Motor Fault Research Articles

Intraperitoneal injection of mesenchymal stem cells-conditioned media (MSCS-CM) treated monocyte can potentially alleviate motor defects in experimental autoimmune encephalomyelitis female mice; An original experimental study

IntroductionMultiple sclerosis (MS), as a destructive pathology of myelin in central nervous system (CNS), causes physical and mental complications. Experimental autoimmune encephalomyelitis (EAE) is laboratory model of MS widely used for CNS-associated inflammatory researches. Cell therapy using macrophage M2 (MPM2) is a cell type with anti-inflammatory characteristics for all inflammatory-based neuropathies. This experimental study investigated the probable therapeutic anti-inflammatory effects of intraperitoneal (IP) injection of MPM2 on alleviation of motor defect in EAE-affected animals. Materials and methods24 C57/BL6 female mice were divided into four groups of EAE, EAE + Dexa, EAE + PBS, and EAE + MP2. EAE was induced through deep cervical injection of spinal homogenate of guinea pigs. MPM2 cells were harvested from bone marrow and injected (106cells/ml) in three days of 10, 13 and 16 post-immunizations (p.i). Clinical score (CS), anti-inflammatory cytokines (IL-4, IL-10), pro-inflammatory gene expression (TNF-α, IL-1β) and histopathological investigations (HE, Nissl and Luxol Fast Blue) were considered. Data were analyzed using SPSS software (v.19) and p < 0.05 was considered significant level. ResultsDuring EAE induction, the mean animal weight was decreased (p < 0.05); besides, following MPM2 injection, the weight gain was applied (p < 0.05) in EAE + MPM2 groups than control. Increased (p < 0.05) levels of CS was found during EAE induction in days 17–28 in EAE animals; besides, CS was decreased (p < 0.05) in EAE + MPM2 group than EAE animals. Also, in days 25–28 of experiment, the CS was decreased (p < 0.05) in EAE + MPM2 than EAE + Dexa. Histopathological assessments revealed low density of cell nuclei in corpus callosum, microscopically. LFB staining also showed considerable decrease in white matter density of corpus callosum in EAE group. Acceleration of white matter density was found in EAE + MPM2 group following cell therapy procedure. Genes expression of TNF-α, IL-1β along with IL-4 and IL-10 were decreased (p < 0.05) in EAE + MPM2 group. ConclusionIP injection of MPM2 to EAE-affected female mice can potentially reduce the CNS inflammation, neuronal death and myelin destruction. MPM2 cell therapy can improve animal motor defects.

Analysis of the influence of winding phase shift on the comprehensive performance of multi-mode operation characteristics of dual-winding permanent magnet synchronous motor

Abstract The objective of this study is to investigate the electromagnetic properties of dual-winding permanent magnet synchronous motors with various winding phase shift angles, both under normal and fault-tolerant operating conditions. Initially, an analysis of the phase shift angle for the dual-winding motor windings is conducted, followed by the design of three distinct configurations with different phase shift angles for a 12-slot 10-pole motor. Subsequently, in the second section, a finite element model is developed to represent both healthy and faulty states of the 12-slot 10-pole dual-winding permanent magnet synchronous motor with varying phase shift angles. The third part of the study involves comparing and analysing the electromagnetic characteristics of the motor under different winding phase shift angles in healthy conditions. The fourth section delves into the comparison and analysis of the electromagnetic properties of motor faults across different winding phase shift angles. Finally, conclusions are drawn based on the analysis and summary of the electromagnetic characteristics of motor faults and health conditions across various winding phase shift angles.

Anti-apoptotic and anti-inflammatory protective mechanisms of Gmelina arborea stem bark extract on ischemic reperfusion injury in albino Wistar rats

Abstract Ethnopharmacological relevance Gemlina arborea Roxb. ex.’s entire plant is utilized in medicine to treat several diseases. Objective and design Cerebral infarction occurs through multiple mechanisms, and herbs are natural multi-component with numerous protective mechanisms. This research investigates the neuroprotective effect of Gemlina arborea stem bark extract in preventing cerebral infarction. Methodology Male albino Wistar rats were inducted with ischemic injury using the bilateral common carotid artery occlusion method. Neurological effects were examined by motor defects, locomotor activity, and forced swim test, while biochemical activities of the brain oxidative stress biomarkers studied include; (lipid peroxidation, superoxide dismutase, catalase levels, glutathione levels, total calcium levels, and sodium-potassium-ATPase) and histopathological examination. Western blotting analysis was used to study the anti-apoptotic and anti-inflammatory mechanisms. Results The elevated oxidative stress biomarkers in the treated group showed extract-inhibited cerebral infarction. Histological examination shows inhibited neutrophil migration and cell damage. The western blot assay depicts reductions in the inflammatory indicators (p38 MAPK, TNF- α) and attenuation of apoptotic mediators (Bcl2/Bax ratio), down-regulation of caspase-3 expression, and a significantly raised anti-inflammatory mediators (IL-10 and actin) in a dose-dependent manner, suggested potential mechanisms by which the extract prevented ischemic reperfusion injury. Conclusion Overall, the estimation of inflammatory and apoptotic mediators revealed the involvement of the anti-inflammatory mechanisms of Gmelina arborea stem bark extract in preventing cerebral infarction.

