scholarly journals Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network

2021 ◽  
Vol 11 (17) ◽  
pp. 8033
Author(s):  
Juan-Jose Saucedo-Dorantes ◽  
Israel Zamudio-Ramirez ◽  
Jonathan Cureno-Osornio ◽  
Roque Alfredo Osornio-Rios ◽  
Jose Alfonso Antonino-Daviu
Keyword(s):  
Neural Network ◽  
Condition Monitoring ◽  
High Performance ◽  
Induction Motors ◽  
Statistical Features ◽  
Monitoring Method ◽  
Data Set ◽  
Linear Discriminant ◽  
Outer Race ◽  
Bearing Fault

Bearings are the elements that allow the rotatory movement in induction motors, and the fault occurrence in these elements is due to excessive working conditions. In induction motors, electrical erosion remains the most common phenomenon that damages bearings, leading to incipient faults that gradually increase to irreparable damages. Thus, condition monitoring strategies capable of assessing bearing fault severities are mandatory to overcome this critical issue. The contribution of this work lies in the proposal of a condition monitoring strategy that is focused on the analysis and identification of different fault severities of the outer race bearing fault in an induction motor. The proposed approach is supported by fusion information of different physical magnitudes and the use of Machine Learning and Artificial Intelligence. An important aspect of this proposal is the calculation of a hybrid-set of statistical features that are obtained to characterize vibration and stator current signals by its processing through domain analysis, i.e., time-domain and frequency-domain; also, the fusion of information of both signals by means of the Linear Discriminant Analysis is important due to the most discriminative and meaningful information is retained resulting in a high-performance condition characterization. Besides, a Neural Network-based classifier allows validating the effectiveness of fusion information from different physical magnitudes to face the diagnosis of multiple fault severities that appear in the bearing outer race. The method is validated under an experimental data set that includes information related to a healthy condition and five different severities that appear in the outer race of bearings.

scholarly journals Broken rotor bars fault detection in induction motors based on current envelope and neural network

2021 ◽  
Vol 3 (2 (111)) ◽  
pp. 88-95
Author(s):  
Mohammed Obaid Mustafa
Keyword(s):  
Neural Network ◽  
Condition Monitoring ◽  
Induction Motors ◽  
Fault Classification ◽  
Monitoring Method ◽  
Broken Rotor Bars ◽  
Stator Current ◽  
Motor Current ◽  
Machine Failure ◽  
Feature Extraction And Selection

The growing demand for dependable manufacturing techniques has sped up research into condition monitoring and fault diagnosis of critical motor parts. On the other hand, in modern industry, machine maintenance is becoming increasingly necessary. An insufficient maintenance strategy can result in unnecessarily high downtime or accidental machine failure, resulting in significant financial and even human life losses. Downtime and repair costs rise as a result of failure. Furthermore, developing an online condition monitoring method may be one solution to come up for the problem. Early detection of faults is very vital since they grow quickly and can cause further problems to the motor. This paper proposes an effective strategy for the classification of broken rotor bars (BRBs) for induction motors (IMs) that uses a new approach based on Artificial Neural Network (ANN) and stator current envelope. The stator current envelope is extracted using the cubic spline interpolation process. This is based on the idea that the amplitude-modulated motor current signal can be revealed using the motor current envelope. The stator current envelope is used to select seven features, which will be used as input for the neural network. Five IM conditions were experimentally used in this study, including a part of BRB, 1 BRB, 2 BRBs and 3 BRBs. The new feature extraction and selection approach achieves a higher level of accuracy than the conventional method for motor fault classification, according to the experimental results. Indeed, the results are impressive, and it is capable of detecting the exact number of broken rotor bars under full load conditions

scholarly journals Condition Monitoring of Wind Turbine Based on Copula Function and Autoregressive Neural Network

