A Bayesian optimized discriminant analysis model for condition monitoring of face milling cutter using vibration datasets

2021 ◽  
pp. 1-12
Author(s):  
Naman S. Bajaj ◽  
Abhishek D. Patange ◽  
R. Jegadeeshwaran ◽  
Kaushal A. Kulkarni ◽  
Rohan S. Ghatpande ◽  
...  
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Condition Monitoring ◽  
Machining Process ◽  
Bayesian Optimization ◽  
Machine Learning Techniques ◽  
Analysis Model ◽  
Tool Condition ◽  
Learning Techniques ◽  
Optimization Search

Abstract With the advent of Industry 4.0, which conceptualizes self-monitoring of rotating machine parts by adopting techniques like Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), data analytics, cloud computing, etc. The significant research area in predictive maintenance is Tool Condition Monitoring (TCM) as the tool condition affects the overall machining process and its economics. Lately, machine learning techniques are being used to classify the tool's condition in operation. These techniques are cost-saving and help industries with adopting future-proof solutions for their operations. One such technique called Discriminant analysis (DA) must be examined particularly for TCM. Owing to its less expensive computation and shorter run times, using them in TCM will ensure effective use of the cutting tool and reduce maintenance times. This paper presents a Bayesian optimized discriminant analysis model to classify and monitor the tool condition into three user-defined classes. The data is collected using an in-house designed and developed Data Acquisition (DAQ) module set up on a Vertical Machining Center (VMC). The hyperparameter tuning has been incorporated using Bayesian optimization search, and the parameter which gives the best model was found out to be ‘Linear’, achieving an accuracy of 93.3%. This work confirms the feasibility of machine learning techniques like DA in the field of TCM and using Bayesian optimization algorithms to fine-tune the model, making it industry-ready.

scholarly journals Use of Learning Mechanisms to Improve the Condition Monitoring of Wind Turbine Generators: A Review

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7129
Author(s):  
Ana Rita Nunes ◽  
Hugo Morais ◽  
Alberto Sardinha
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Predictive Maintenance ◽  
Machine Learning Techniques ◽  
Learning Mechanisms ◽  
Learning Stage ◽  
Operation Conditions ◽  
Turbine Generators ◽  
Wind Turbine Generators ◽  
Learning Techniques

The main goal of this paper is to review and evaluate how we can take advantage of state-of-the-art machine learning techniques and apply them in wind energy operation conditions monitoring and fault diagnosis, boosting wind turbines’ availability. To accomplish this, we focus our work on analysing the current techniques in predictive maintenance, which are aimed at acting before a major failure occurs using condition monitoring. In particular, we start framing the predictive maintenance problem as an ML problem to detect patterns that indicate a fault on turbine generators. Then, we extend the problem to detect future faults. Therefore, this review will consist of analysing techniques to tackle the challenges of each machine learning stage, such as data pre-processing, feature engineering, and the selection of the best-suited model. By using specific evaluation metrics, the expected final result of using these techniques will be an improvement in the early prediction of a future fault. This improvement will have an increase in the availability of the turbine, and therefore in energy production.

scholarly journals Identifying Mangrove Species Using Field Close-Range Snapshot Hyperspectral Imaging and Machine-Learning Techniques

2018 ◽  
Vol 10 (12) ◽  
pp. 2047 ◽  
Author(s):  
Jingjing Cao ◽  
Kai Liu ◽  
Yuanhui Zhu ◽  
Jun Li ◽  
Zhi He
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Reflectance Spectra ◽  
Machine Learning Techniques ◽  
Order Derivative ◽  
Mangrove Species ◽  
First Order ◽  
Learning Techniques ◽  
Close Range

Investigating mangrove species composition is a basic and important topic in wetland management and conservation. This study aims to explore the potential of close-range hyperspectral imaging with a snapshot hyperspectral sensor for identifying mangrove species under field conditions. Specifically, we assessed the data pre-processing and transformation, waveband selection and machine-learning techniques to develop an optimal classification scheme for eight mangrove species in Qi'ao Island of Zhuhai, Guangdong, China. After data pre-processing and transformation, five spectral datasets, which included the reflectance spectra R and its first-order derivative d(R), the logarithm of the reflectance spectra log(R) and its first-order derivative d[log(R)], and hyperspectral vegetation indices (VIs), were used as the input data for each classifier. Consequently, three waveband selection methods, including the stepwise discriminant analysis (SDA), correlation-based feature selection (CFS), and successive projections algorithm (SPA) were used to reduce dimensionality and select the effective wavebands for identifying mangrove species. Furthermore, we evaluated the performance of mangrove species classification using four classifiers, including linear discriminant analysis (LDA), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). Application of the four considered classifiers on the reflectance spectra of all wavebands yielded overall classification accuracies of the eight mangrove species higher than 80%, with SVM having the highest accuracy of 93.54% (Kappa=0.9256). Using the selected wavebands derived from SPA, the accuracy of SVM reached 93.13% (Kappa=0.9208). The addition of hyperspectral VIs and d[log(R)] spectral datasets further improves the accuracies to 93.54% (Kappa=0.9253) and 96.46% (Kappa=0.9591), respectively. These results suggest that it is highly effective to apply field close-range snapshot hyperspectral images and machine-learning classifiers to classify mangrove species.