Application of Salp Swarm Algorithm and Extended Repository Feature Selection Method in Bearing Fault Diagnosis

Motor fault diagnosis is an important task in the operational monitoring of electrical machines in manufacturing. This study proposes an effective bearing fault diagnosis model for electrical machinery based on machine learning techniques. The proposed model is a combination of three processes: feature extraction of signals collected from the motor based on multi-resolution analysis, fast Fourier transform, and envelope analysis. Next, redundant or irrelevant features are removed using the feature selection technique. A binary salps swarm algorithm combined with an extended repository is the proposed method to remove unnecessary features. As a result, an optimal feature subset is obtained to improve the performance of the classification model. Finally, two classifiers, k-nearest neighbor and support vector machine, are used to classify the fault of the electric motor. There are four input datasets used to evaluate the model performance, and UCI is the benchmark dataset to verify the effectiveness of the proposed feature selection technique. The remaining three datasets include the bearing dataset collected from experiments, with an average classification accuracy of 99.9%, as well as Case Western Reserve University (CWRU) and Machinery Failure Prevention Technology (MFPT), which are public datasets with average classification accuracies of 99.6% and 98.98%, respectively. The experimental results show that this method is more effective in diagnosing bearing faults than other traditional methods and prove its robustness.

A Classification Approach for Induction Motor Faults Based on Empirical Mode Decomposition and Machine Learning Algorithms

A Classification Approach for Induction Motor Faults Based on Empirical Mode Decomposition and Machine Learning Algorithms

A deep learning approach for electric motor fault diagnosis based on modified InceptionV3

Electric motors are essential equipment widely employed in various sectors. However, factors such as prolonged operation, environmental conditions, and inadequate maintenance make electric motors prone to various failures. In this study, we propose a thermography-based motor fault detection method based on InceptionV3 model. To enhance the detection accuracy, we apply Contrast Limited Adaptive Histogram Equalization (CLAHE) to the input images. Furthermore, we improved the performance of the InceptionV3 by integrating a Squeeze-and-Excitation (SE) channel attention mechanism. The proposed model was tested using a dataset containing 369 thermal images of an electric motor with 11 types of faults. Image augmentation was employed to increase the data size and the evaluation was conducted using fivefold cross validation. Experimental results indicate that the proposed model can achieve accuracy, precision, recall, and F1 score of 98.82%, 98.93%, 98.82%, and 98.87%, respectively. Additionally, by freezing the fully connected layers of the InceptionV3 model for feature extraction and training a Support Vector Machines (SVM) to perform classification, it is able to achieve 100% detection rate across all four evaluation metrics. This research contributes to the field of industrial motor fault diagnosis. By incorporating deep learning techniques based on InceptionV3 and SE channel attention mechanism with a traditional classifier, the proposed method can accurately classify different motor faults.

Health Status Detection for Motor Drive Systems Based on Generalized-Layer-Added Principal Component Analysis

Health status detection for motor drive systems includes detecting the working status of the motor and diagnosing open-circuit (OC) faults in the inverter. This paper proposes a generalized-layer-added principle component analysis (GPCA) to determine the load-up/load-shedding status of a motor and diagnose faults in its inverter. Most current methods for detecting OC faults are constrained by changes in the current amplitude and frequency, potentially leading to misjudgments during load-up/load-shedding transient states. The proposed method addresses this issue. Initially, this paper employs a homogenization method to process current data, eliminating the impact of transient processes during motor load-up/load-shedding states on inverter fault diagnosis. Subsequently, the fast Fourier transform (FFT) is used to extract the frequency domain characteristics of the data. If the PCA method is trained with a singular matrix, this can lead to an unreliable result. This paper introduces a generalization layer based on the PCA method, leading to the GPCA method, which enables training with singular matrices. The GPCA method is then developed to compute data features. By presetting thresholds and utilizing the prediction error value and contribution rate index of the GPCA method, the relevant state of the motor drive system can be determined. Finally, through simulations and experiments, it has been demonstrated that the method, using data from the stable working state, can effectively detect the working status of a motor and diagnose OC faults in its inverter, with a diagnostic time of 0.05 current cycles.

