An integrated approach for aircraft turbofan engine fault detection based on data mining techniques

Gharoun, Hassan; Keramati, Abbas; Nasiri, Mohammad Mahdi; Azadeh, A.

doi:10.1111/exsy.12370

Cited by 19 publications

(10 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, Gerum et al (2019) apply a recurrent neural network to study rail and geometry defects in order to schedule maintenance interventions. The artificial neural network deployed by Bangalore and Tjernberg (2015), instead, serves as a fault detector, as well as the radial basis function neural network employed in Gharoun et al (2019), that also compare its performance with the adaptive neuro-fuzzy inference. Kusiak and Verma (2012), though a neural network, address both the fault prediction and the normal behavior modeling of wind turbines' bearings.…”

Section: Literature Reviewmentioning

confidence: 99%

Data-driven approach to predict the sequence of component failures: a framework and a case study on a process industry

Antomarioni

Ciarapica

2022

IJQRM

View full text Add to dashboard Cite

PurposeThe research approach is based on the concept that a failure event is rarely random and is often generated by a chain of previous events connected by a sort of domino effect. Thus, the purpose of this study is the optimal selection of the components to predictively maintain on the basis of their failure probability, under budget and time constraints.Design/methodology/approachAssets maintenance is a major challenge for any process industry. Thanks to the development of Big Data Analytics techniques and tools, data produced by such systems can be analyzed in order to predict their behavior. Considering the asset as a social system composed of several interacting components, in this work, a framework is developed to identify the relationships between component failures and to avoid them through the predictive replacement of critical ones: such relationships are identified through the Association Rule Mining (ARM), while their interaction is studied through the Social Network Analysis (SNA).FindingsA case example of a process industry is presented to explain and test the proposed model and to discuss its applicability. The proposed framework provides an approach to expand upon previous work in the areas of prediction of fault events and monitoring strategy of critical components.Originality/valueThe novel combined adoption of ARM and SNA is proposed to identify the hidden interaction among events and to define the nature of such interactions and communities of nodes in order to analyze local and global paths and define the most influential entities.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Data-driven approach to predict the sequence of component failures: a framework and a case study on a process industry

Antomarioni

Ciarapica

2022

IJQRM

View full text Add to dashboard Cite

show abstract

“…For system fault diagnosis and prediction problems using data-driven methods [11][12][13][14], oneclass SVM (OCSVM) is a typical classification tool, which can identify the known object class(normal sample) and the unknown object class(novel sample). As a popular method, a lot of improved OCSVM algorithms are studied.…”

Section: Introductionmentioning

confidence: 99%

A Fast Small-Sample Modeling Method for Precision Inertial Systems Fault Prediction and Quantitative Anomaly Measurement

Wang¹,

Cai²

2022

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Inertial system platforms are a kind of important precision devices, which have the characteristics of difficult acquisition for state data and small sample scale. Focusing on the model optimization for data-driven fault state prediction and quantitative degree measurement, a fast small-sample supersphere one-class SVM modeling method using support vectors pre-selection is systematically studied in this paper. By theorem-proving the irrelevance between the model's learning result and the non-support vectors (NSVs), the distribution characters of the support vectors are analyzed. On this basis, a modeling method with selected samples having specific geometry character from the training sets is also proposed. The method can remarkably eliminate the NSVs and improve the algorithm's efficiency. The experimental results testify that the scale of training samples and the modeling time consumption both give a sharply decrease using the support vectors pre-selection method. The experimental results on inertial devices also show good fault prediction capability and effectiveness of quantitative anomaly measurement.

show abstract

“…However, this would require certain degrees of penetration into the engine system or an installation of inspection devices beforehand. Gharoun [5] innovatively utilizes data mining techniques applied to existing flight data and draws connections between the engine exhaust gas temperature and environmental conditions. The investigating claims to be successful at identifying faults and proposes that there would be no need for technical knowledge on the interior of the engine, and therefore theoretically simple to apply.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Engine Audio Signal Analysis in Assisting Maintenance Inspections

Sakai

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

As the core component of modern commercial aircraft, turbofan engines have long been the center of focus in aircraft maintenance. Being subject to high temperatures and immense pressures causes problems for the engine components, such as the fan blades, as they are frequently burdened with the potential of overhaul and malfunction. Over many years, the industry has seen various methods of engine inspection and maintenance, ranging from manual inspection to computing large quantities of pre-existing data. Within, audio signal analysis has stood out as a productive, non-invasive method, with many alternate studies analyzing sound signals from components such as the combustion chamber. However, many of these methods, despite demonstrating good accuracy, are incredibly complex and require sophisticated apparatus. Therefore, this study begins by investigating the sound generation process of turbofan engines, especially how the features and form of the fan blade characterize its audio signals. This investigation proposes a solution that utilizes a fast Multi-Class Support Vector Machine (SVM) algorithm based on fan-blade-related audio signals from a perspective similar to the classification of music and images through supervised machine learning. Experimental results show that this fast Multi-Class SVM is more effective than traditional machine learning methods in its accuracy, F1-score, and other indicators.

show abstract

An integrated approach for aircraft turbofan engine fault detection based on data mining techniques

Cited by 19 publications

References 41 publications

Data-driven approach to predict the sequence of component failures: a framework and a case study on a process industry

Data-driven approach to predict the sequence of component failures: a framework and a case study on a process industry

A Fast Small-Sample Modeling Method for Precision Inertial Systems Fault Prediction and Quantitative Anomaly Measurement

Aircraft Engine Audio Signal Analysis in Assisting Maintenance Inspections

Contact Info

Product

Resources

About