Analysis of Non-Traditional Machining Processes Using Machine Learning

2021 ◽  
pp. 195-202
Author(s):  
Somnath Das
Keyword(s):  
Machine Learning ◽  
Manufacturing Systems ◽  
Complex Dynamics ◽  
Rapid Development ◽  
Machining Process ◽  
Machine Learning Techniques ◽  
Machining Processes ◽  
High Quality ◽  
New Developments ◽  
Learning Techniques

The nature of manufacturing systems faces increasingly complex dynamics to meet the demand for high quality products efficiently. One area, which experienced rapid development in terms not only of promising results but also of usability, is machine learning. New developments in certain domains such as mathematics, computer science, and the availability of easy-to-use tools, often freely available, offer great potential to transform the non-traditional machining domain and its understanding of the increase in manufacturing data. However, the field is very broad and even confusing, which presents a challenge and a barrier that hinders wide application. Here, this chapter helps to present an overview of the available machine learning techniques for improving the non-traditional machining process area. It provides a basis for the subsequent argument that the machine learning is a suitable tool for manufacturers to face these challenges head-on in non-traditional machining processes.

scholarly journals Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

2016 ◽  
Vol 16 (4) ◽  
pp. 86-98 ◽  
Author(s):  
Ralph Olusola Aluko ◽  
Olumide Afolarin Adenuga ◽  
Patricia Omega Kukoyi ◽  
Aliu Adebayo Soyingbe ◽  
Joseph Oyewale Oyedeji
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Academic Success ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Undergraduate Students ◽  
Machine Learning Techniques ◽  
Nearest Neighbour ◽  
Analysis Model ◽  
Linear Discriminant

In recent years, there has been an increase in the number of applicants seeking admission into architecture programmes. As expected, prior academic performance (also referred to as pre-enrolment requirement) is a major factor considered during the process of selecting applicants. In the present study, machine learning models were used to predict academic success of architecture students based on information provided in prior academic performance. Two modeling techniques, namely K-nearest neighbour (k-NN) and linear discriminant analysis were applied in the study. It was found that K-nearest neighbour (k-NN) outperforms the linear discriminant analysis model in terms of accuracy. In addition, grades obtained in mathematics (at ordinary level examinations) had a significant impact on the academic success of undergraduate architecture students. This paper makes a modest contribution to the ongoing discussion on the relationship between prior academic performance and academic success of undergraduate students by evaluating this proposition. One of the issues that emerges from these findings is that prior academic performance can be used as a predictor of academic success in undergraduate architecture programmes. Overall, the developed k-NN model can serve as a valuable tool during the process of selecting new intakes into undergraduate architecture programmes in Nigeria.

scholarly journals EMG BASED DIAGNOSIS OF MYOPATHY AND NEUROPATHY USING MACHINE LEARNING TECHNIQUES

2020 ◽  
Vol 4 (4) ◽  
pp. 38-45
Author(s):  
Anisha C. D ◽  
Arulanand N
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Time Domain ◽  
Neuromuscular Disorders ◽  
Bayesian Optimization ◽  
Machine Learning Techniques ◽  
Diagnostic Model ◽  
Learning Techniques ◽  
The Individual ◽  
Better Than

Myopathy and Neuropathy are non-progressive and progressive neuromuscular disorders which weakens the muscles and nerves respectively. Electromyography (EMG) signals are bio signals obtained from the individual muscle cells. EMG based diagnosis for neuromuscular disorders is a safe and reliable method. Integrating the EMG signals with machine learning techniques improves the diagnostic accuracy. The proposed system performs analysis on the clinical raw EMG dataset which is obtained from the publicly available PhysioNet database. The two-channel raw EMG dataset of healthy, myopathy and neuropathy subjects are divided into samples. The Time Domain (TD) features are extracted from divided samples of each subject. The extracted features are annotated with the class label representing the state of the individual. The annotated features split into training and testing set in the standard ratio 70: 30. The comparative classification analysis on the complete annotated features set and prominent features set procured using Pearson correlation technique is performed. The features are scaled using standard scaler technique. The analysis on scaled annotated features set and scaled prominent features set is also implemented. The hyperparameter space of the classifiers are given by trial and error method. The hyperparameters of the classifiers are tuned using Bayesian optimization technique and the optimal parameters are obtained. and are fed to the tuned classifier. The classification algorithms considered in the analysis are Random Forest and Multi-Layer Perceptron Neural Network (MLPNN). The performance evaluation of the classifiers on the test data is computed using the Accuracy, Confusion Matrix, F1 Score, Precision and Recall metrics. The evaluation results of the classifiers states that Random Forest performs better than MLPNN wherein it provides an accuracy of 96 % with non-scaled Time Domain (TD) features and MLPNN outperforms better than Random Forest with an accuracy of 97% on scaled Time Domain (TD) features which is higher than the existing systems. The inferences from the evaluation results is that Bayesian optimization tuned classifiers improves the accuracy which provides a robust diagnostic model for neuromuscular disorder diagnosis.

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