A Realistic Method for Diagnosing Bearing Faults of Three-Phase Induction Motor using Advanced Support Vector Machine Algorithm

Bearings are one of the most crucial components of any induction motor. The faults or failure of bearings can lead to high maintenance and operational costs. To ensure the induction motor operates consistently, it is crucial to identify and diagnose bearing faults at an early stage. In this perspective, this manuscript presents a method based on least square support vector machine (LSSVM) to identify the bearing defects of an induction motor. The IM parameters, such as stator currents, input voltage, and rotor speed, were extracted using experimental setup at different loads and health conditions of bearing. Then these parameters were used to train and validate the LSSVM algorithm in MATLAB to diagnose the bearing defects. The proposed method provides 97.14% fault prediction accuracy with an RBF kernel.

Research on Virtual Simulation Experiment Teaching of Scraper Conveyor Condition Monitoring and Fault Diagnosis

The scraper conveyor status monitoring and fault diagnosis virtual simulation experiment system is designed and developed based on the operating environment of the scraper conveyor in the fully mechanized mining face. Using augmented virtual reality teaching resources, through immersive display and interactive operation experiments, students are guided on basis of scraper conveyor mechanical cognition, master the virtual teaching experiment of state monitoring and fault diagnosis such as broken chain monitoring, motor fault classification and identification, reducer compound fault separation, etc. The teaching practice shows that the system can overcome the harsh production environment in coal mines and the risk factors caused by equipment failure, improve students' understanding of the mechanical fault diagnosis technology, and mobilize students' enthusiasm for experiments and initiative to enhance students' innovative ability.

Motor fault diagnosis based on composite multi-scale weighted reverse slope entropy and neighborhood preserving embedding

The motors are critical components of the electromechanical transmission in vehicles, and its operating status directly affects the maneuverability of vehicles. To quickly and accurately identify the operating status of motors, this paper proposes a new entropy - Composite Multi-scale Weighted Reverse Slope Entropy (CMWRSlE) for motor fault diagnosis, which is a more interpretable entropy due to its deep exploration of signals. Firstly, the composite multi-scale weighted reverse slope entropy values are extracted from the vibration signals of the motor in different states; Secondly, the extracted features are dimensionally reduced by the manifold learning algorithm – Neighbourhood Preserving Embedding (NPE) and classified by the hierarchical prototype-based approach (HPA) to achieve the fault diagnosis of the motor. Finally, the method proposed in this paper is validated through two sets of experimental data: motor rotor faults and motor bearing faults. The results show that the accuracy of the proposed method in motor fault diagnosis reaches 100 %, which indicates the effectiveness of the proposed method.

Single nuclei RNA-seq reveals a medium spiny neuron glutamate excitotoxicity signature prior to the onset of neuronal death in an ovine Huntington's disease model.

Huntington's disease (HD) is a neurodegenerative genetic disorder caused by an expansion in the CAG repeat tract of the huntingtin (HTT) gene resulting in behavioural, cognitive, and motor defects. Current knowledge of disease pathogenesis remains incomplete, and no disease course-modifying interventions are in clinical use. We have previously reported the development and characterisation of the OVT73 transgenic sheep model of HD. The 73 polyglutamine repeat is somatically stable and therefore likely captures a prodromal phase of the disease with an absence of motor symptomatology even at 5-years of age and no detectable striatal cell loss. To better understand the disease-initiating events we have undertaken a single nuclei transcriptome study of the striatum of an extensively studied cohort of 5-year-old OVT73 HD sheep and age matched wild-type controls. We have identified transcriptional upregulation of genes encoding N-methyl-D-aspartate (NMDA), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and kainate receptors in medium spiny neurons, the cell type preferentially lost early in HD. Further, we observed an upregulation of astrocytic glutamate uptake transporters and medium spiny neuron GABAA receptors, which may maintain glutamate homeostasis. Taken together, these observations support the glutamate excitotoxicity hypothesis as an early neurodegeneration cascade-initiating process but the threshold of toxicity may be regulated by several protective mechanisms. Addressing this biochemical defect early may prevent neuronal loss and avoid the more complex secondary consequences precipitated by cell death.

