Automatic feature extraction and selection for classification of cyclical time series data

Schneider, Tizian; Helwig, Nikolai; Schütze, Andreas

doi:10.1515/teme-2016-0072

Cited by 40 publications

(21 citation statements)

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“…Hydraulic systems play an important role in a wide variety of industrial applications, such as robotics, manufacturing, aerospace, and engineering machinery. Monitoring the condition of hydraulic equipment can not only effectively improve productivity and reduce maintenance costs and downtime, but also improve the reliability and safety of this equipment in its application [1][2][3]. In particular, the hydraulic valve is the core control component of the hydraulic system, and it is widely used in numerous engineering applications to control the flow and pressure of fluids [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Lei

Jiang

et al. 2019

Processes

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A novel fault diagnosis method is proposed, depending on a cloud service, for the typical faults in the hydraulic directional valve. The method, based on the Machine Learning Service (MLS) HUAWEI CLOUD, achieves accurate diagnosis of hydraulic valve faults by combining both the advantages of Principal Component Analysis (PCA) in dimensionality reduction and the eXtreme Gradient Boosting (XGBoost) algorithm. First, to obtain the principal component feature set of the pressure signal, PCA was utilized to reduce the dimension of the measured inlet and outlet pressure signals of the hydraulic directional valve. Second, a machine learning sample was constructed by replacing the original fault set with the principal component feature set. Third, the MLS was employed to create an XGBoost model to diagnose valve faults. Lastly, based on model evaluation indicators such as precision, the recall rate, and the F1 score, a test set was used to compare the XGBoost model with the Classification And Regression Trees (CART) model and the Random Forests (RFs) model, respectively. The research results indicate that the proposed method can effectively identify valve faults in the hydraulic directional valve and have higher fault diagnosis accuracy.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Lei

Jiang

et al. 2019

Processes

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show abstract

“…Moreover, this dataset is also beneficial in performing component or system FDD, such as the application researched in [28,29]. It is also applicable in creating and testing feature extraction and selection algorithms [30].…”

Section: Condition Monitoring Of Hydraulic Test Rig Data Setmentioning

confidence: 99%

A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest

Mallak¹,

Fathi²

2020

Sci

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In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, it shines the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using three-fold cross validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. This is followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, and the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time-series multivariate sensor readings.

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“…Moreover, this dataset is also beneficial to perform component or system FDD such as the application researched in [29]. As well as, being applicable for creating and testing feature extraction and selection algorithms in [30].…”

Section: Condition Monitoring Of Hydraulic Test Rig Data Setmentioning

confidence: 99%

A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest

Mallak¹,

Fathi²

2020

Preprint

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In this work, A hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, spotting the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using 3-fold cross-validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. Followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data, in order to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, as well as the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time series multivariate sensor readings.

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Automatic feature extraction and selection for classification of cyclical time series data

Cited by 40 publications

References 0 publications

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest

A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest

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