Early Detection of Ball Bearing Faults Using the Decision Tree Method

The energy management of electric motors is an important part of the industrial energy management, because the electric motors consume about 70% of industrial electricity. The energy management allows reduce electricity consumption through the increase efficiency of industrial processes and reduce CO2 emissions. Knowing the cause of failure in electric motors is a very important element of the industrial energy management. This paper presents a review of the causes and classification of the most common failures in industrial electric motors. The work is complemented by a classification of faults made from surveys and information provided by repair centers electrical machines in southwestern Colombian.

As industrialisation progresses, electric motors are increasingly utilised in manufacturing sectors, and their regular operation plays a crucial role in enhancing production efficiency, safety, and ease. Consequently, there's a growing emphasis on developing technology for monitoring the condition of electric motors. This study focuses on the analysis of common issues like rotor bar failure and eccentricity in induction motors, examining their causes, creating motor models in both normal and malfunctioning conditions through computer simulations, identifying the stator current signals, and comparing their spectra to validate the stator current data. Additionally, this research offers a dependable and efficient dataset for further analysis. The complex and fluctuating nature of the current signals in induction motors necessitates the use of advanced techniques like the tunable-Q wavelet transform (TQWT) and box dimension method for feature extraction, which is more effective in signal characterisation than other approaches. The study then explores the application of Support Vector Machines (SVM) and Artificial Neural Networks (ANN) in fault diagnosis, achieving accuracies of 91.67% and 100%, respectively. The findings indicate that ANN is superior to SVM and suggest this strategy for the automatic detection of motor faults. Implementing such intelligent systems can prevent unexpected and unplanned production interruptions caused by electric motor failures.

Predictive soil mapping depends on understanding the relationships between soil properties and environmental factors. However, in a complex soil landscapes, there is a shortage of suitable approaches to establish these relationships. The main objective is to predict soil thickness in an alpine watershed relating to soil environmental factors based on an unsupervised fuzzy clustering method (fuzzy c-means, FCM) and decision tree (DT) method. In this study, FCM method was used for stratifying the landscape, and then, a representative soil thickness was assigned to each class. For each class, a number of points were randomly chosen in proportion to representative areas, and then, the environmental factors at these point locations were extracted as a training data set (3626 points). For the training data set, DT method was used to obtain the critical threshold of soil–environment relationships. Finally, soil thickness map was created by applying the results of the DT across the region. An independently collected field sampling set (31 points) was used to evaluate the effectiveness of the proposed approach. For training set, 95.48 % of the total training data were correctly predicted. For validation set, the overall accuracy and Kappa coefficient could reach 74.2 % and 0.659, respectively. Evaluation accuracy of soil map was up to 74.2 %. In conclusion, it is suggested that soil–landscape modeling using FCM and DT methods can be efficiently used as a valuable research technique for spatial soil thickness prediction in a complex soil landscape where soil characteristics and properties are not available.

This study aims to determine the classification of student graduation timeliness by using the Decision Tree and Naive Bayes methods. This study uses a quantitative method, where the approach used is the classification of various attributes that affect the timeliness of student graduation. The independent variables in the classification are mostly called attributes; In this study, the attributes of school of origin, gender, area of origin, profession of parents, study program and Grade Point Average (GPA) were used. While the dependent variable or in the classification is usually called a label, in this study the label used as a decision attribute is the timeliness of student graduation. In this study, two methods were used, namely using the nave Bayes method and a decision tree (decision tree) to determine the classification of the timeliness of student graduation and to determine the level of classification accuracy. Based on the results of the analysis, it can be concluded that the classification using the nave Bayes method obtained 36 predicted data according to the actual data and 7 different predicted data from the actual data. Meanwhile, in the 42 decision tree method, the predicted data is in accordance with the actual data and there is only 1 predicted data that is different from the actual data. Decision Tree method has a lower classification error rate than the Naive Bayes method. The level of accuracy of prediction results using the Decision Tree method is higher than the Naive Bayes method.