Electric vehicle motor fault diagnosis using improved wavelet packet decomposition and particle swarm optimization algorithm

This study addresses the issue of diagnosing faults in electric vehicle motors and presents a method utilizing Improved Wavelet Packet Decomposition (IWPD) combined with particle swarm optimization (PSO). Initially, the analysis focuses on common demagnetization faults, inter turn short circuit faults, and eccentricity faults of permanent magnet synchronous motors. The proposed approach involves the application of IWPD for extracting signal feature vectors, incorporating the energy spectrum scale, and extracting the feature vectors of the signal using the energy spectrum scale. Subsequently, a binary particle swarm optimization algorithm is employed to formulate strategies for updating particle velocity and position. Further optimization of the binary particle swarm algorithm using chaos theory and the simulated annealing algorithm results in the development of a motor fault diagnosis model based on the enhanced particle swarm optimization algorithm. The results demonstrate that the chaotic simulated annealing algorithm achieves the highest accuracy and recall rates, at 0.96 and 0.92, respectively. The model exhibits the highest fault accuracy rates on both the test and training sets, exceeding 98.2%, with a minimal loss function of 0.0035. Following extraction of fault signal feature vectors, the optimal fitness reaches 97.4%. In summary, the model constructed in this study demonstrates effective application in detecting faults in electric vehicle motors, holding significant implications for the advancement of the electric vehicle industry.

Mitigation of TDP-43 toxic phenotype by an RGNEF fragment in amyotrophic lateral sclerosis models.

Aggregation of the RNA-binding protein TAR DNA binding protein (TDP-43) is a hallmark of TDP-proteinopathies including amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). As TDP-43 aggregation and dysregulation are causative of neuronal death, there is a special interest in targeting this protein as a therapeutic approach. Previously, we found that TDP-43 extensively co-aggregated with the dual function protein GEF (guanine exchange factor) and RNA-binding protein rho guanine nucleotide exchange factor (RGNEF) in ALS patients. Here, we show that an N-terminal fragment of RGNEF (NF242) interacts directly with the RNA recognition motifs of TDP-43 competing with RNA and that the IPT/TIG domain of NF242 is essential for this interaction. Genetic expression of NF242 in a fruit fly ALS model overexpressing TDP-43 suppressed the neuropathological phenotype increasing lifespan, abolishing motor defects and preventing neurodegeneration. Intracerebroventricular injections of AAV9/NF242 in a severe TDP-43 murine model (rNLS8) improved lifespan and motor phenotype, and decreased neuroinflammation markers. Our results demonstrate an innovative way to target TDP-43 proteinopathies using a protein fragment with a strong affinity for TDP-43 aggregates and a mechanism that includes competition with RNA sequestration, suggesting a promising therapeutic strategy for TDP-43 proteinopathies such as ALS and FTD.

Research on rotor fault diagnosis technology of three-phase asynchronous motor based on NA-MEMD mutual information and SVM

Aiming at the problem of mode aliasing in the adaptive decomposition of nonlinear and non-stationary current signals generated by three-phase asynchronous motor faults, and the fault features contained in signals collected by a single sensor can not be accurately and comprehensively extracted and characterized when early rotor bar breakage and air gap eccentricity faults occur, A fault diagnosis method for three-phase asynchronous motor based on noise assisted multivariate empirical mode decomposition (NA-MEMD) and mutual information is proposed. Firstly, the NA-MEMD algorithm is used to decompose the three-phase stator current signal of the asynchronous motor to obtain multi-scale intrinsic mode functions (IMFs). Then, the correlation algorithm is used to screen the IMFs containing useful information. Then, the filtered IMF components are reconstructed into new signals and their features are extracted, Finally, support vector machines (SVM) are used to identify the rotor broken bars and air gap eccentric faults of the three-phase asynchronous motor. The experimental results show that the NA-MEMD method has a higher recognition rate than the traditional empirical mode decomposition (EMD) and the ensemble empirical mode decomposition (EEMD) methods.

Improved deep-learning rotor fault diagnosis based on multi vibration sensors and recurrence plots

Deep learning techniques are increasingly applied to time series data, offering promising results in various fields. Deep learning techniques can handle data from multiple sensors to detect anomalies in an industrial environment. This paper proposes a new method of anomaly detection based on a multilayer image representation of different vibration sensors’ recurrence plots. Each sensor’s recurrence plot forms a layer. The performance and reliability of our method were assessed using an experimental database collected under different load conditions and with different types of rotor anomalies. Experiment results demonstrate the effectiveness of GoogLeNet using individual and multi-layered recurrence plots to find rotor faults in an induction motors.

