scholarly journals Missing sensor data restoration for vibration sensors on a jet aircraft engine

Author(s):  
S. Narayanan ◽  
J.L. Vian ◽  
J.J. Choi ◽  
R.J. Marks ◽  
M.A. El-Sharkawi ◽  
...  
2021 ◽  
Author(s):  
Diederik van Binsbergen ◽  
Amir R. Nejad ◽  
Jan Helsen

Abstract This paper aims to analyze the feasibility of establishing a dynamic drivetrain model from condition monitoring measurements. In this study SCADA data and further sensor data is analyzed from a 1.5MW wind turbine, provided by the National Renewable Energy Laboratory. A multibody model of the drivetrain is made and simulation based sensors are placed on bearings to look at the possibility to obtain geometrical and modal properties from simulation based vibration sensors. Results show that the axial proxy sensor did not provide any usable system information due to its application purpose. SCADA data did not meet the Nyquist frequency and cannot be used to determine geometrical or modal properties. Strain gauges on the shaft can provide the shaft rotational frequency, while torque and angular displacement sensors can provide the torsional eigenfrequency of the system. Simulation based vibration sensors are able to capture gear mesh frequencies, harmonics, sideband frequencies and shaft rotational frequencies.


2021 ◽  
Author(s):  
Bradley Krough ◽  
Paul Corbitt ◽  
Lucia Cazares ◽  
James Masdea ◽  
David Scadden

Abstract Modern drill bits designs have become more efficient using static modelling, and in more advanced cases, time-based dynamic modelling. These methods have created improved cutting structures that fail rock more effectively, however, at-bit vibrations are difficult to estimate because of the high-frequency nature of the vibration and its proximity to typical vibration sensors. In conventional applications, vibration is not measured near the bit. A solution to capture this data on conventional assemblies and use the data in an actual bit design is presented in this paper with subsequent performance and vibration results. The relative efficiency, bit dull grading, and vibration performance are compared across these designs and explored in depth. This new generation of vibration tool fits inside the bit pin, enabling accurate at-bit vibration measurements by a suite of sensors. The tool includes a tri-axis accelerometer that measures lateral and axial acceleration, and gyro sensors to measure rpm and torsional acceleration. Together, these outputs combine with the rig surface data to have time- and depth-based vibration data in the context of the run. When used to quantify the dynamic model, this represents a modelling calibration that improves bit design performance. The lower-vibration environment created by the new bit design enables the operator to run increased parameters with a lower likelihood for measurement-while-drilling (MWD) failures, motor failures, and premature catastrophic bit failures leading to faster run times and less nonproductive time (NPT). These results also prove that meaningful bit design changes can take place more frequently than through traditional means, translating value to the operator in the form more successful BHA improvements and less drilling time. Using the new in-bit sensor in a baseline design to start the design cycle, a baseline mechanical specific energy (MSE) and vibration model was developed foot-by-foot. The worst areas of vibration were seen as the bit became dull in the lower section of the drilling interval. A new dull bit model was created in parallel to capture this section of data. A new design was proposed to Whiting Petroleum to improve both sharp and dull efficiency and vibration, and subsequently run with sensor in an offset well.


Author(s):  
Rolf F. Orsagh ◽  
Jeremy Sheldon ◽  
Christopher J. Klenke

Development of robust in-flight prognostics or diagnostics for oil wetted gas turbine engine components will play a critical role in improving aircraft engine reliability and maintainability. Real-time algorithms for predicting and detecting bearing and gear failures are currently being developed in parallel with emerging flight-capable sensor technologies including in-line oil debris/condition monitors, and vibration analysis MEMS. These advanced prognostic/diagnostic algorithms utilize intelligent data fusion architectures to optimally combine sensor data, with probabilistic component models to achieve the best decisions on the overall health of oil-wetted components. By utilizing a combination of health monitoring data and model-based techniques, a comprehensive component prognostic capability can be achieved throughout a components life, using model-based estimates when no diagnostic indicators are present and monitored features such as oil debris and vibration at later stages when failure indications are detectable. Implementation of these oil-wetted component prognostic modules will be illustrated in this paper using bearing and gearbox test stand run-to-failure data.


Author(s):  
Farid K. Moghadam ◽  
Amir R. Nejad

Abstract Drivetrain bearings are seen as the most common reason of the wind turbine drivetrain system failures and the consequent downtimes. In this study, the angular velocity error function is used for the condition monitoring of the bearings and gears in the wind turbine drivetrain. This approach benefits from using the sensor data and the dedicated communication network which already exist in the turbine for performance monitoring purposes. Minor required modification includes an additional moderate sampling frequency encoder without any need of adding an extra condition monitoring system. The additional encoder is placed on the low speed shaft and can also be used as the backup for the high speed shaft encoder which is critical for turbine control purposes. A theory based on the variations of the energy of response around the defect frequency is suggested to detect abnormalities in the drivetrain operation. The proposed angular velocity based method is compared with the classical vibration-based detection approach based on axial/radial acceleration data, for the faults initiated by different types of excitation sources. The method is experimentally evaluated using the data obtained from the encoders and vibration sensors of an operational wind turbine.


2021 ◽  
Vol 11 (10) ◽  
pp. 4318
Author(s):  
Longhuan Cheng ◽  
Jiantao Lu ◽  
Shunming Li ◽  
Rui Ding ◽  
Kun Xu ◽  
...  

