Prognostics of Machine Condition Using Energy Based Monitoring Index and Computational Intelligence

2009 ◽  
Vol 9 (4) ◽  
Author(s):  
B. Samanta ◽  
C. Nataraj
Keyword(s):  
Computational Intelligence ◽  
Support Vector ◽  
Machine Condition ◽  
Data Set ◽  
Inference System ◽  
Training Time ◽  
Vibration Data ◽  
Monitoring Index ◽  
Improved Performance ◽  
Adaptive Neurofuzzy Inference System

A study is presented on applications of computational intelligence (CI) techniques for monitoring and prognostics of machinery conditions. The machine condition is assessed through an energy-based feature, termed as “energy index,” extracted from the vibration signals. The progression of the “monitoring index” is predicted using the CI techniques, namely, recursive neural network (RNN), adaptive neurofuzzy inference system (ANFIS), and support vector regression (SVR). The proposed procedures have been evaluated through benchmark data sets for one-step-ahead prediction. The prognostic effectiveness of the techniques has been illustrated through vibration data set of a helicopter drivetrain system gearbox. The prediction performance of SVR was better than RNN and ANFIS. The improved performance of SVR can be attributed to its inherently better generalization capability. The training time of SVR was substantially higher than RNN and ANFIS. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating feature, the level of damage or degradation, and their progression.

Prognostics of Machine Condition Using Energy Based Monitoring Index and Computational Intelligence

2008 ◽  
Author(s):  
B. Samanta ◽  
C. Nataraj
Keyword(s):  
Computational Intelligence ◽  
Fuzzy Inference ◽  
Support Vector ◽  
Machine Condition ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Benchmark Datasets ◽  
Monitoring Index ◽  
Relationship Of ◽  
The Relationship

A procedure is presented for monitoring and prognostics of machine conditions using computational intelligence (CI) techniques. The machine condition is assessed through an energy-based feature, termed as ‘energy index’, extracted from the vibration signals. The progression of the ‘monitoring index’ is predicted using CI techniques, namely, recursive neural network (RNN), adaptive neuro-fuzzy inference system (ANFIS) and support vector regression (SVR). The proposed prediction procedures have been evaluated through benchmark datasets. The prognostic effectiveness of the techniques has been illustrated through vibration dataset of a helicopter drivetrain system gearbox. The performance of SVR was found to be better than RNN and ANFIS for the dataset used. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating feature, the level of damage/degradation and their progression.

Forecasting Rice Production in West Bengal State in India

2013 ◽  
Vol 4 (4) ◽  
pp. 68-91 ◽  
Author(s):  
Arindam Chaudhuri
Keyword(s):  
Time Series ◽  
Multiple Regression ◽  
Computational Intelligence ◽  
Time Series Data ◽  
Rice Production ◽  
Productivity Index ◽  
Series Data ◽  
Support Vector ◽  
Production Time ◽  
Inference System

Forecasting rice production is a challenging problem in agricultural statistics. The inherent difficulty lies in demand and supply affected by many uncertain factors viz. economic policies, agricultural factors, credit measures, foreign trade etc. which interact in a complex manner. Since last few decades, Statistical techniques are used for developing predictive models to estimate required parameters. Determination of nature of rice production time series data is difficult, expensive, time consuming and involves tedious tests. In this paper, we use Interval Type Fuzzy Auto Regressive Integrated Moving Average (ITnARIMA), Adaptive Neuro Fuzzy Inference System (ANFIS) and Modified Regularized Least Squares Fuzzy Support Vector Regression (MRLSFSVR) for prediction of Productivity Index percent (PI %) of rice production time series data and compare it with traditional Statistical tool of Multiple Regression. The accuracies of ITnARIMA and ANFIS techniques are evaluated as relatively similar. It is found that ANFIS exhibits high performance than ITnARIMA, MRLSFSVR and Multiple Regression for predicting PI %. The performance comparison shows that Computational Intelligence paradigm is a promising tool for minimizing uncertainties in rice production data. Further Computational Intelligence techniques also minimize potential inconsistency of correlations.

scholarly journals Investigations on Incipient Fault Diagnosis of Power Transformer Using Neural Networks and Adaptive Neurofuzzy Inference System

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Nandkumar Wagh ◽  
D. M. Deshpande
Keyword(s):  
Distribution System ◽  
Power Transformer ◽  
Rbf Neural Network ◽  
Correct Diagnosis ◽  
Well Being ◽  
Gas Analysis ◽  
Inference System ◽  
Training Time ◽  
Multiple Faults ◽  
Adaptive Neurofuzzy Inference System

Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA) interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP), radial basis function (RBF) neural network, and adaptive neurofuzzy inference system (ANFIS) has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented.

scholarly journals Vibration Analysis and Time Series Prediction for Wind Turbine Gearbox Prognostics

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Sajid Hussain ◽  
Hossam A. Gabbar
Keyword(s):  
Wind Turbine ◽  
Fuzzy Inference ◽  
Health Management ◽  
Time Series Prediction ◽  
Health Condition ◽  
Wind Turbine Gearbox ◽  
Data Set ◽  
Inference System ◽  
Monitoring Index ◽  
Prediction Techniques

Multiple premature failures of a gearbox in a wind turbine pose a high risk of increasing the operational and maintenance costs and decreasing the profit margins. Prognostics and health management (PHM) techniques are widely used to assess the current health condition of the gearbox and project it in future to predict premature failures. This paper proposes such techniques for predicting gearbox health condition index extracted from the vibration signals. The progression of the monitoring index is predicted using two different prediction techniques, adaptive neuro-fuzzy inference system (ANFIS) and nonlinear autoregressive model with exogenous inputs (NARX). The proposed prediction techniques are evaluated through sun-spot data-set and applied on vibration based health related monitoring index calculated through psychoacoustic phenomenon. A comparison is given for their prediction accuracy. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating features, the level of damage/degradation, and their progression.

Recognition and labeling of faults in wind turbines with a density-based clustering algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shuai Luo ◽  
Hongwei Liu ◽  
Ershi Qi
Keyword(s):  
Wind Turbines ◽  
Clustering Algorithm ◽  
Support Vector ◽  
Scanning Strategy ◽  
Data Set ◽  
Content Type ◽  
Vibration Data ◽  
Density Based Clustering ◽  
Extreme Gradient Boosting ◽  
Data Points

PurposeThe purpose of this paper is to recognize and label the faults in wind turbines with a new density-based clustering algorithm, named contour density scanning clustering (CDSC) algorithm.Design/methodology/approachThe algorithm includes four components: (1) computation of neighborhood density, (2) selection of core and noise data, (3) scanning core data and (4) updating clusters. The proposed algorithm considers the relationship between neighborhood data points according to a contour density scanning strategy.FindingsThe first experiment is conducted with artificial data to validate that the proposed CDSC algorithm is suitable for handling data points with arbitrary shapes. The second experiment with industrial gearbox vibration data is carried out to demonstrate that the time complexity and accuracy of the proposed CDSC algorithm in comparison with other conventional clustering algorithms, including k-means, density-based spatial clustering of applications with noise, density peaking clustering, neighborhood grid clustering, support vector clustering, random forest, core fusion-based density peak clustering, AdaBoost and extreme gradient boosting. The third experiment is conducted with an industrial bearing vibration data set to highlight that the CDSC algorithm can automatically track the emerging fault patterns of bearing in wind turbines over time.Originality/valueData points with different densities are clustered using three strategies: direct density reachability, density reachability and density connectivity. A contours density scanning strategy is proposed to determine whether the data points with the same density belong to one cluster. The proposed CDSC algorithm achieves automatically clustering, which means that the trends of the fault pattern could be tracked.

Diagnosis Model of Hydrogen Sulfide Poisoning Based on Support Vector Machine

2020 ◽  
Vol 16 ◽  
Author(s):  
Yifan Ying ◽  
Yongxi Jin ◽  
Xianchuan Wang ◽  
Jianshe Ma ◽  
Min Zeng ◽  
...  
Keyword(s):  
Hydrogen Sulfide ◽  
Evaluation Method ◽  
Information Gain ◽  
Training Data ◽  
Support Vector ◽  
Control Group ◽  
Diagnostic Model ◽  
Data Set ◽  
Training Time ◽  
Svm Classification

