Air compressor fault diagnosis through statistical feature extraction and random forest classifier

Building area is a vital consumer of all globally produced energy. Structures of buildings absorb about 40 % of the total energy created which transcription about 30 % of the integral worldwide CO2 radiations. As such, reducing the measure of energy absorbed by the building area would incredibly help the much-crucial depletions in world energy utilization and the related ecological concerns. This paper presents a smart system for thermal comfort prediction on residential buildings using data driven model with Random Forest Classifier. The system starts by acquiring a global thermal comfort data, pre-processed the acquired data, by removing missing values and duplicated values, and also reduced the numbers of features in the dataset by selecting just twelve columns out of 70 columns in total. This process is called feature extraction. After the pre-processing and feature extraction, the dataset was split into a training and testing set. The training set was 70% while the testing set was 30% of the original dataset. The training data was used in training our thermal comfort model with Random Forest Classifier. After training, Random Forest Classifier had an accuracy of 99.99% which is about 100% approximately. We then save our model and deployed to web through python flask, so that users can use it in predicting real time thermal comfort in their various residential buildings.

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An integrated method based on refined composite multivariate hierarchical permutation entropy and random forest and its application in rotating machinery

Journal of Vibration and Control ◽

10.1177/1077546319877711 ◽

2019 ◽

Vol 26 (3-4) ◽

pp. 146-160

Author(s):

Xianzhi Wang ◽

Shubin Si ◽

Yongbo Li ◽

Xiaoqiang Du

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Random Forest ◽

Extraction Method ◽

Rotating Machinery ◽

Permutation Entropy ◽

Feature Extraction Method ◽

Multichannel Signals ◽

Fault Feature Extraction ◽

Fault Information

Fault feature extraction of rotating machinery is crucial and challenging due to its nonlinear and nonstationary characteristics. In order to resolve this difficulty, a quality nonlinear fault feature extraction method is required. Hierarchical permutation entropy has been proven to be a promising nonlinear feature extraction method for fault diagnosis of rotating machinery. Compared with multiscale permutation entropy, hierarchical permutation entropy considers the fault information hidden in both high frequency and low frequency components. However, hierarchical permutation entropy still has some shortcomings, such as poor statistical stability for short time series and inability of analyzing multichannel signals. To address such disadvantages, this paper proposes a new entropy method, called refined composite multivariate hierarchical permutation entropy. Refined composite multivariate hierarchical permutation entropy can extract rich fault information hidden in multichannel signals synchronously. Based on refined composite multivariate hierarchical permutation entropy and random forest, a novel fault diagnosis framework is proposed in this paper. The effectiveness of the proposed method is validated using experimental and simulated signals. The results demonstrate that the proposed method outperforms multivariate multiscale fuzzy entropy, refined composite multivariate multiscale fuzzy entropy, multivariate multiscale sample entropy, multivariate multiscale permutation entropy, multivariate hierarchical permutation entropy, and composite multivariate hierarchical permutation entropy in recognizing the different faults of rotating machinery.

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Fault Diagnosis of a Single Point Cutting Tool using Statistical Features by Random Forest Classifier

Indian Journal of Science and Technology ◽

10.17485/ijst/2016/v9i33/101340 ◽

2016 ◽

Vol 9 (33) ◽

Author(s):

Cynthia Deb ◽

M. Ramesh Nachiappan ◽

M. Elangovan ◽

V. Sugumaran

Keyword(s):

Fault Diagnosis ◽

Random Forest ◽

Cutting Tool ◽

Single Point ◽

Random Forest Classifier ◽

Statistical Features

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Heart Disease Prediction Using Decision Tree and Random Forest Classification Techniques

Applications of Big Data in Large- and Small-Scale Systems - Advances in Data Mining and Database Management ◽

10.4018/978-1-7998-6673-2.ch015 ◽

2021 ◽

pp. 234-259

Author(s):

Nitika Kapoor ◽

Parminder Singh

Keyword(s):

Feature Extraction ◽

Heart Disease ◽

Random Forest ◽

Decision Tree ◽

Random Forest Classifier ◽

Disease Prediction ◽

Decision Tree Classifier ◽

Hybrid Classifier ◽

Forest Classification ◽

Tree Classifier

Data mining is the approach which can extract useful information from the data. The prediction analysis is the approach which can predict future possibilities based on the current information. The authors propose a hybrid classifier to carry out the heart disease prediction. The hybrid classifier is combination of random forest and decision tree classifier. Moreover, the heart disease prediction technique has three steps, which are data pre-processing, feature extraction, and classification. In this research, random forest classifier is applied for the feature extraction and decision tree classifier is applied for the generation of prediction results. However, random forest classifier will extract the information and decision tree will generate final classifier result. The authors show the results of proposed model using the Python platform. Moreover, the results are compared with support vector machine (SVM) and k-nearest neighbour classifier (KNN).

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