A preordonance-based decision tree method and its parallel implementation in the framework of Map-Reduce

Objective: To compare the stepwise regression (SR) method and the decision tree (DT) method for classification of parkinsonian syndromes. Method: We applied the scaled subprofile model/principal component analysis (SSM/PCA) method to FDG-PET brain image data to obtain covariance patterns and the corresponding subject scores. The subject scores formed the input to the C4.5 decision tree algorithm to classify the subject brain images. For the SR method, scatter plots and receiver operating characteristic (ROC) curves indicate the subject classifications. We then compare the decision tree classifier results with those of the SR method. Results: We found out that the SR method performs slightly better than the DT method. We attribute this to the fact that the SR method uses a linear combination of the best features to form one robust feature, unlike the DT method. However, when the same robust feature is used as the input for the DT classifier, the performance is as high as that of the SR method. Conclusion: Even though the SR method performs better than the DT method, including the SR procedure in the DT classification yields a better performance. Additionally, the decision tree approach is more suitable for human interpretation and exploration than the SR method.

This research was conducted to compare the accuracy when decision tree and logistic regression methods are used on some data. Decision tree is one method of classification techniques in data mining. In the decision tree method, very large data samples will be represented as smaller rules, and logistic regression is a method that aims to determine the effect of an independent variable on other variables, namely dichotomous dependent variables. Both algorithms were written and analyzed using R software to see which method is better between the decision tree method and the logistic regression method applied to SNP (Single Nucleotide Polymorphism) genetic data, namely Asthma data. SNP Genetic Data was obtained from R software with the package name "SNPassoc" and the data name "asthma". Asthma data has 57 features, namely Country, Gender, Age, BMI, Smoke, Case control, and SNP (Single Nucleotide Polymorphism) genetic code. Comparative analysis was carried out based on the results of the accuracy values obtained in the two methods. Variations in the proportion of the test data used were 40%, 30%, 20% and 10% and were simulated 1000 times on the grounds of obtaining a better accuracy value. The results obtained show that the decision tree method obtains an accuracy value of 0.5793, 0.5777, 0.5745, 0.5526, respectively, while the logistic regression method is 0.7696, 0.7729, 0.7763, 0.7788, respectively and they are achieved at the proportion of test data of 40%, 30%, 20%, 10%. Thus it can be concluded that in this case the logistic regression method is better than the decision tree method in classifying Asthma data.

It is not easy for students who have just entered the university to adapt to the new education and living environment. In this process, social-economic, educational, and personal factors have a great impact on the academic achievements of first-year students. In this study, the aim is to determine the factors affecting the academic success of university freshmen and to predict the academic success of students by using decision tree and fuzzy logic methods together, considering the most important factors. Students’ achievement was predicted by the decision tree method considering the students’ university entrance scores, satisfaction with the educational environment, accommodation, attendance to the course, and preference of the department as input of the prediction model. Fuzzy inference rules were created based on the decision tree obtained from implementation of the J48 method, which provides a better understanding of the dependence between students’ academic achievement and impact factors.KeywordsAcademic achievementDecision treePredictive modelFuzzy inference

The ability to give a prognosis for failure of a system is an invaluable tool and can be applied to electric motors. In this paper, three wavelet based methods have been developed that achieve this goal. Wavelet and filter bank theory, the nearest neighbor rule, and linear discriminant functions are reviewed. A framework for the development of a fault detection and classification algorithm based on the coefficients calculated from the discrete wavelet transform and using clustering is described. An experimental setup based on RT-Linux is described and results from testing are presented, verifying the analysis.

