scholarly journals Evaluation of ASTM D6424 standard for knock analysis using unleaded fuel candidates on a six cylinder aircraft engine

2021 ◽  
pp. 146808742110087
Author(s):  
Khashayar Ebrahimi ◽  
David Gordon ◽  
Pervez Canteenwalla ◽  
Charles R Koch
Keyword(s):  
Sampling Rate ◽  
Aircraft Engine ◽  
General Aviation ◽  
Aviation Fuel ◽  
Detection Methods ◽  
Discrete Wavelet ◽  
Current Standard ◽  
Pressure Signal ◽  
Engine Knock ◽  
Knock Detection

The ASTM D6424 standard is used for general aviation piston engine knock detection and is tested for unleaded fuel candidates. Issues are discussed regarding the identification of knocking cycles, filtering frequency bands, and the effects of down-sampling for this knock detection technique. The knock tests were performed on the Continental TSIO-520-VB engine at 12,000 ft for take-off and cruise conditions using three different fuels, the standard leaded 100LL avgas and two unleaded fuel candidates. The ASTM D6424 knock detection method has its own particular disadvantages, which are detailed and compared to other knock detection methods including the third derivative of pressure signal and discrete wavelet transform. Updates to the standard include a minimum sampling rate of 0.2 CAD. Additionally, the current standard does not contain recommendations for filtering the cylinder pressure which results in over detection of knocking cycles with the two new aviation fuel candidates tested. Recommendations are provided regarding the pressure signal processing prior to ASTM D6424 knock-characterization.

Embracing Collisions to Increase Fidelity of Sensing Systems with COTS Tags

2021 ◽  
Vol 5 (2) ◽  
pp. 1-20
Author(s):  
Jiaqi Xu ◽  
Wei Sun ◽  
Kannan Srinivasan
Keyword(s):  
Low Cost ◽  
Sampling Rate ◽  
Identification Algorithm ◽  
Current Standard ◽  
Tag Identification ◽  
Rfid Systems ◽  
Passive Tags ◽  
Sensing Systems ◽  
Channel Diversity ◽  
Channel Information

RFID techniques have been extensively used in sensing systems due to their low cost. However, limited by the structural simplicity, collision is one key issue which is inevitable in RFID systems, thus limiting the accuracy and scalability of such sensing systems. Existing anti-collision techniques try to enable parallel decoding without sensing based applications in mind, which can not operate on COTS RFID systems. To address the issue, we propose COFFEE, which enables parallel channel estimation of COTS passive tags by harnessing the collision. We revisit the physical layer design of current standard. By exploiting the characteristics of low sampling rate and channel diversity of RFID tags, we separate the collided data and extract the channels of the collided tags. We also propose a tag identification algorithm which explores history channel information and identify the tags without decoding. COFFEE is compatible with current COTS RFID standards which can be applied to all RFID-based sensing systems without any modification on tag side. To evaluate the real world performance of our system, we build a prototype and conduct extensive experiments. The experimental results show that we can achieve up to 7.33x median time resolution gain for the best case and 3.42x median gain on average.

Feature weighting network for aircraft engine defect detection

2020 ◽  
Vol 18 (03) ◽  
pp. 2050012
Author(s):  
Liqiong Chen ◽  
Lian Zou ◽  
Cien Fan ◽  
Yifeng Liu
Keyword(s):  
Defect Detection ◽  
State Of The Art ◽  
Aircraft Engine ◽  
Air Transportation ◽  
Feature Weighting ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Practical Applications ◽  
Feature Pyramid ◽  
New Feature

Automatic aircraft engine defect detection is a challenging but important task in industry which can ensure safe air transportation and flight. In this paper, we propose a fast and accurate feature weighting network (FWNet) to solve the problem of defect scale variation and improve detection accuracy. The framework is designed based on recent popular convolutional neural networks and feature pyramid. To further boost the representation power of the network, a new feature weighting module (FWM) was proposed to recalibrate the channel-wise attention and increase the weights of valid features. The model was trained and tested on a self-built dataset, which consisted of 1916 images and contained three defect types: ablation, crack and coating missing. Extensive experimental results verify the effectiveness of the proposed FWM and show that the proposed method can accurately detect engine defects of different scales and different locations. Our method obtains 89.4% mAP and can run at 6FPS, which surpasses other state-of-the-art detection methods and can quickly provide diagnostic basis for aircraft maintenance inspectors in practical applications.