Online Fault Detection of Open-Circuit Faults in a DTP-PMSM Using Double DQ Current Prediction

This research proposes a strategy to diagnose open-phase faults (OPF) and open-switching faults (OSF) in dual three-phase permanent magnet synchronous motor (DTP-PMSM) inverters. The method is based on the dual d–q predictive current model and involves establishing a mathematical model and utilizing the finite control set model predictive current extraction technique to predict the motor current. It then analyzes the characteristics of the switching-tube current under both normal and fault conditions. Finally, a fault predictive current model is introduced and the residual is calculated based on the predicted fault current value and the actual measured current value to diagnose the inverter fault. The proposed method effectively overcomes misjudgment issues encountered in traditional open-circuit fault diagnosis of inverters. It enhances the system’s response speed during dynamic processes and strengthens the robustness of diagnosis algorithm parameters. The experimental results demonstrate that the proposed method can rapidly, effectively, and accurately diagnose open-circuit faults presented in this paper fastest within one-fifth of a current cycle. It achieves a diagnostic accuracy rate of 97% in the dual three-phase permanent magnet synchronous motor drive system.

Unsupervised Feature-Construction-Based Motor Fault Diagnosis.

Any bearing faults are a leading cause of motor damage and bring economic losses. Fast and accurate identification of bearing faults is valuable for preventing damaging the whole equipment and continuously running industrial processes without interruption. Vibration signals from a running motor can be utilized to diagnose a bearing health condition. This study proposes a detection method for bearing faults based on two types of neural networks from motor vibration data. The proposed method uses an autoencoder neural network for constructing a new motor vibration feature and a feed-forward neural network for the final detection. The constructed signal feature enhances the prediction performance by focusing more on a fault type that is difficult to detect. We conducted experiments on the CWRU bearing datasets. The experimental study shows that the proposed method improves the performance of the feed-forward neural network and outperforms the other machine learning algorithms.

Motor fault classification using hybrid short-time Fourier transform and wavelet transform with vibration signal and convolutional neural network

Motor fault classification using hybrid short-time Fourier transform and wavelet transform with vibration signal and convolutional neural network

A deep residual neural network model for synchronous motor fault diagnostics

Synchronous motors play a significant role in a wide range of industrial applications. Their reliable operation is paramount. Any faults in synchronous motors can lead to costly downtime, decreased productivity, and potential safety hazards. By accurately diagnosing and classifying faults, we can proactively address issues before they escalate, ensuring the smooth operation of synchronous motors and minimizing the risk of equipment failure. The accurate diagnosis and fault detection in synchronous motors pose a significant challenge in their operation and maintenance. In the existing models, the feature data at various depths are not thoroughly extracted to maximize their feature extraction capability. Additionally, they employ a single support vector machine to make the final decision on the output. The single support vector machine may not consistently produce more accurate outcomes. Therefore, this paper proposes a novel fault diagnosis model based on a deep residual neural network and multiple support vector machines to diagnose mechanical and electrical faults of synchronous motors. The proposed model improves upon existing fault diagnosis models in two key aspects. Firstly, by employing a deep neural network, the model is able to effectively process and extract fault features from the motor fault dataset, capturing more nuanced information that may be missed by existing models. Secondly, the use of multiple support vector machines enhances the decision-making capability of the model, allowing for more accurate fault diagnosis. By combining these two aspects, the proposed model achieves superior diagnostic performance compared to single support vector machine-based models. Our proposed model has been rigorously evaluated using mechanical and electrical fault datasets, and the results of experimental tests clearly demonstrate its superior diagnostic performance when compared to existing fault diagnosis models. The synergy of deep neural networks and multiple support vector machines not only improves fault detection accuracy but also enhances the robustness and generalizability of the model, making it a valuable tool for real-world industrial applications.

Finding fault types of BLDC motors within UAVs using machine learning techniques

Due to the potential of the Unmanned Aerial Vehicle (UAV), they began to be increasingly used in various fields such as: environment, leisure, health, military, transport, etc. Along with increasing battery storage capacity, the UAVs began to be propulsion by Brushless DC (BLDC) motors. Failure of BLDC motors can lead to loss of control, which can cause accidents. In these conditions, it is necessary to devise methods that can find the defects of the BLDC motors in the UAVs. In this article, the authors propose a novel method to predict BLDC motor defects using machine learning. To maximize the method results, the performance of three machine learning models, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Bayesian Network (BN) in predicting the flaws of BLDC motors, have been compared.