Automatically Detecting Excavator Anomalies Based on Machine Learning
Excavators are one of the most frequently used pieces of equipment in large-scale construction projects. They are closely related to the construction speed and total cost of the entire project. Therefore, it is very important to effectively monitor their operating status and detect abnormal conditio...
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doaj-f13dc9dc6b064d02b0b92b6740aff51e2020-11-24T21:30:42ZengMDPI AGSymmetry2073-89942019-07-0111895710.3390/sym11080957sym11080957Automatically Detecting Excavator Anomalies Based on Machine LearningQingqing Zhou0Guo Chen1Wenjun Jiang2Kenli Li3Keqin Li4College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaDepartment of Computer Science, State University of New York at New Paltz, New Paltz, NY 14821, USAExcavators are one of the most frequently used pieces of equipment in large-scale construction projects. They are closely related to the construction speed and total cost of the entire project. Therefore, it is very important to effectively monitor their operating status and detect abnormal conditions. Previous research work was mainly based on expert systems and traditional statistical models to detect excavator anomalies. However, these methods are not particularly suitable for modern sophisticated excavators. In this paper, we take the first step and explore the use of machine learning methods to automatically detect excavator anomalies by mining its working condition data collected from multiple sensors. The excavators we studied are from Sany Group, the largest construction machinery manufacturer in China. We have collected 40 days working condition data of 107 excavators from Sany. In addition, we worked with six excavator operators and engineers for more than a month to clean the original data and mark the anomalous samples. Based on the processed data, we have designed three anomaly detection schemes based on machine learning methods, using support vector machine (SVM), back propagation (BP) neural network and decision tree algorithms, respectively. Based on the real excavator data, we have carried out a comprehensive evaluation. The results show that the anomaly detection accuracy is as high as 99.88%, which is obviously superior to the previous methods based on expert systems and traditional statistical models.https://www.mdpi.com/2073-8994/11/8/957excavatoranomaly detectionmachine learningSVMBP neural networkdecision tree |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qingqing Zhou Guo Chen Wenjun Jiang Kenli Li Keqin Li |
spellingShingle |
Qingqing Zhou Guo Chen Wenjun Jiang Kenli Li Keqin Li Automatically Detecting Excavator Anomalies Based on Machine Learning Symmetry excavator anomaly detection machine learning SVM BP neural network decision tree |
author_facet |
Qingqing Zhou Guo Chen Wenjun Jiang Kenli Li Keqin Li |
author_sort |
Qingqing Zhou |
title |
Automatically Detecting Excavator Anomalies Based on Machine Learning |
title_short |
Automatically Detecting Excavator Anomalies Based on Machine Learning |
title_full |
Automatically Detecting Excavator Anomalies Based on Machine Learning |
title_fullStr |
Automatically Detecting Excavator Anomalies Based on Machine Learning |
title_full_unstemmed |
Automatically Detecting Excavator Anomalies Based on Machine Learning |
title_sort |
automatically detecting excavator anomalies based on machine learning |
publisher |
MDPI AG |
series |
Symmetry |
issn |
2073-8994 |
publishDate |
2019-07-01 |
description |
Excavators are one of the most frequently used pieces of equipment in large-scale construction projects. They are closely related to the construction speed and total cost of the entire project. Therefore, it is very important to effectively monitor their operating status and detect abnormal conditions. Previous research work was mainly based on expert systems and traditional statistical models to detect excavator anomalies. However, these methods are not particularly suitable for modern sophisticated excavators. In this paper, we take the first step and explore the use of machine learning methods to automatically detect excavator anomalies by mining its working condition data collected from multiple sensors. The excavators we studied are from Sany Group, the largest construction machinery manufacturer in China. We have collected 40 days working condition data of 107 excavators from Sany. In addition, we worked with six excavator operators and engineers for more than a month to clean the original data and mark the anomalous samples. Based on the processed data, we have designed three anomaly detection schemes based on machine learning methods, using support vector machine (SVM), back propagation (BP) neural network and decision tree algorithms, respectively. Based on the real excavator data, we have carried out a comprehensive evaluation. The results show that the anomaly detection accuracy is as high as 99.88%, which is obviously superior to the previous methods based on expert systems and traditional statistical models. |
topic |
excavator anomaly detection machine learning SVM BP neural network decision tree |
url |
https://www.mdpi.com/2073-8994/11/8/957 |
work_keys_str_mv |
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