A sliding window feature extraction method for rotating machinery based on the lifting scheme

Duan, Chang‐Kui; He, Zhengjia; Jiang, Hongkai

doi:10.1016/j.jsv.2006.07.037

Cited by 27 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Inspired by the sliding window feature extraction [23], the cumulative amplitude of the sliding window can be used to extract the features. However, for the cumulative amplitude of the sliding window, the features are usually extracted according to thresholds and the thresholds are generally obtained after many attempts in actual experiments, the process is time-consuming.…”

Section: Feature Extractionmentioning

confidence: 99%

Privacy Leakage in Mobile Sensing: Your Unlock Passwords Can Be Leaked through Wireless Hotspot Functionality

Zhang

Zheng

Tang

et al. 2016

Mobile Information Systems

View full text Add to dashboard Cite

Mobile sensing has become a new style of applications and most of the smart devices are equipped with varieties of sensors or functionalities to enhance sensing capabilities. Current sensing systems concentrate on how to enhance sensing capabilities; however, the sensors or functionalities may lead to the leakage of users’ privacy. In this paper, we present WiPass, a way to leverage the wireless hotspot functionality on the smart devices to snoop the unlock passwords/patterns without the support of additional hardware. The attacker can “see” your unlock passwords/patterns even one meter away. WiPass leverages the impacts of finger motions on the wireless signals during the unlocking period to analyze the passwords/patterns. To practically implement WiPass, we are facing the difficult feature extraction and complex unlock passwords matching, making the analysis of the finger motions challenging. To conquer the challenges, we use DCASW to extract feature and hierarchical DTW to do unlock passwords matching. Besides, the combination of amplitude and phase information is used to accurately recognize the passwords/patterns. We implement a prototype of WiPass and evaluate its performance under various environments. The experimental results show that WiPass achieves the detection accuracy of 85.6% and 74.7% for passwords/patterns detection in LOS and in NLOS scenarios, respectively.

show abstract

Section: Feature Extractionmentioning

confidence: 99%

Privacy Leakage in Mobile Sensing: Your Unlock Passwords Can Be Leaked through Wireless Hotspot Functionality

Zhang

Zheng

Tang

et al. 2016

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…The statistical probability distributions of the mean, variance and the second-order statistical moment of the scaling coefficients at first, second and third scale were calculated and used to classify the different piston conditions. (Chendong et al, 2007) proposed a new sliding window feature extraction method based on the lifting scheme for extracting transient impacts from signals. A sliding window -designed according to the revolution cycle of rotating machinery-is applied to process the detail signals.…”

Section: Motorsmentioning

confidence: 99%

Utilising the Wavelet Transform in Condition-Based Maintenance: A Review with Applications

Λούτας¹,

Kostopoulos²

2012

Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology

View full text Add to dashboard Cite

“…The new wavelet generation is called lifting scheme or the second generation wavelets and consist on an entirely spatial-domain interpretation of the wavelet transform [1,2,11,12].The lifting scheme decomposes the filters into a finite sequence of filtering steps [12]. This decomposition consists on splitting the original signal into two subsets defined by the even and odd index signal samples, and then gradually a new wavelet coefficients set is built [3,5,11,16].The decomposition is held in three steps ( Fig.…”

Section: Lifting Schemementioning

confidence: 99%

“…The discrete wavelet transform (DWT) is used in a large number of signal and image processing applications [1,2,3,4], due to its good localization both in time and frequency space [20]. Therefore, it is the basis of the new standard JPEG2000.…”

Section: Introductionmentioning

confidence: 99%

DSP implementation and performances evaluation of 1D and 2D DWT using the lifting scheme

Gazzah

Souani

Besbes

2008

2008 3rd International Design and Test Workshop

View full text Add to dashboard Cite

Where T L and T H are the number of coefficients of the low-pass and high-pass filter respectively.The inverse transform is done by conducting the sequence in reverse order: begin with the upsampling, filtering through two synthesis filters (namely h and g) and sum the results of the two filters, as shown in Figure 1.To vary resolution for the direct DWT, the low-pass filtered and decimated output YL is recursively Discrete direct wavelet transform can be implemented as a set of filter banks using two parallel filters, a highpass and a low-pass filter, each filter is followed by a downsampling-by-two [7,8,9]. One decomposition level of 1D DWT, needs that the input signal (x) is filtered by two parallel filters, a high-pass filter (g ) and a low-pass filter (h). The two filter outputs are then downsampled by a factor of two and produce low-pass (yL ) and highpass (yH) subb~d outputs as shown in Figure 1. The parallel filters (h and g) represent the analysis filter bank [8]. The output signals are computed as follows:To do the LS-based DWT computations, the source image stored in the off-chip memory need to be loaded from the off-chip to on-chip memories. The execution time for performing both 5/3 and 9/7 is measured for ID and 2D-DWT for different number of level, depending on on-chip and off-chip memory.The paper is organized as follows: In section 2, a brief presentation of the DWT is shown. In section 3, background of the lifting scheme is presented. Implantation of test programs are presented in section 4, where the execution performances of the 1D and then of the 2D wavelet transform are evaluated. The images are decomposed by the direct wavelet transform and then reconstructed by the inverse wavelet transform. The PSNR factors are also evaluated using MATLAB and the DSP implementations. Conclusions are drawn in the end. II. BRIEF PRESENTATION OF DWTAbstract-This paper presents the implementation of a lifting scheme-based DWT architecture on a digital signal processor (DSP) (TMS320C6713) core and the simulation using MATLAB. The 5/3 and 9/7 wavelet filters used in JPEG 2000 are both implanted and executed by the DSP processor for comparisons. The algorithms proposed are optimized at source code level and memory usage. The execution time for performing both DWTs is measured for ID and 2D-DWT for different number of level, depending on on-chip and off-chip memory.

show abstract

A sliding window feature extraction method for rotating machinery based on the lifting scheme

Cited by 27 publications

References 11 publications

Privacy Leakage in Mobile Sensing: Your Unlock Passwords Can Be Leaked through Wireless Hotspot Functionality

Privacy Leakage in Mobile Sensing: Your Unlock Passwords Can Be Leaked through Wireless Hotspot Functionality

Utilising the Wavelet Transform in Condition-Based Maintenance: A Review with Applications

DSP implementation and performances evaluation of 1D and 2D DWT using the lifting scheme

Contact Info

Product

Resources

About