helpful for low cost and high-speed diagnosis of motors. In addition, by learning multiple sensor signals in the same state, it obtains stronger robustness and effectiveness than a single signal model.

A Novel Intelligent Fault Diagnosis Method for Bearings with Multi-Source Data and Improved GASA.

In recent years, single-source-data-based deep learning methods have made considerable strides in the field of fault diagnosis. Nevertheless, the extraction of useful information from multi-source data remains a challenge. In this paper, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with a multi-source data convolutional neural network (MSCNN) for the fault diagnosis of rolling bearing. This method aims to identify bearing faults more accurately and make full use of multi-source data. Initially, the bearing vibration signal is transformed into a time-frequency graph using the continuous wavelet transform (CWT) and the signal is integrated with the motor current signal and fed into the network model. Then, a GASA-MSCNN fault diagnosis method is established to better capture the crucial information within the signal and identify various bearing health conditions. Finally, a rolling bearing dataset under different noisy environments is employed to validate the robustness of the proposed model. The experimental results demonstrate that the proposed method is capable of accurately identifying various types of rolling bearing faults, with an accuracy rate reaching up to 98% or higher even in variable noise environments. The experiments reveal that the new method significantly improves fault detection accuracy.

Improved Fault Detection Using Shifting Window Data Augmentation of Induction Motor Current Signals

Deep learning models have demonstrated potential in Condition-Based Monitoring (CBM) for rotating machinery, such as induction motors (IMs). However, their performance is significantly influenced by the size of the training dataset and the way signals are presented to the model. When trained on segmented signals over a fixed period, the model’s accuracy can decline when tested on signals that differ from the training interval or are randomly sampled. Conversely, models utilizing data augmentation techniques exhibit better generalization to unseen conditions. This paper highlights the bias introduced by traditional training methods towards specific periodic waveform sampling and proposes a new method to augment phase current signals during training using a shifting window technique. This approach is considered as a practical approach for motor current augmentation and is shown to enhance classification accuracy and improved generalization when compared to existing techniques.

An integrated gear tooth crack analysis of coupled electromechanical model: A complexity-based approach

This paper presents a novel approach by conducting a complexity-based study of an electromechanical (EM) gearbox system with tooth root cracks, utilizing an analytical model and motor current signals for the first time, in contrast to existing studies that primarily use experimental vibration data for complexity-based analyses. The coupled EM model is developed using the modified Lagrangian approach and by incorporating improved time-varying mesh stiffness and gear crack models. Refined composite multiscale dispersion entropy (RCMDE) is employed to evaluate system complexity at various crack depths. The analysis focuses on residual current signals and uncovers an inverse correlation between RCMDE values and the severity of cracks. To demonstrate the efficacy of the proposed approach, a comparison study is conducted between RCMDE and multiscale dispersion entropy (MDE) on both residual current and vibration signals. The results show that RCMDE provides superior stability and effectiveness compared to MDE. Additionally, it has been found that residual current signals offer more robust insights into system complexity than residual vibration signals. This study contributes valuable insights into the behavior of gearbox systems experiencing crack-induced faults, especially concerning short-time series data, highlighting the advantages of using motor current signals and RCMDE for complexity-based analyses.

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.

Research on Failure Characteristics of Electric Logistics Vehicle Powertrain Gearbox Based on Current Signal

As a core component of the powertrain system of Electric Logistics Vehicles (ELVs), the gearbox is crucial for ensuring the reliability and stability of ELV operations. Traditional fault diagnosis methods for gearboxes primarily rely on the analysis of vibration signals during operation. This paper presents research on diagnosing gear tooth wear faults in ELV powertrains using motor current signals. Firstly, an experimental test platform was constructed based on the structural principle of the powertrain of ELV models. Subsequently, a pure electric light truck powertrain gearbox with tooth wear was tested. Time–frequency domain analysis, amplitude analysis, ANOVA analysis, kurtosis analysis, and zero−crossing points analysis were used to analyze the U−phase current of the motor connected to the gearbox to study the characteristics of the phase current of the drive motor after tooth wear. The results indicate that while the time–frequency domain characteristics of the U−phase currents are not significantly altered by tooth wear faults, the amplitude, variance, and kurtosis of the current increase with the severity of the wear. Conversely, the number of zero−crossing points decreases. These findings provide valuable insights into new methodologies for diagnosing faults in ELV powertrain systems, potentially enhancing the efficiency and effectiveness of troubleshooting processes.

AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors

This study introduces a novel predictive methodology for diagnosing and predicting gear problems in DC motors. Leveraging AdaBoost with weak classifiers and regressors, the diagnostic aspect categorizes the machine’s current operational state by analyzing time–frequency features extracted from motor current signals. AdaBoost classifiers are employed as weak learners to effectively identify fault severity conditions. Meanwhile, the prognostic aspect utilizes AdaBoost regressors, also acting as weak learners trained on the same features, to predict the machine’s future state and estimate its remaining useful life. A key contribution of this approach is its ability to address the challenge of limited historical data for electrical equipment by optimizing AdaBoost parameters with minimal data. Experimental validation is conducted using a dedicated setup to collect comprehensive data. Through illustrative examples using experimental data, the efficacy of this method in identifying malfunctions and precisely forecasting the remaining lifespan of DC motors is demonstrated.

Research on online identification of surface burnishing tool machining conditions by spindle current signal analysis

The surface burnishing tool (SBT) is optimized for aluminum alloys' surface hardness and surface roughness. The SBT's rolling body wears during machining and reaches a certain threshold where the machining performance decreases. Sandpapers are used to abrade the rolling bodies to simulate surface damage. The preconditioned tools are utilized for machining, and the spindle motor current signals are recorded. The SBT's hardening capacity is weakened when the rolling body's Sa exceeds 0.581, and the finishing capacity is weakened when the Sa exceeds 0.684. The PSO-SVM model accurately identifies the failure point of the SBT's hardening capacity with an accuracy of 96.67%. Another PSO-SVM model accurately identified the failure point of SBT's finishing capacity with an accuracy of 85.83%.

An efficient method for bearing fault diagnosis

Statistical features and wavelet based fault detection are attempted to find computationally less complex, low-memory, and power for real-time implementation. The mean absolute value (MAV), simple sign integral (SSI), waveform length (WL), slope sign change, and zero crossing are extracted from the vibration signal, phase current signal-1, and phase current signal-2. The extracted features are combined varyingly to obtain 31 combinations and classified using a decision tree, k-nearest neighbor {k-NN}, and support vector machine. The identified features {MAV, SSI, WL} performed better with vibration and combined current signals, with an average accuracy of 99.8% and 99.5% with the k-NN classifier, respectively. Wavelet has shown an accuracy of 98%, and the Alexnet method obtained an average accuracy of 97.5% using a combined current signal, which is less than the time domain features-based machine learning approach. In addition, simple time-domain features require memory of 9.6 MB times less than wavelets and 4.18MB times less than Alexnet. The time domain-based technique requires a computation time of 30.21 minutes less than Alexnet and 53.54 minutes less than wavelets. Experimentally, the effectiveness of identified minimal features is verified using an induction motor current signal and achieved 100% accuracy with {MAV, SSI, WL}.