Feedback on the Surveillance 8 challenge: Vibration-based diagnosis of a Safran aircraft engine

Antoni, Jérôme; Griffaton, Julien; André, Hugo; Avendaño-Valencia, Luis David; Bonnardot, Frédéric; Cardona-Morales, Oscar; Castellanos-Domínguez, Germán; Daga, Alessandro Paolo; Leclère, Quentin; Vicuña, Cristián Molina; Acuña, David Quezada; Ompusunggu, Agusmian Partogi; Sierra-Alonso, Edgar F.

doi:10.1016/j.ymssp.2017.01.037

Cited by 93 publications

(36 citation statements)

References 22 publications

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“…In this case, the data is the aircraft engine vibration provided by [27]. The signal represents a slow acceleration progress from idle to full power as displayed in Fig.…”

Section: Aircraft Engine Datamentioning

confidence: 99%

Tacholess Rotational Speed Estimation via The Integration of Continuous Wavelet Transform and Time-Dependent Autoregression

Zhang

Liu

Zheng

et al. 2020

Preprint

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Speed estimation is crucial to monitor the conditions of rotational machinery. Most speed measurements are carried out by installing encoders or tachometers inside the machines. In many cases, such method could be cumbersome or even inaccessible. This paper proposes a vibration-based speed estimation method. The vibration sensors are often cheaper and easier to install than angle encoders. In the proposed method, the continuous wavelet transform (CWT) is used as a preprocessing technique to extract the signal of importance. Then, the time-varying autoregressive (TAR) model is applied to analyze the rotational frequency. Additionally, the paper presents a fast algorithm for implementation. The proposed method is validated by both synthetic and empirical data.

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“…In this case, the data is the aircraft engine vibration provided by [27]. The signal represents a slow acceleration progress from idle to full power as displayed in Fig.…”

Section: Aircraft Engine Datamentioning

confidence: 99%

Tacholess Rotational Speed Estimation via The Integration of Continuous Wavelet Transform and Time-Dependent Autoregression

Zhang

Liu

Zheng

et al. 2020

Preprint

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“…Spectral lines, such as the gear-mesh frequency, are known to be sensitive to damage and are then suitable features. More sophisticated signal processing techniques are currently available (e.g., see [5][6][7][8][9][10]) to highlight the signal of interest with respect to the noise (i.e., to increase the signal-to-noise ratio-to de-noise), to compensate for the transmission path from the source to the sensor and to isolate the different sources to enhance their contribution (e.g., Blind Source Separation). These algorithms can be very effective, but in general, are not ready for working independently from human supervision.…”

Section: Featuresmentioning

confidence: 99%

Machine Vibration Monitoring for Diagnostics through Hypothesis Testing

Daga

Garibaldi

2019

Information

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Nowadays, the subject of machine diagnostics is gathering growing interest in the research field as switching from a programmed to a preventive maintenance regime based on the real health conditions (i.e., condition-based maintenance) can lead to great advantages both in terms of safety and costs. Nondestructive tests monitoring the state of health are fundamental for this purpose. An effective form of condition monitoring is that based on vibration (vibration monitoring), which exploits inexpensive accelerometers to perform machine diagnostics. In this work, statistics and hypothesis testing will be used to build a solid foundation for damage detection by recognition of patterns in a multivariate dataset which collects simple time features extracted from accelerometric measurements. In this regard, data from high-speed aeronautical bearings were analyzed. These were acquired on a test rig built by the Dynamic and Identification Research Group (DIRG) of the Department of Mechanical and Aerospace Engineering at Politecnico di Torino. The proposed strategy was to reduce the multivariate dataset to a single index which the health conditions can be determined. This dimensionality reduction was initially performed using Principal Component Analysis, which proved to be a lossy compression. Improvement was obtained via Fisher's Linear Discriminant Analysis, which finds the direction with maximum distance between the damaged and healthy indices. This method is still ineffective in highlighting phenomena that develop in directions orthogonal to the discriminant. Finally, a lossless compression was achieved using the Mahalanobis distance-based Novelty Indices, which was also able to compensate for possible latent confounding factors. Further, considerations about the confidence, the sensitivity, the curse of dimensionality, and the minimum number of samples were also tackled for ensuring statistical significance. The results obtained here were very good not only in terms of reduced amounts of missed and false alarms, but also considering the speed of the algorithms, their simplicity, and the full independence from human interaction, which make them suitable for real time implementation and integration in condition-based maintenance (CBM) regimes.

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“…On account of the developments, recently, many international and even domestic flights are preferred as an alternative means of transportation compared to road transport [10]. However, this new habit causes challenges from environmental aspects such as high noise levels [2]. Environmental concerns and statutory regulations push airlines to operate low noise aircraft at takeoff, especially during the night flights [16].…”

Section: Introductionmentioning

confidence: 99%

“…This paper aims to create an initial knowledge environment for designers by using trade-off curves in order to identify a set of possible design solutions from the previous projects of an aircraft jet engine [19]. Having such a designset provides designers with sufficient knowledge during the SBCE applications of achieving a final optimized solution, which is expected to have the lowest noise level available in the market [2]. This paper consists of different research steps.…”

Section: Introductionmentioning

confidence: 99%

Creating Knowledge Environment during Lean Product Development Process of Jet Engine

Araci¹,

Al-Ashaab²,

Tariq³

et al. 2020

IJACSA

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Organizations invest intense resources in their product development processes. This paper aims to create a knowledge environment using trade-off curves during the early stages of the set-based concurrent engineering (SBCE) process of an aircraft jet engine for a reduced noise level at takeoff. Data is collected from a range of products in the same family as the jet engine. Knowledge-based trade-off curves are used as a methodology to create and visualize knowledge from the collected data. Findings showed that this method provides designers with enough confidence to identify a set of design solutions during the SBCE applications.

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Feedback on the Surveillance 8 challenge: Vibration-based diagnosis of a Safran aircraft engine

Cited by 93 publications

References 22 publications

Tacholess Rotational Speed Estimation via The Integration of Continuous Wavelet Transform and Time-Dependent Autoregression

Tacholess Rotational Speed Estimation via The Integration of Continuous Wavelet Transform and Time-Dependent Autoregression

Machine Vibration Monitoring for Diagnostics through Hypothesis Testing

Creating Knowledge Environment during Lean Product Development Process of Jet Engine

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