2018 ◽  
Vol 198 ◽  
pp. 04008
Author(s):  
Zhongshan Huang ◽  
Ling Tian ◽  
Dong Xiang ◽  
Sichao Liu ◽  
Yaozhong Wei
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Copula Function ◽  
Single Step ◽  
Fusion Model ◽  
Monitoring Method ◽  
Monitoring Model ◽  
Autoregressive Neural Network

The traditional wind turbine fault monitoring is often based on a single monitoring signal without considering the overall correlation between signals. A global condition monitoring method based on Copula function and autoregressive neural network is proposed for this problem. Firstly, the Copula function was used to construct the binary joint probability density function of the power and wind speed in the fault-free state of the wind turbine. The function was used as the data fusion model to output the fusion data, and a fault-free condition monitoring model based on the auto-regressive neural network in the faultless state was established. The monitoring model makes a single-step prediction of wind speed and power, and statistical analysis of the residual values of the prediction determines whether the value is abnormal, and then establishes a fault warning mechanism. The experimental results show that this method can provide early warning and effectively realize the monitoring of wind turbine condition.

scholarly journals Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1474 ◽  
Author(s):  
Francesco Castellani ◽  
Luigi Garibaldi ◽  
Alessandro Paolo Daga ◽  
Davide Astolfi ◽  
Francesco Natili
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Statistical Tests ◽  
Novelty Detection ◽  
General Idea ◽  
Data Set ◽  
Vibration Measurements ◽  
Bearing Fault ◽  
Operation Conditions

Condition monitoring of gear-based mechanical systems in non-stationary operation conditions is in general very challenging. This issue is particularly important for wind energy technology because most of the modern wind turbines are geared and gearbox damages account for at least the 20% of their unavailability time. In this work, a new method for the diagnosis of drive-train bearings damages is proposed: the general idea is that vibrations are measured at the tower instead of at the gearbox. This implies that measurements can be performed without impacting the wind turbine operation. The test case considered in this work is a wind farm owned by the Renvico company, featuring six wind turbines with 2 MW of rated power each. A measurement campaign has been conducted in winter 2019 and vibration measurements have been acquired at five wind turbines in the farm. The rationale for this choice is that, when the measurements have been acquired, three wind turbines were healthy, one wind turbine had recently recovered from a planetary bearing fault, and one wind turbine was undergoing a high speed shaft bearing fault. The healthy wind turbines are selected as references and the damaged and recovered are selected as targets: vibration measurements are processed through a multivariate Novelty Detection algorithm in the feature space, with the objective of distinguishing the target wind turbines with respect to the reference ones. The application of this algorithm is justified by univariate statistical tests on the selected time-domain features and by a visual inspection of the data set via Principal Component Analysis. Finally, a novelty index based on the Mahalanobis distance is used to detect the anomalous conditions at the damaged wind turbine. The main result of the study is that the statistical novelty of the damaged wind turbine data set arises clearly, and this supports that the proposed measurement and processing methods are promising for wind turbine condition monitoring.

A Health Condition Monitoring Method of Aeroengine Gas Path System Based on Consistency Fusion and Neural Network

2013 ◽  
Vol 385-386 ◽  
pp. 981-984
Author(s):  
Jian Guo Cui ◽  
Can Wu ◽  
Li Ying Jiang ◽  
Yi Wen Qi ◽  
Guo Qiang Li
Keyword(s):  
Neural Network ◽  
Condition Monitoring ◽  
Probabilistic Neural Network ◽  
Complex Structure ◽  
Reference Value ◽  
Health Condition ◽  
Monitoring Method ◽  
Accurate Localization ◽  
Safety And Reliability ◽  
Path System

Because of the complex structure, poor working conditions and lots of fault modes of aeroengine , it is necessary to monitor the operational status, accurate localization of aeroengine fault and identify fault to improve the safety and reliability of aircraft. Based on consistency fusion, this paper uses probabilistic neural network to monitor health condition of aeroengine and puts forward a combined method of health condition monitoring based on the consistency fusion and the neural network. The results of test show that this method can quickly monitor the health condition of the aeroengine and has certain reference value for other mechanical equipments condition monitoring.

