Application of Machine Learning for Tool Condition Monitoring in Turning

Patange, Abhishek D.; R., Jegadeeshwaran,; Bajaj, N. S.; Khairnar, A. N.; Gavade, N. A.

doi:10.32604/sv.2022.014910

Cited by 20 publications

(19 citation statements)

References 36 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DT is a tree-based machine learning model used for classification and regression tasks [ 35 , 36 ]. DT constructs a tree using the features set, and put important features on the root nodes, while leaf nodes function as decision nodes for the DT.…”

Section: Materials and Methodsmentioning

confidence: 99%

White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images

et al. 2022

View full text Add to dashboard Cite

White blood cell (WBC) type classification is a task of significant importance for diagnosis using microscopic images of WBC, which develop immunity to fight against infections and foreign substances. WBCs consist of different types, and abnormalities in a type of WBC may potentially represent a disease such as leukemia. Existing studies are limited by low accuracy and overrated performance, often caused by model overfit due to an imbalanced dataset. Additionally, many studies consider a lower number of WBC types, and the accuracy is exaggerated. This study presents a hybrid feature set of selective features and synthetic minority oversampling technique-based resampling to mitigate the influence of the above-mentioned problems. Furthermore, machine learning models are adopted for being less computationally complex, requiring less data for training, and providing robust results. Experiments are performed using both machine- and deep learning models for performance comparison using the original dataset, augmented dataset, and oversampled dataset to analyze the performances of the models. The results suggest that a hybrid feature set of both texture and RGB features from microscopic images, selected using Chi2, produces a high accuracy of 0.97 with random forest. Performance appraisal using k-fold cross-validation and comparison with existing state-of-the-art studies shows that the proposed approach outperforms existing studies regarding the obtained accuracy and computational complexity.

show abstract

Section: Materials and Methodsmentioning

confidence: 99%

White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In this strategy, an ELM classifier was used to identify different fault types. The overall fault diagnosis framework is shown in Figure 2 [ 31 ]. The main steps were as follows:…”

Section: Methodsmentioning

confidence: 99%

Composite Multiscale Transition Permutation Entropy-Based Fault Diagnosis of Bearings

Guo

Zou³

et al. 2022

Sensors

View full text Add to dashboard Cite

When considering the transition probability matrix of ordinal patterns, transition permutation entropy (TPE) can effectively extract fault features by quantifying the irregularity and complexity of signals. However, TPE can only characterize the complexity of the vibration signals at a single scale. Therefore, a multiscale transition permutation entropy (MTPE) technique has been proposed. However, the original multiscale method still has some inherent defects in the coarse-grained process, such as considerably shortening the length of time series at large scale, which leads to a low entropy evaluation accuracy. In order to solve these problems, a composite multiscale transition permutation entropy (CMTPE) method was proposed in order to improve the incomplete coarse-grained analysis of MTPE by avoiding the loss of some key information in the original fault signals, and to improve the performance of feature extraction, robustness to noise, and accuracy of entropy estimation. A fault diagnosis strategy based on CMTPE and an extreme learning machine (ELM) was proposed. Both simulation and experimental signals verified the advantages of the proposed CMTPE method. The results show that, compared with other comparison strategies, this strategy has better robustness, and can carry out feature recognition and bearing fault diagnosis more accurately and with improved stability.

show abstract

“…The use of decision tree classifiers to sense the changes in pressure using MEMS built accelerometer to collect and store data is provided by Pardeshi et al [ 44 ]. Recent works by Patange et al [ 45 ] and Shewale et al [ 46 ] provided us with the importance of vibrations, temperature and other parameters in health monitoring systems. All these researches will lead us to develop more smart and accurate devices which will change human health monitoring systems forever.…”

Section: Literaure Surveymentioning

confidence: 99%

Artificial Intelligence Based Approach for Classification of Human Activities Using MEMS Sensors Data

Khan

Imaduddin

Singh

et al. 2023

Sensors

View full text Add to dashboard Cite

The integration of Micro Electronic Mechanical Systems (MEMS) sensor technology in smartphones has greatly improved the capability for Human Activity Recognition (HAR). By utilizing Machine Learning (ML) techniques and data from these sensors, various human motion activities can be classified. This study performed experiments and compiled a large dataset of nine daily activities, including Laying Down, Stationary, Walking, Brisk Walking, Running, Stairs-Up, Stairs-Down, Squatting, and Cycling. Several ML models, such as Decision Tree Classifier, Random Forest Classifier, K Neighbors Classifier, Multinomial Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine, were trained on sensor data collected from accelerometer, gyroscope, and magnetometer embedded in smartphones and wearable devices. The highest test accuracy of 95% was achieved using the random forest algorithm. Additionally, a custom-built Bidirectional Long-Short-Term Memory (Bi-LSTM) model, a type of Recurrent Neural Network (RNN), was proposed and yielded an improved test accuracy of 98.1%. This approach differs from traditional algorithmic-based human activity detection used in current wearable technologies, resulting in improved accuracy.

show abstract

Application of Machine Learning for Tool Condition Monitoring in Turning

Cited by 20 publications

References 36 publications

White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images

White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images

Composite Multiscale Transition Permutation Entropy-Based Fault Diagnosis of Bearings

Artificial Intelligence Based Approach for Classification of Human Activities Using MEMS Sensors Data

Contact Info

Product

Resources

About