Combined with other signal processing methods, related algorithms are widely used in the diagnosis and identification of rotor faults. In order to solve the problem that the vibration signal of a single sensor is too single, a new multi-source vibration signal fusion method is proposed. This method explores the correlation between vibration sensors at different locations by using multiple cross-correlations of spatial locations. First, wavelet noise reduction and linear normalization are used to process the original data. Then, the signal energy correlation function between the sensors is established, and the adaptive weight is obtained. Finally, the data fusion result is obtained. Taking rotor bearing and gear failures at different speeds as an example, the data of three vibration sensors at different positions are fused using the spatio-temporal multiple correlation fusion method (STMF). Through the intelligent fault diagnosis method stacked auto encoder (SAE), compared with single sensor data, average weighted fusion data and neural network fusion data, STMF method can reach a diagnosis accuracy of more than 94% at 700 rpm, 900 rpm and 1100 rpm. It is concluded that the result of the STMF method is more effective and superior.


Author(s):  
Bing Li ◽  
Yong-Ping Zhao

Lacking of the management of simultaneous fault is one of the limitations of condition monitoring for a gas turbine, which is critical for the safety and decision-making of aircraft operation. To this end, this paper employed a multi-label (ML) learning strategy to address the simultaneous fault issues. Moreover, a feature selection algorithm is proposed, which is based on the viewpoint that different class labels might be distinguished by certain specific characteristics of their own. The proposed algorithm achieves the goal of label-specific feature selection by iteratively optimizing the weight reconstruction matrix, and the learned label-specific features for the corresponding label can be used for multi-label classification. As thus, sensor data for different components of aircraft engines can be determined by the proposed algorithm to deal with the simultaneous fault diagnosis. Finally, comprehensive experiments on the benchmark data sets of multi-label learning validate the advantages and feasibility of the presented approaches, and the effectiveness of their application to simultaneous fault diagnosis of aircraft engines is also proved by extensive experiments.


Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3301
Author(s):  
Wei Jiang ◽  
Yanhe Xu ◽  
Yahui Shan ◽  
Han Liu

As the core component and main power source for aircrafts, the reliability of an aero engine is vital for the security operation of aircrafts. Degradation tendency measurement on an engine can not only improve its safety, but effectively reduce the maintenance costs. In this paper, a hybrid method using multi-sensor data based on fast ensemble empirical mode decomposition permutation entropy (FEEMD-PE) and regularized extreme learning machine (RELM), systematically blending the signal processing technology and trend prediction approach, is proposed for aircraft engine degradation tendency measurement. Firstly, a synthesized degradation index was designed utilizing multi-sensor data and a data fusion technique to evaluate the degradation level of the engine unit. Secondly, in order to eliminate the irregular data fluctuation, FEEMD was employed to efficiently decompose the constructed degradation index series. Subsequently, considering the complexity of intrinsic mode functions (IMFs) obtained through sequence decomposition, a permutation entropy-based reconstruction strategy was innovatively developed to generate the refactored IMFs (RIMFs), which have stronger ability for describing the degradation states and contribute to improving the prediction accuracy. Finally, RIMFs were used as the inputs of the RELM model to measure the degradation tendency. The proposed method was applied to the degradation tendency measurement of aircraft engines. The results confirm the effectiveness and superiority of the proposed method, and it is more suitable for actual applications compared with other existing approaches.


Author(s):  
Xiao Hu ◽  
Neil Eklund ◽  
Kai Goebel

Accurate and timely detection and identification of aircraft engine faults is critical to keeping the engine and aircraft in a healthy operating state. Early detection of faults increases the window of opportunity to schedule maintenance actions both at a convenient time and before the fault progresses and causes equipment downtime and secondary damage to the system. Typically, diagnostic models are built using parametric sensor data to infer the state of the system. However, recording and collecting this data is costly, and it is generally limited to a few snapshots over the course of a flight for commercial aircraft. Another way to recognize faults is through the use of built-in tests that produce error log messages. These tests produce data that is less information rich, but provide insight over the course of the entire flight. Each data source provides a different perspective of the state of the system. Therefore, it may be advantageous to combine information from parametric and nonparametric sources to improve fault diagnosis in terms of accuracy and timeliness of diagnosis. In this paper, we investigate integrating parametric sensor data and nonparametric information in fault diagnosis, specifically the way to parameterize nonparametric information for use in diagnostic models that accept only parametric data (e.g., most machine learning techniques). Results from high bypass commercial engines are presented.


Author(s):  
M. Larsen ◽  
R.G. Rowe ◽  
D.W. Skelly

Microlaminate composites consisting of alternating layers of a high temperature intermetallic compound for elevated temperature strength and a ductile refractory metal for toughening may have uses in aircraft engine turbines. Microstructural stability at elevated temperatures is a crucial requirement for these composites. A microlaminate composite consisting of alternating layers of Cr2Nb and Nb(Cr) was produced by vapor phase deposition. The stability of the layers at elevated temperatures was investigated by cross-sectional TEM.The as-deposited composite consists of layers of a Nb(Cr) solid solution with a composition in atomic percent of 91% Nb and 9% Cr. It has a bcc structure with highly elongated grains. Alternating with this Nb(Cr) layer is the Cr2Nb layer. However, this layer has deposited as a fine grain Cr(Nb) solid solution with a metastable bcc structure and a lattice parameter about half way between that of pure Nb and pure Cr. The atomic composition of this layer is 60% Cr and 40% Nb. The interface between the layers in the as-deposited condition appears very flat (figure 1). After a two hour, 1200 °C heat treatment, the metastable Cr(Nb) layer transforms to the Cr2Nb phase with the C15 cubic structure. Grain coarsening occurs in the Nb(Cr) layer and the interface between the layers roughen. The roughening of the interface is a prelude to an instability of the interface at higher heat treatment temperatures with perturbations of the Cr2Nb grains penetrating into the Nb(Cr) layer.


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