scholarly journals Prediction and prognosis of acute myocardial infarction in patients with previous coronary artery bypass grafting using neural network model

2020 ◽  
pp. 118-118
Author(s):  
Predrag Mitrovic ◽  
Branislav Stefanovic ◽  
Mina Radovanovic ◽  
Nebojsa Radovanovic ◽  
Dubravka Rajic ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Performance ◽  
Artery Bypass ◽  
Test Group ◽  
Training Group ◽  
Original Data ◽  
Data Set ◽  
Linear Discriminant ◽  
Comparative Results ◽  
Almost All

Introduction/Objective. The aim of this study was to analyze the usefulness and accuracy of artificial neural network in the prognosis of infarcted patients with operation. Methods. The thirteen predictor variables per patient were defined as data set. All patients were divided in two groups randomly: training group of 1090 patients and test group of 1090 patients. Evaluation of neural network performance was organized by using of the original data, as well as its complementary test data, containing patient data not used for training the network. Generating a file of comparative results, program compared actual with predict outcome for each patient. Results. All results were compared with 2x2 contingency table constructed from sensitivity, specificity, accuracy and positive-negative prediction. Network was able to predict outcome with accuracy of 96.2%, sensitivity of 78.4%, specificity 100%, positive predictivity 100% and negative predictivity 96%. There was not efficient for prognosis of infarcted patients with operation using linear discriminant analysis (accuracy 68.3%, sensitivity 66.4%, and positive predictivity 30.2%). Conclusion. This study suggest that neural network was better for almost all parameters in outcome prognosis of infarcted patients with operation.

scholarly journals Novel Methodology for Condition Monitoring of Gear Wear Using Supervised Learning and Infrared Thermography

2020 ◽  
Vol 10 (2) ◽  
pp. 506 ◽  
Author(s):  
Emmanuel Resendiz-Ochoa ◽  
Juan J. Saucedo-Dorantes ◽  
Juan P. Benitez-Rangel ◽  
Roque A. Osornio-Rios ◽  
Luis A. Morales-Hernandez
Keyword(s):  
Supervised Learning ◽  
Infrared Thermography ◽  
Condition Monitoring ◽  
Infrared Imaging ◽  
Laboratory Data ◽  
Industrial Process ◽  
Operating Conditions ◽  
Data Set ◽  
Linear Discriminant

In gearboxes, the occurrence of unexpected failures such as wear in the gears may occur, causing unwanted downtime with significant financial losses and human efforts. Nowadays, noninvasive sensing represents a suitable tool for carrying out the condition monitoring and fault assessment of industrial equipment in continuous operating conditions. Infrared thermography has the characteristic of being installed outside the machinery or the industrial process under assessment. Also, the amount of information that sensors can provide has become a challenge for data processing. Additionally, with the development of condition monitoring strategies based on supervised learning and artificial intelligence, the processing of signals with significant improvements during the classification of information has been facilitated. Thus, this paper proposes a novel noninvasive methodology for the diagnosis and classification of different levels of uniform wear in gears through thermal analysis with infrared imaging. The novelty of the proposed method includes the calculation of statistical time-domain features from infrared imaging, the consideration of a dimensionality reduction stage by means of Linear Discriminant Analysis, and automatic fault diagnosis performed by an artificial neural network. The proposed method is evaluated under an experimental laboratory data set, which is composed of the following conditions: healthy, and three severity degrees of uniform wear in gears, namely, 25%, 50%, and 75% of uniform wear. Finally, the obtained results are compared with classical condition monitoring approaches based on vibration analysis.