Efficient and Adaptive Rotation Invariant Wavelet Transform

2003 ◽  
Vol 01 (03) ◽  
pp. 353-372
Author(s):  
Chi-Man Pun
Keyword(s):  
Wavelet Transform ◽  
Texture Classification ◽  
Wavelet Packet ◽  
Sampling Rate ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
Information Cost ◽  
Image Size ◽  
Rotation Invariant ◽  
Adaptive Wavelet

It is well known that the sensitivity to translations and orientations is a major drawback in 2D discrete wavelet transform (DWT). In this paper, we have proposed an effective scheme for rotation invariant adaptive wavelet packet transform. During decomposition, the wavelet coefficients are obtained by applying a polar transform (PT) followed by a row-shift invariant wavelet packet decomposition (RSIWPD). In the first stage, the polar transform generates a row-shifted image and is adaptive to the image size to achieve complete and minimum sampling rate. In the second stage, the RSIWPD is applied to the row-shifted image to generate rotation invariant but over completed subbands of wavelet coefficients. In order to reduce the redundancy and computational complexity, we adaptively select some subbands to decompose and form a best basis representation with minimal information cost with respect to an appropriate information cost function. With this best basis representation, the original image can be reconstructed easily by applying a row-shift invariant wavelet packet reconstruction (RSIWPR) followed by an inverse polar transform (IPT). In the experiments, we study the application of this representation for texture classification and achieve 96.5% classification accuracy.

STATISTICAL ENGINE KNOCK DETECTION

2007 ◽  
Vol 40 (10) ◽  
pp. 319-326
Author(s):  
Alexander Stotsky
Keyword(s):  
Engine Knock ◽  
Knock Detection

scholarly journals CIRCADA: Shiny Apps for Exploration of Experimental and Synthetic Circadian Time Series with an Educational Emphasis

2020 ◽  
Vol 35 (2) ◽  
pp. 214-222
Author(s):  
Lisa Cenek ◽  
Liubou Klindziuk ◽  
Cindy Lopez ◽  
Eleanor McCartney ◽  
Blanca Martin Burgos ◽  
...  
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Locomotor Activity ◽  
Body Temperature ◽  
Sampling Rate ◽  
Discrete Wavelet ◽  
Data Set ◽  
Maximum Entropy Spectral Analysis ◽  
Noise Characteristics ◽  
Synthetic Time Series

Circadian rhythms are daily oscillations in physiology and behavior that can be assessed by recording body temperature, locomotor activity, or bioluminescent reporters, among other measures. These different types of data can vary greatly in waveform, noise characteristics, typical sampling rate, and length of recording. We developed 2 Shiny apps for exploration of these data, enabling visualization and analysis of circadian parameters such as period and phase. Methods include the discrete wavelet transform, sine fitting, the Lomb-Scargle periodogram, autocorrelation, and maximum entropy spectral analysis, giving a sense of how well each method works on each type of data. The apps also provide educational overviews and guidance for these methods, supporting the training of those new to this type of analysis. CIRCADA-E (Circadian App for Data Analysis–Experimental Time Series) allows users to explore a large curated experimental data set with mouse body temperature, locomotor activity, and PER2::LUC rhythms recorded from multiple tissues. CIRCADA-S (Circadian App for Data Analysis–Synthetic Time Series) generates and analyzes time series with user-specified parameters, thereby demonstrating how the accuracy of period and phase estimation depends on the type and level of noise, sampling rate, length of recording, and method. We demonstrate the potential uses of the apps through 2 in silico case studies.

scholarly journals Application of a hygroscopicity tandem differential mobility analyzer for characterizing PM emissions in exhaust plumes from an aircraft engine burning conventional and alternative fuels

2018 ◽  
Vol 18 (23) ◽  
pp. 17029-17045 ◽  
Author(s):  
Max B. Trueblood ◽  
Prem Lobo ◽  
Donald E. Hagen ◽  
Steven C. Achterberg ◽  
Wenyan Liu ◽  
...  
Keyword(s):  
Alternative Fuels ◽  
Particle Diameter ◽  
Aircraft Engine ◽  
Aviation Fuel ◽  
Limited Information ◽  
Differential Mobility Analyzer ◽  
Differential Mobility ◽  
Fischer Tropsch ◽  
Hygroscopic Properties ◽  
Exhaust Plumes

Abstract. In the last several decades, significant efforts have been directed toward better understanding the gaseous and particulate matter (PM) emissions from aircraft gas turbine engines. However, limited information is available on the hygroscopic properties of aircraft engine PM emissions which play an important role in the water absorption, airborne lifetime, obscuring effect, and detrimental health effects of these particles. This paper reports the description and detailed lab-based performance evaluation of a robust hygroscopicity tandem differential mobility analyzer (HTDMA) in terms of hygroscopic properties such as growth factor (GF) and the hygroscopicity parameter (κ). The HTDMA system was subsequently deployed during the Alternative Aviation Fuel EXperiment (AAFEX) II field campaign to measure the hygroscopic properties of aircraft engine PM emissions in the exhaust plumes from a CFM56-2C1 engine burning several types of fuels. The fuels used were conventional JP-8, tallow-based hydroprocessed esters and fatty acids (HEFA), Fischer–Tropsch, a blend of HEFA and JP-8, and Fischer–Tropsch doped with tetrahydrothiophene (an organosulfur compound). It was observed that GF and κ increased with fuel sulfur content and engine thrust condition, and decreased with increasing dry particle diameter. The highest GF and κ values were found in the smallest particles, typically those with diameters of 10 nm.

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