Machine-learning algorithms for helicopter hydraulic faults detection: model based research

Гареев, А. М.; Minaev, Evgeniy; Stadnik, D. M.; Davydov, Nikita; Protsenko, V. I.; Попельнюк, И. А.; Никоноров, Артем; Gimadiev, Asgat

doi:10.1088/1742-6596/1368/5/052027

Cited by 6 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They have reported a classification accuracy of 99.17%. A comparison of SVM and CatBoost classifiers was performed by Gareev et al 51 to diagnose mechanical faults and they conclude that SVM gives lower accuracy (85.3%) while CatBoost gives higher classification accuracy up to 99.3%. Long et al 52 has used an improved AdaBoost classifier fed with multi-sensors data for motor fault diagnosis and has achieved a classification accuracy of 92.38%.…”

Section: Performance Comparisonmentioning

confidence: 99%

Condition monitoring of water pump bearings using ensemble classifier

Irfan

Althobiani

Alwadie

et al. 2022

Advances in Mechanical Engineering

View full text Add to dashboard Cite

The bearings faults are reported to be the major reason for centrifugal pump (CPs) failures. Limited literature is available to diagnose the minor scratches in the bearing surface through non-intrusive condition monitoring techniques. Recent research on the analysis of bearing scratches through non-intrusive motor current analysis (MCA) has shown encouraging results where the comparison of machine learning and convolutional neural networks (CNNs) was performed in the classification of healthy bearings and faulty bearings (holes and scratches). The fault classification accuracy of 89.26% through MCA combination with machine learning and CNN algorithm was reported which is very low. The key factors of low accuracies were identified as low amplitudes of the harmonics in the MCA spectrum, the magnitude of environmental noise, and utilization of conventional feature extraction techniques. This problem has been tackled in this paper by developing a novel feature extractor (NFE) that extracts powerful features from the integrated current and voltage sensors data. The NFE has been derived using the threshold-based decision mechanism which has the capability to identify the location of the feature harmonic, feature extraction, measure the amplitude of the fault component, and compare it with the derived threshold. The experimental data has been collected for the bearing balls (BB), bearing cage (BC), inner race (IR) and the outer race (OR) faults, and the performance of the NFE has been tested on an ensemble classifier (CatBoost) and the better classification accuracy (99.2% for an individual feature and 100% with the combination of two or more features) of NFE has been achieved as compared to previously reported methods.

show abstract

Section: Performance Comparisonmentioning

confidence: 99%

Condition monitoring of water pump bearings using ensemble classifier

Irfan

Althobiani

Alwadie

et al. 2022

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…Recently, the machine learning methods and artificial intelligence techniques have been developed for various applications, such as helicopter hydraulic FD problems, medical diagnosis systems, risk assessment applications, and so on [17][18][19][20]. These approaches have also been employed for FD problems in a few studies.…”

Section: Literature Reviewmentioning

confidence: 99%

Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry

Wang

Tavoosi

Mohammadzadeh

et al. 2021

Sensors

View full text Add to dashboard Cite

The main contribution of this paper is to develop a new flowmeter fault detection approach based on optimized non-singleton type-3 (NT3) fuzzy logic systems (FLSs). The introduced method is implemented on an experimental gas industry plant. The system is modeled by NT3FLSs, and the faults are detected by comparison of measured end estimated signals. In this scheme, the detecting performance depends on the estimation and modeling performance. The suggested NT3FLS is used because of the existence of a high level of measurement errors and uncertainties in this problem. The designed NT3FLS with uncertain footprint-of-uncertainty (FOU), fuzzy secondary memberships and adaptive non-singleton fuzzification results in a powerful tool for modeling signals immersed in noise and error. The level of non-singleton fuzzification and membership parameters are tuned by maximum correntropy (MC) unscented Kalman filter (KF), and the rule parameters are learned by correntropy KF (CKF) with fuzzy kernel size. The suggested learning algorithms can handle the non-Gaussian noises that are common in industrial applications. The various types of flowmeters are investigated, and the effect of common faults are examined. It is shown that the suggested approach can detect the various faults with good accuracy in comparison with conventional approaches.

show abstract

“…Gareev et al compared the accuracy of four machine-learning approaches for fault detection in a hydraulic system. The first three approaches are based on support vector machine (SVM) classifiers with linear, polynomial, and radial basis function kernels, and the last is gradient boosting on oblivious decision trees [ 8 ]. Kim and Jeong used a five-layer VGGNet based on a convolutional neural network (CNN) model for data augmentation because the amount of hydraulic system data is insufficient, which shows good performance in terms of accuracy and loss for the classification of hydraulic system data [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection Based on Time Series Data of Hydraulic Accumulator

Park

Chakraborty²,

Vuong

et al. 2022

Sensors

View full text Add to dashboard Cite

Although hydraulic accumulators play a vital role in the hydraulic system, they face the challenges of being broken by continuous abnormal pulsating pressure which occurs due to the malfunction of hydraulic systems. Hence, this study develops anomaly detection algorithms to detect abnormalities of pulsating pressure for hydraulic accumulators. A digital pressure sensor was installed in a hydraulic accumulator to acquire the pulsating pressure data. Six anomaly detection algorithms were developed based on the acquired data. A threshold averaging algorithm over a period based on the averaged maximum/minimum thresholds detected anomalies 2.5 h before the hydraulic accumulator failure. In the support vector machine (SVM) and XGBoost model that distinguish normal and abnormal pulsating pressure data, the SVM model had an accuracy of 0.8571 on the test set and the XGBoost model had an accuracy of 0.8857. In a convolutional neural network (CNN) and CNN autoencoder model trained with normal and abnormal pulsating pressure images, the CNN model had an accuracy of 0.9714, and the CNN autoencoder model correctly detected the 8 abnormal images out of 11 abnormal images. The long short-term memory (LSTM) autoencoder model detected 36 abnormal data points in the test set.

show abstract

Machine-learning algorithms for helicopter hydraulic faults detection: model based research

Cited by 6 publications

References 5 publications

Condition monitoring of water pump bearings using ensemble classifier

Condition monitoring of water pump bearings using ensemble classifier

Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry

Anomaly Detection Based on Time Series Data of Hydraulic Accumulator

Contact Info

Product

Resources

About