Wavelet-based methods for the prognosis of mechanical and electrical failures in electric motors

There are available metrics for predicting fault prone classes, which may help software organizations for planning and performing testing activities. This may be possible due to proper allocation of resources on fault prone parts of the design and code of the software. Hence, importance and usefulness of such metrics is understandable, but empirical validation of these metrics is always a great challenge. Decision Tree (DT) methods have been successfully applied for solving classification problems in many applications. This paper evaluates the capability of three DT methods and compares its performance with statistical method in predicting fault prone software classes using publicly available NASA data set. The results indicate that the prediction performance of DT is generally better than statistical model. However, similar types of studies are required to be carried out in order to establish the acceptability of the DT models.

Delays in the completion of pvd production can be caused by several factors. Including the actual experience in the production of the difficulty of each process and color type, even the difficulty of the product type can also be affected. In this study, the prediction of the delay in the completion of pvd production was carried out using the C4.5 decision tree and Multilayer Perceptron data mining method approach using Production Results data at PT. Surya Toto Indonesia, whose results are expected to provide information and input for the company in making production plans in the future. The data testing method was carried out with 5 (five) testing times with different amounts of data to determine the level of consistency of accuracy obtained. C4.5 gives the results of a decision tree where the root is the color type and as the leaf is the product category, type type and order period. The average value of accuracy generated in the C4.5 decision tree method is 87.15%. While the Multilayer Perceptron obtained an average accuracy of 87.98%, which is greater than the decision tree method with a difference of 0.83%.

Diabetes is a disease with a growing number of sufferers and is the cause of death of 1.5 million people in the world in 2019. A treatment for diabetes is needed, one of which is by predicting diabetics. The K-Nearest Neighbour (KNN), Random Forest, and Decision Tree methods are some methods that can be used to predict diabetes classification. This research aims to compare the performance of KNN, Random Forest, and Decision Tree methods based on accuracy and computation time. The data used in this study are Pregnancies, Glucose, Insulin, Body Mass Index (BMI), and Age as independent variables and Outcome as a dependent variable. The results of research on data that has not been normalised with Min-Max show that the KNN method has a faster computation time than the other two methods, while based on the accuracy value the Decision Tree method has a higher value than the other two methods. Furthermore, the Min-Max normalised data shows a decrease in the accuracy value of the Decision Tree and Random Forest methods, while the accuracy value of the KNN method has increased. Therefore, the Min-Max normalisation treatment is better used for the KNN method.

Decision tree method is a classification method that has been widely used for the solution of problems of classification. Decision tree classification provides a rapid and effective method. The approach has been proven decision tree method can be applied in various fields of life. Capability classification is indicated by the decision tree method is what encourages authors to use decision tree methods approach to measure the performance of civil servants. To build a decision tree induction algorithms used. In this study, the ID3 algorithm method is used to construct a decision tree. Starting with the data collecting training samples and then measuring the entropy and information gain. Information Gain value will be used as the root of a decision tree. And translates it into a decision tree classification rules.The results show that the decision tree method is used to produce classification rules into groups employee performance Good and Bad. The resulting rules are used to measure the performance of employees and classifying employees into two groups.The result to assist management in making more objective assessment process. Keywords: ID3 Algorithm, Decision Tree, Employee Performance.

Decision tree method is a classification method that has been widely used for the solution of problems of classification. Decision tree classification provides a rapid and effective method. The approach has been proven decision tree method can be applied in various fields of life. Capability classification is indicated by the decision tree method is what encourages authors to use decision tree methods approach to measure the performance of civil servants. To build a decision tree induction algorithms used. In this study, the ID3 algorithm method is used to construct a decision tree. Starting with the data collecting training samples and then measuring the entropy and information gain. Information Gain value will be used as the root of a decision tree. And translates it into a decision tree classification rules. The results show that the decision tree method is used to produce classification rules into groups employee performance Good and Bad. The resulting rules are used to measure the performance of employees and classifying employees into two groups above are constructed in an information system. Information system built to assist management in making more objective assessment process. *) Penulis korespondensi: utje_caem@yahoo.com Keywords : ID3 Algorithm; Decision tree; Employee performance