Introduction: Hydrogen sulfide (H2S) is a lethal environmental and industrial poison. The mortality rate of occupational acute H2S poisoning reported in China is 23.1% ~ 50%. Due to the huge amount of information on metabolomics changes after body poisoning, it is important to use intelligent algorithms to mine multivariate interactions. Methods: This paper first uses GC-MS metabolomics to detect changes in the urine components of the poisoned group and control rats to form a metabolic data set, and then uses the SVM classification algorithm in machine learning to train the hydrogen sulfide poisoning training data set to obtain a classification recognition model. A batch of rats (n = 15) was randomly selected and exposed to 20 ppm H2S gas for 40 days (twice morning and evening, 1 hour each exposure) to prepare a chronic H2S rat poisoning model. The other rats (n = 15) were exposed to the same volume of air and 0 ppm hydrogen sulfide gas as the control group. The treated urine samples were tested using a GC-MS. Results: The method locates the optimal parameters of SVM, which improves the accuracy of SVM classification to 100%. This paper uses the information gain attribute evaluation method to screen out the top 6 biomarkers that contribute to the predicted category (Glycerol，β-Hydroxybutyric acid， arabinofuranose，Pentitol，L-Tyrosine，L-Proline). Conclusion: The SVM diagnostic model of hydrogen sulfide poisoning constructed in this work has training time and prediction accuracy; it has achieved excellent results and provided an intelligent decision-making method for the diagnosis of hydrogen sulfide poisoning.

scholarly journals Intelligent and Effective Intrusion Detection System using Machine Learning Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 237-240
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Training Time

Intrusion Detection System observes the network traffic and identifies the attack and also inform the admin to corrective action. Powerful Intrusion Detection system is required for detection to various modern attack. There is need of efficient Intrusion Detection system .The focus of IDS research is the application of machine Learning and Deep Learning techniques. Projected work is combination of Deep Learning Technique in which Non Symmetric Deep Auto Encoder and Machine Learning Algorithm, Support Vector Machine Classifier is used to develop the Model. Stack power of the Non symmetric Deep Auto Encoder and Quickness with exactness of the SVM makes the Model very efficient. This Model not only improves the accuracy value but also improve recall and precision. It also cause the reduction of training time .To evaluate the performance of the Model and do the analysis the special Data set which are used are KDD CUP and NSL KDD Dataset.

Research on Large Scale Data Set Processing Based on SVM

2011 ◽  
Vol 216 ◽  
pp. 738-741
Author(s):  
Yue E Chen ◽  
Bai Li Ren
Keyword(s):  
Large Scale ◽  
Support Vector ◽  
Simulation Experiments ◽  
Data Set ◽  
Training Time ◽  
Training Support ◽  
Large Scale Data ◽  
Vector Machines ◽  
Speech Classification ◽  
Scale Data

SVM has got very good results in the area of solving the classification, regression and density estimation problem in machine learning, has been successfully applied to practical problems of text recognition, speech classification, but the training time is too long is a big drawback. A new reduction strategy is proposed for training support vector machines. This method is fast in convergence without learning machine’s generalization performance, the results of simulation experiments show the feasibility and effectiveness of that method through this method.

scholarly journals Improved K-Means with Adaptive Divergence Weight Bat Algorithm (IKM-ADWBA) and Feature Selection for Type 2 Diabetes Mellitus Prediction

2019 ◽  
Vol 8 (2S11) ◽  
pp. 288-299
Keyword(s):  
Diabetes Mellitus ◽  
Data Mining ◽  
Feature Selection ◽  
Bat Algorithm ◽  
Experimental Result ◽  
Support Vector ◽  
Pima Indians ◽  
Data Set ◽  
Missing Value ◽  
Inference System

Increase in blood glucose (hyperglycaemia) leads to Diabetes Mellitus. There are two kinds of Diabetes mellitus: (Type 1 Diabetes Mellitus (T1DM) and (Diabetes Mellitus (T2DM), then former one is dependent on insulin and the latter one is independent of insulin. Various factors make it difficult to diagnose it. SO the author focuses at binging-in and analyzing the method for making a novel robust diagnosis system using data mining methods. Complete datasets is necessary for data mining techniques, but these techniques doesn’t give accurate results with missing values and all features. So, for prediction, Handling Missing value replacement and selection of important features are becomes a major issue. Hence, Adaptive Neuro Fuzzy Inference System (ANFIS) were proposed to acquire the missing value in dataset and to rectify the above mentioned issue. Then for an effective seed selection in Improved K-means algorithm, Enhanced Inertia Weight Binary Bat Algorithm (EIWBBA) is proposed, which results in high convergence speed. This research work proposed for feature selection with the help of Improved Distributed Kernel based Principal Component analysis (IDKPCA) with less time, after minimizing the entire feature space to the best features set. Then for classification of clustered samples, the author brought-in the Support Vector Machine (SVM). The experimental result confirms that the proposed algorithm gives the best classification accuracy rate when compared with other methods. From Pima Indians Diabetes, the data set has been considered and the experiment is done with the help of MATLAB for examining the Knowledge and the results were distinguished with other outcomes using appropriate toolkits.

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