Gear Condition Monitoring and Classification Using Wireless Sensor Networks

2006 ◽  
Author(s):  
Xin Xue ◽  
V. Sundararajan ◽  
Luis Gonzalez
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Condition Monitoring ◽  
Wireless Sensors ◽  
High Performance ◽  
Nearest Neighbor ◽  
Industrial Applications ◽  
Classification Performance ◽  
Wireless Sensor ◽  
Linear Discriminant

Current research in wireless sensor networks has chiefly focused on environmental monitoring applications. Wireless sensors are emerging as viable instrumentation techniques for industrial applications because of their flexibility, non-intrusive operation, safety and their low cost, low power characteristics. We describe a prototype gear condition monitoring system incorporating wireless sensors. Measurements of strain on gear teeth, vibration and temperature were undertaken using strain gage, accelerometer, and thermistors, respectively. The sensors interface to a sensor board that is connected to a microprocessor and a radio. Gear faults diagnosis using conventional classification techniques such as principle component analysis (PCA), Fisher linear discriminant analysis (LDA) and Nearest-Neighbor Rule (NNR) is studied in this paper. Two sets of vibration data, one set of strain data, and three sets of temperature data are used to classify a running gear under normal condition and a running gear with simulated crack teeth. Feature level data fusion is used to test the classification performance of simple but less effective features to study the fusion effects. The results show high performance of strain features, high quality of the classifier and obvious fusion effect which increases the classification performance.

scholarly journals Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1884 ◽  
Author(s):  
Rafia Nishat Toma ◽  
Alexander E. Prosvirin ◽  
Jong-Myon Kim
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Induction Motors ◽  
Statistical Features ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Motor Current ◽  
Comparison Results

Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich information about bearing health conditions and are commonly utilized for fault diagnosis in bearings. However, collecting these signals is expensive and sometimes impractical because it requires the use of external sensors. The external sensors demand enough space and are difficult to install in inaccessible sites. To overcome these disadvantages, motor current signal-based bearing fault diagnosis methods offer an attractive solution. As such, this paper proposes a hybrid motor-current data-driven approach that utilizes statistical features, genetic algorithm (GA) and machine learning models for bearing fault diagnosis. First, the statistical features are extracted from the motor current signals. Second, the GA is utilized to reduce the number of features and select the most important ones from the feature database. Finally, three different classification algorithms namely KNN, decision tree, and random forest, are trained and tested using these features in order to evaluate the bearing faults. This combination of techniques increases the accuracy and reduces the computational complexity. The experimental results show that the three classifiers achieve an accuracy of more than 97%. In addition, the evaluation parameters such as precision, F1-score, sensitivity, and specificity show better performance. Finally, to validate the efficiency of the proposed model, it is compared with some recently adopted techniques. The comparison results demonstrate that the suggested technique is promising for diagnosis of IM bearing faults.

scholarly journals State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)

Aerospace ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 335
Author(s):  
Wei Dai ◽  
Kui Liang ◽  
Bin Wang
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Convolutional Neural Network ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Manufacturing Processes ◽  
State Monitoring ◽  
Monitoring Method ◽  
Tool Condition ◽  
Aerospace Manufacturing

In the aerospace manufacturing field, tool conditions are essential to ensure the production quality for aerospace parts and reduce processing failures. Therefore, it is extremely necessary to develop a suitable tool condition monitoring method. Thus, we propose a tool wear process state monitoring method for aerospace manufacturing processes based on convolutional neural networks to recognize intermediate abnormal states in multi-stage processes. There are two innovations and advantages of the proposed approach: one is that the criteria for judging abnormal conditions are extended, which is more useful for practical application. The other is that the proposed approach solved the influence of feature-to-recognition stability. Firstly, the tool wear level was divided into different state modes according to the probability density interval based on the kernel density estimation (KDE), and the corresponding state modes were connected to obtain the point-to-point control limit. Then, the state recognition model based on a convolutional neural network (CNN) was developed, and the sensitivity of the monitoring window was considered in the model. Finally, open-source datasets were used to verify the feasibility of the proposed method, and the results demonstrated the applicability of the proposed method in practice for tool condition monitoring.

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