Early detection and classification of internal leakage in boom actuator of mobile hydraulic machines using SVM

Jose, Joseph T.; Das, J. C.; Mishra, Surendra Kumar; Wrat, Gyan

doi:10.1016/j.engappai.2021.104492

Cited by 22 publications

(12 citation statements)

References 15 publications

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“…In this study, the model’s performance was judged through classification accuracy by hydraulic component and the TPR and TNR. Regarding classification accuracy, the classification performance of cooler, valve, pump, and accumulator in related studies had performances of 1, 1, 0.80, and 0.65, respectively [ 18 ]; however, the results of this study showed that the maximum classification performances of 0.88, 0.96, 0.96, and 0.96 were obtained. It had high classification accuracy in the case of internal pump leakage and hydraulic accumulator.…”

Section: Discussionmentioning

confidence: 72%

“…To detect internal leakage faults that affect the hydraulic system’s dynamic performance and reduce energy efficiency, periodic data were analyzed through trained support vector classifier (SVC) to detect faults early [ 18 ]. In addition, a more efficient electricity production process was proposed by integrating the production process control system and IT system by designing a method to detect faults in the water pump early based on data measured by the control device through internet of things (IoT)-enabled predictive maintenance [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

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Anomaly Detection with Feature Extraction Based on Machine Learning Using Hydraulic System IoT Sensor Data

Kim

Heo

2022

Sensors

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Hydraulic systems are advanced in function and level as they are used in various industrial fields. Furthermore, condition monitoring using internet of things (IoT) sensors is applied for system maintenance and management. In this study, meaningful features were identified through extraction and selection of various features, and classification evaluation metrics were presented through machine learning and deep learning to expand the diagnosis of abnormalities and defects in each component of the hydraulic system. Data collected from IoT sensor data in the time domain were divided into clusters in predefined sections. The shape and density characteristics were extracted by cluster. Among 2335 newly extracted features, related features were selected using correlation coefficients and the Boruta algorithm for each hydraulic component and used for model learning. Linear discriminant analysis (LDA), logistic regression, support vector classifier (SVC), decision tree, random forest, XGBoost, LightGBM, and multi-layer perceptron were used to calculate the true positive rate (TPR) and true negative rate (TNR) for each hydraulic component to detect normal and abnormal conditions. Valve condition, internal pump leakage, and hydraulic accumulator data showed TPR performance of 0.94 or more and a TNR performance of 0.84 or more. This study’s findings can help to determine the stable and unstable states of each component of the hydraulic system and form the basis for engineers’ judgment.

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Section: Discussionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Anomaly Detection with Feature Extraction Based on Machine Learning Using Hydraulic System IoT Sensor Data

Kim

Heo

2022

Sensors

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“…Results indicated that the proposed PSO-Improve-CNN system had the highest fault-recognition accuracy reaching 98.7%. Another solution proposed by Jose [69] uses the Support Vector Machine (SVM) classifier with Particle Swarm Optimization (PSO) for early detection and classification of internal leakage in the boom actuator of a mobile hydraulic machine. The PSO was used for feature selection.…”

Section: Swarm Fault Diagnosismentioning

confidence: 99%

Artificial Intelligence Methods in Hydraulic System Design

Filo

2023

Energies

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Reducing energy consumption and increasing operational efficiency are currently among the leading research topics in the design of hydraulic systems. In recent years, hydraulic system modeling and design techniques have rapidly expanded, especially using artificial intelligence methods. Due to the variety of algorithms, methods, and tools of artificial intelligence, it is possible to consider the prospects and directions of their further development. The analysis of the most recent publications allowed three leading technologies to be indicated, including artificial neural networks, evolutionary algorithms, and fuzzy logic. This article summarizes their current applications in the research, main advantages, and limitations, as well as expected directions for further development.

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“…The coupling effects between the different components make real-time hydraulic system fault diagnosis a challenging task. To address this issue, data-driven fault diagnostic methods have attracted considerable attention from both industry and academia [ 4 , 5 , 6 , 7 , 8 , 9 ]. Models that are able to abstract valuable information from historical data are the key for these data-driven fault diagnostic methods [ 10 ], which have been widely investigated in recent years.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning (ML) models are used widely in data-driven fault diagnosis tasks. Many machine learning models, including support vector machines (SVMs) [ 7 , 11 ], neural networks (NNs) [ 4 , 5 ], random forest [ 12 ], and the extreme learning machine (ELM) [ 13 ], have been developed and applied to fault detection and classification. In these methods, the diagnostic accuracy is strongly dependent on the features that are fed to the classifier.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Fault Diagnosis for Hydraulic System Based on Multi-Sensor Convolutional Neural Network

Tao,

Jia,

Wang

et al. 2024

Sensors

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This paper proposed a real-time fault diagnostic method for hydraulic systems using data collected from multiple sensors. The method is based on a proposed multi-sensor convolutional neural network (MS-CNN) that incorporates feature extraction, sensor selection, and fault diagnosis into an end-to-end model. Both the sensor selection process and fault diagnosis process are based on abstract fault-related features learned by a CNN deep learning model. Therefore, compared with the traditional sensor-and-feature selection method, the proposed MS-CNN can find the sensor channels containing higher-level fault-related features, which provides two advantages for diagnosis. First, the sensor selection can reduce the redundant information and improve the diagnostic performance of the model. Secondly, the reduced number of sensors simplifies the model, reducing communication burden and computational complexity. These two advantages make the MS-CNN suitable for real-time hydraulic system fault diagnosis, in which the multi-sensor feature extraction and the computation speed are both significant. The proposed MS-CNN approach is evaluated experimentally on an electric-hydraulic subsea control system test rig and an open-source dataset. The proposed method shows obvious superiority in terms of both diagnosis accuracy and computational speed when compared with traditional CNN models and other state-of-the-art multi-sensor diagnostic methods.

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Early detection and classification of internal leakage in boom actuator of mobile hydraulic machines using SVM

Cited by 22 publications

References 15 publications

Anomaly Detection with Feature Extraction Based on Machine Learning Using Hydraulic System IoT Sensor Data

Anomaly Detection with Feature Extraction Based on Machine Learning Using Hydraulic System IoT Sensor Data

Artificial Intelligence Methods in Hydraulic System Design

Real-Time Fault Diagnosis for Hydraulic System Based on Multi-Sensor Convolutional Neural Network

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