Fault Detection of Aircraft System with Random Forest Algorithm and Similarity Measure

Lee, Sanghyuk; Park, Wookje; Jung, Sikhang

doi:10.1155/2014/727359

Cited by 20 publications

(9 citation statements)

References 14 publications

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“…In [11], we see another example of using RF in a solo fashion to achieve FDD in industrial sensor systems applied to unmanned aircraft vehicles. This study deployed a brilliant interpretation of RF and feature importance to extract a weighted similarity metric based on the data priority represented by RF.…”

Section: Related Work: Rf For Fdd In Industry 40mentioning

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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Section: Related Work: Rf For Fdd In Industry 40mentioning

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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show abstract

“…In [11], another example of using RF in a solo fashion to achieve FDD in industrial sensor systems applied to unmanned aircraft vehicle. This study deployed a brilliant interpretation of RF and feature importance, to extract a weighted similarity metric based on the data priority represented by RF.…”

Section: Related Work: Rf For Fdd In Industry 40mentioning

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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“…The situations lead to some challenges for fault diagnosis in CPSs: the first one is the complexity of modern industrial systems sets’ obstacles for devising a practical model, and the features extracted from multi-dimensional sensor data depend too much on the experts’ knowledge; thus, handcrafted features influence the performance of fault diagnosis; the second one is the imbalance between faulty and normal samples, which has a serious impact on the performance of classifiers, for the reason that learning algorithms without consideration of imbalance tend to be overwhelmed by the majority class and ignore the minority class [ 2 ]. Some notable work [ 3 , 4 , 5 , 6 , 7 ] has been studied for industrial fault diagnosis based on machine learning algorithms; however, they have not taken the restrictions of these challenges into consideration. Consequently, a new model with automatic feature extraction and class-imbalance learning mechanism is necessary for the fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Weighted Deep Representation Learning Model for Imbalanced Fault Diagnosis in Cyber-Physical Systems

Yang

Lin

et al. 2018

Sensors

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Predictive maintenance plays an important role in modern Cyber-Physical Systems (CPSs) and data-driven methods have been a worthwhile direction for Prognostics Health Management (PHM). However, two main challenges have significant influences on the traditional fault diagnostic models: one is that extracting hand-crafted features from multi-dimensional sensors with internal dependencies depends too much on expertise knowledge; the other is that imbalance pervasively exists among faulty and normal samples. As deep learning models have proved to be good methods for automatic feature extraction, the objective of this paper is to study an optimized deep learning model for imbalanced fault diagnosis for CPSs. Thus, this paper proposes a weighted Long Recurrent Convolutional LSTM model with sampling policy (wLRCL-D) to deal with these challenges. The model consists of 2-layer CNNs, 2-layer inner LSTMs and 2-Layer outer LSTMs, with under-sampling policy and weighted cost-sensitive loss function. Experiments are conducted on PHM 2015 challenge datasets, and the results show that wLRCL-D outperforms other baseline methods.

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Fault Detection of Aircraft System with Random Forest Algorithm and Similarity Measure

Cited by 20 publications

References 14 publications

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

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

A Weighted Deep Representation Learning Model for Imbalanced Fault Diagnosis in Cyber-Physical Systems

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