Determination of Time-to-Failure for Automotive System Components Using Machine Learning

2020 ◽  
Vol 20 (6) ◽  
Author(s):  
John O’Donnell ◽  
Hwan-Sik Yoon
Keyword(s):  
Machine Learning ◽  
Degradation Rate ◽  
Shock Absorber ◽  
Learning Algorithm ◽  
Health Condition ◽  
Performance Degradation ◽  
Remaining Useful Life ◽  
Machine Learning Algorithm ◽  
Time To Failure ◽  
And Performance

Abstract In recent years, there has been a growing interest in the connectivity of vehicles. This connectivity allows for the monitoring and analysis of large amount of sensor data from vehicles during their normal operations. In this paper, an approach is proposed for analyzing such data to determine a vehicle component’s remaining useful life named time-to-failure (TTF). The collected data is first used to determine the type of performance degradation and then to train a regression model to predict the health condition and performance degradation rate of the component using a machine learning algorithm. When new data is collected later for the same component in a different system, the trained model can be used to estimate the time-to-failure of the component based on the predicted health condition and performance degradation rate. To validate the proposed approach, a quarter-car model is simulated, and a machine learning algorithm is applied to determine the time-to-failure of a failing shock absorber. The results show that a tap-delayed nonlinear autoregressive network with exogenous input (NARX) can accurately predict the health condition and degradation rate of the shock absorber and can estimate the component’s time-to-failure. To the best of the authors’ knowledge, this research is the first attempt to determine a component’s time-to-failure using a machine learning algorithm.

scholarly journals Solid-State Lithium Battery Cycle Life Prediction Using Machine Learning

2021 ◽  
Vol 11 (10) ◽  
pp. 4671
Author(s):  
Danpeng Cheng ◽  
Wuxin Sha ◽  
Linna Wang ◽  
Shun Tang ◽  
Aijun Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solid State ◽  
Life Prediction ◽  
Learning Algorithm ◽  
Remaining Useful Life ◽  
Cycle Life ◽  
Quantitative Prediction ◽  
Test Accuracy ◽  
Machine Learning Algorithm ◽  
Discharge Data

Battery lifetime prediction is a promising direction for the development of next-generation smart energy storage systems. However, complicated degradation mechanisms, different assembly processes, and various operation conditions of the batteries bring tremendous challenges to battery life prediction. In this work, charge/discharge data of 12 solid-state lithium polymer batteries were collected with cycle lives ranging from 71 to 213 cycles. The remaining useful life of these batteries was predicted by using a machine learning algorithm, called symbolic regression. After populations of breed, mutation, and evolution training, the test accuracy of the quantitative prediction of cycle life reached 87.9%. This study shows the great prospect of a data-driven machine learning algorithm in the prediction of solid-state battery lifetimes, and it provides a new approach for the batch classification, echelon utilization, and recycling of batteries.

scholarly journals Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Peter Appiahene ◽  
Yaw Marfo Missah ◽  
Ussiph Najim
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Banking Sector ◽  
Banking Industry ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
And Performance

The financial crisis that hit Ghana from 2015 to 2018 has raised various issues with respect to the efficiency of banks and the safety of depositors’ in the banking industry. As part of measures to improve the banking sector and also restore customers’ confidence, efficiency and performance analysis in the banking industry has become a hot issue. This is because stakeholders have to detect the underlying causes of inefficiencies within the banking industry. Nonparametric methods such as Data Envelopment Analysis (DEA) have been suggested in the literature as a good measure of banks’ efficiency and performance. Machine learning algorithms have also been viewed as a good tool to estimate various nonparametric and nonlinear problems. This paper presents a combined DEA with three machine learning approaches in evaluating bank efficiency and performance using 444 Ghanaian bank branches, Decision Making Units (DMUs). The results were compared with the corresponding efficiency ratings obtained from the DEA. Finally, the prediction accuracies of the three machine learning algorithm models were compared. The results suggested that the decision tree (DT) and its C5.0 algorithm provided the best predictive model. It had 100% accuracy in predicting the 134 holdout sample dataset (30% banks) and a P value of 0.00. The DT was followed closely by random forest algorithm with a predictive accuracy of 98.5% and a P value of 0.00 and finally the neural network (86.6% accuracy) with a P value 0.66. The study concluded that banks in Ghana can use the result of this study to predict their respective efficiencies. All experiments were performed within a simulation environment and conducted in R studio using R codes.

scholarly journals FPGA-based Hardware Acceleration for SVM Machine Learning Algorithm

2021 ◽  
Vol 229 ◽  
pp. 01024
Author(s):  
Jakjoud Fatimazahra ◽  
Hatim Anas ◽  
Abella Bouaaddi
Keyword(s):  
Machine Learning ◽  
Object Recognition ◽  
Execution Time ◽  
Learning Algorithm ◽  
Hardware Acceleration ◽  
Machine Learning Algorithm ◽  
Computing Power ◽  
Recognition Algorithms ◽  
Plant Recognition ◽  
And Performance

object recognition algorithms are both large consumers of computing power and memory which affects the quality and performance especially when it comes to large image datasets, in this paper we propose an algorithm for fruit/plant recognition that we will accelerate it using the PYNQ Board to evaluate the execution time and the accuracy of the classifier.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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