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Research on vibration-based early diagnostic system for excavator motor bearing using 1-D CNN

Yandagsuren Dorjsuren, Kurauchi Tatsuki, Toriya Hisatoshi, Ikeda Hajime, Adachi Tsuyoshi, Kawamura Youhei

Journal of Sustainable Mining

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2023

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Vol. 22, iss. 1

65--80

EN

In mining, super-large machines such as rope excavators are used to perform the main mining operations. A rope excavator is equipped with motors that drive mechanisms. Motors are easily damaged as a result of harsh mining conditions. Bearings are important parts in a motor; bearing failure accounts for approximately half of all motor failures. Failure reduces work efficiency and increases maintenance costs. In practice, reactive, preventive, and predictive maintenance are used to minimize failures. Predictive maintenance can prevent failures and is more effective than other maintenance. For effective predictive maintenance, a good diagnosis is required to accurately determine motor-bearing health. In this study, vibration-based diagnosis and a one-dimensional convolutional neural network (1-D CNN) were used to evaluate bearing deterioration levels. The system allows for early diagnosis of bearing failures. Normal and failure-bearing vibrations were measured. Spectral and wavelet analyses were performed to determine the normal and failure vibration features. The measured signals were used to generate new data to represent bearing deterioration in increments of 10%. A reliable diagnosis system was proposed. The proposed system could determine bearing health deterioration at eleven levels with considerable accuracy. Moreover, a new data mixing method was applied.

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Automatic cardiac arrhythmia classification based on hybrid 1-D CNN and Bi-LSTM model

Rahul Jagdeep, Sharma Lakhan Dev

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

312--324

EN

Cardiovascular diseases (CVDs) are a group of heart and blood vessel ailments that can cause chest pain and trouble breathing, especially while active. However, some patients with heart disease have no symptoms and may benefit from screening. Electrocardiogram (ECG) measures electrical activity of the heart using sensors positioned on the skin over the chest, and it can be used for the timely detection of CVDs. This work presents a technique for classification among lethal CVDs like atrial fibrillation (Afib), ventricular fibrillation (Vfib), ventricular tachycardia (Vtec), and normal (N) beats. A novel combination of Stationary wavelet transforms (SWT) and a two-stage median filter with Savitzky–Golay (SG) filter were utilised for pre-processing of the ECG signal followed by segmentation and z-score normalisation process. Next, 1-D six-layers convolutional neural network (1- D CNN) was used for automated and reliable feature extraction. After that, bidirectional long short-term memory (Bi-LSTM) was used in the back end for classification of arrhythmias. The novelty of the present work is the use of 1-D CNN and Bi-LSTM architecture followed by relevant and effective pre-processing of the ECG signal makes this technique accurate and reliable. An accuracy of 99.41 % was achieved using 10-fold cross validation, which is superior to the existing state-of-art methods. Thus, this method presents a noble, accurate, and reliable method for classification of cardiac arrhythmia beats.

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Feature engineering combined with 1-D convolutional neural network for improved mortality prediction

Verma Rohit, Maheshwari Saumil, Shukla Anupam

Bio-Algorithms and Med-Systems

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2020

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Vol. 16, no. 4

art. no. 20200056

EN

Objectives: The appropriate care for patients admitted in Intensive care units (ICUs) is becoming increasingly prominent, thus recognizing the use of machine learning models. The real-time prediction of mortality of patients admitted in ICU has the potential for providing the physician with the interpretable results. With the growing crisis including soaring cost, unsafe care, misdirected care, fragmented care, chronic diseases and evolution of epidemic diseases in the domain of healthcare demands the application of automated and real-time data processing for assuring the improved quality of life. The intensive care units (ICUs) are responsible for generating a wealth of useful data in the form of Electronic Health Record (EHR). This data allows for the development of a prediction tool with perfect knowledge backing. Method: We aimed to build the mortality prediction model on 2012 Physionet Challenge mortality prediction database of 4,000 patients admitted in ICU. The challenges in the dataset, such as high dimensionality, imbalanced distribution and missing values, were tackled with analytical methods and tools via feature engineering and new variable construction. The objective of the research is to utilize the relations among the clinical variables and construct new variables which would establish the effectiveness of 1- Dimensional Convolutional Neural Network (1-D CNN) with constructed features. Results: Its performance with the traditional machine learning algorithms like XGBoost classifier, Light Gradient Boosting Machine (LGBM) classifier, Support Vector Machine (SVM), Decision Tree (DT), K-Neighbours Classifier (K-NN), and Random Forest Classifier (RF) and recurrent models like Long Short-Term Memory (LSTM) and LSTMattention is compared for Area Under Curve (AUC). The investigation reveals the best AUC of 0.848 using 1-D CNN model. Conclusion: The relationship between the various features were recognized. Also, constructed new features using existing ones. Multiple models were tested and compared on different metrics.