Fault diagnosis and location of the aero-engine hydraulic pipeline based on Kalman filter

Li, Zhezhu; Gao, Peixin; Zhao, Dazhe; Liu, Jiren

doi:10.1177/1687814017742811

Cited by 12 publications

(5 citation statements)

References 23 publications

(31 reference statements)

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“…2 (Li et al 2017). Firstly, decision trees are constructed by using the training samples, and the random forest is formed to obtain the classification rules.…”

Section: Diagnosis Algorithm Based On Random Forestmentioning

confidence: 99%

Research on Comparison of Different Algorithms in Diagnosing Faults of Aircraft Engines

Liao

2021

J. Aerosp. Technol. Manag.

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For the aircraft, the engine is its core component. Once the engine fails, the flight safety will be seriously affected; therefore, it is necessary to diagnose the failure in time. This paper briefly introduced three aircraft engine fault diagnosis algorithms based on support vector machine (SVM), random forest, and particle swarm optimization-back-propagation (PSO-BP) and carried out a simulation experiment on the performance of the three algorithms in MATLAB software. The results showed that the PSO-BPbased diagnosis algorithm had the highest recognition accuracy and the SVM-based diagnosis algorithm had the lowest, both for artificial fault data and real fault data. The PSO-BP-based diagnosis algorithm took the least average recognition time, and the SVM-based diagnosis algorithm took the longest time.

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“…2 (Li et al 2017). Firstly, decision trees are constructed by using the training samples, and the random forest is formed to obtain the classification rules.…”

Section: Diagnosis Algorithm Based On Random Forestmentioning

confidence: 99%

Research on Comparison of Different Algorithms in Diagnosing Faults of Aircraft Engines

Liao

2021

J. Aerosp. Technol. Manag.

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“…In recent years, to avoid the vibration failure of the aero-engine pipeline system, a few research studies have been conducted on the mechanical parameters and the pipeline model of the aero-engine. For example, Zhezhu Li et al [5] proposed a crack fault diagnosis method for aero-hydraulic pipelines based on HHT. Hu Ding et al [6][7][8] established a nonlinear coupled dynamics model for the hydraulic pipeline based on the Timoshenko beam.…”

Section: Introductionmentioning

confidence: 99%

A Novel 1D-Convolutional Spatial-Time Fusion Strategy for Data-Driven Fault Diagnosis of Aero-Hydraulic Pipeline Systems

Yang

Wang

et al. 2023

Mathematics

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Intelligent diagnosis of faults in an aero-hydraulic pipeline is important for condition monitoring of its systems. However, there are no more qualitative formulas or feature indicators to describe the faults of aero-hydraulic pipelines because of the complexity and diversity of aero-hydraulic pipeline systems, which leads to a very complex pipeline fault mechanism. In addition, although it is well known that the expression of interpretable and representable pipeline intelligent diagnosis models with pipeline fault characteristics are buried in high background noise and strong noise disturbance conditions in practical industrial scenarios, this has yet to be discussed. Inspired by the demand, this paper proposes a novel diagnosis strategy: the 1D-convolutional space-time fusion strategy for aero-engine hydraulic pipelines. Firstly, by optimizing the convolutional neural network and using it to design a one-dimensional convolutional neural network (1DCNN) with a wide input scale to expand the input field of perception, thereby obtaining more comprehensive spatial information of the pipeline data, which can effectively extract richer short sequence features. Secondly, a network of bidirectional gated recurrent Unit (Bi-GRU) is proposed, which integrates a short sequence of high-dimensional features for temporal information fusion, resulting in a certain degree of avoiding memory loss and gradient dispersion caused by the too-large step size. It is demonstrated that, for the noise signal and variable pressure signal, the fault identification accuracy approximated 95.9%, proving the proposed strategy’s robustness. By comparing with the other five methods, the proposed strategy has the ability to identify 10 different fault states in the aero-hydraulic pipeline with higher accuracy.

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“…22 Consequently, several damage prevention studies focusing on pipelines have been conducted across diverse industries. These studies involve autoregressive-moving-average-based vibration characteristic monitoring and analysis to diagnose the condition of subsea pipelines, 23 monitoring rapid water pressure fluctuations that cause damage to waterworks pipelines, 24 monitoring hydraulic pipe damage for aircraft engines using fiber Bragg grating sensors and Kalman filters, 25 and classifying convolutional neural network-based leakage signal for pipe damage monitoring. 26 As indicated in these examples, the primary goal of these studies was to improve the accuracy of leakage detection or prevent damage through damage source detection.…”

Section: Introductionmentioning

confidence: 99%

Source location and anomaly detection for damage identification of buried pipelines using kurtosis-based transfer function

Lee,

Park,

Yoon

2023

Structural Health Monitoring

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The failure of buried pipelines can lead to serious consequences such as explosions, environmental pollution, settlement, as well as economic loss. To prevent these outcomes, it is crucial to identify the causes of failure and monitor their signs. One of the main causes of failure is unexpected third-party interference (TPI), which is particularly challenging to detect. Regarding to this issue, this study proposes a new algorithm for monitoring impact damage, which can be used for prompt response and damage prevention. The algorithm is integrated into the system using two approaches. The first approach focuses on detecting the location of the damage (referred to as source location). A kurtosis-based transfer function was newly proposed to selecting the optimal frequency band for time-difference-of-arrival based source location, resulting in accurate pinpointing of damage, even in a noisy environment. The second approach is used to determine whether impact damage has actually occurred by observing newly suggested features in both the time and frequency domains (referred to as anomaly detection). These features evaluate the presence of damage and the similarity between signals. As a result, it was evaluated that the field applicability was higher than that of conventional methods, and the superiority of the proposed method was also verified through field experiments. The method proposed in this study is expected to enable immediate response when the integrity of the buried pipelines is on the line of failure due to TPI.

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Fault diagnosis and location of the aero-engine hydraulic pipeline based on Kalman filter

Cited by 12 publications

References 23 publications

Research on Comparison of Different Algorithms in Diagnosing Faults of Aircraft Engines

Research on Comparison of Different Algorithms in Diagnosing Faults of Aircraft Engines

A Novel 1D-Convolutional Spatial-Time Fusion Strategy for Data-Driven Fault Diagnosis of Aero-Hydraulic Pipeline Systems

Source location and anomaly detection for damage identification of buried pipelines using kurtosis-based transfer function

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