Fault detection of slow speed rolling element bearing with noise removal techniques

Rolling Element Bearings (REBs) are vital components in rotating machineries for providing rotating motion. In slow speed rotating machines, bearings are normally subjected to heavy static loads and a catastrophic failure can cause enormous disruption to production and human safety. Due to its low operating speed the impact energy generated by the rotating elements on the defective components is not sufficient to produce a detectable vibration response. This is further aggravated by the inability of general measuring instruments to detect and process the weak signals at the initiation of the defect accurately. Furthermore, the weak signals are often corrupted by background noise. This is a serious problem faced by maintenance engineers today and the inability to detect an incipient failure of the machine can significantly increases the risk of functional failure and costly downtime. This paper presents the application of noise removal techniques for enhancing the detection capability for slow speed REB condition monitoring. Blind deconvolution (BD) and adaptive line enhancer (ALE) are compared to evaluate their performance in enhancing the source signal with consequential removal of background noise. In the experimental study, incipient defects were seeded on a number of roller bearings and the signals were acquired using acoustic emission (AE) sensor. Kurtosis and modified peak ratio (mPR) were used to determine the detectability of signal corrupted by noise.

Multi-shot person re-identification via relational stein divergence

Person re-identification is particularly challenging due to significant appearance changes across separate camera views. In order to re-identify people, a representative human signature should effectively handle differences in illumination, pose and camera parameters. While general appearance-based methods are modelled in Euclidean spaces, it has been argued that some applications in image and video analysis are better modelled via non-Euclidean manifold geometry. To this end, recent approaches represent images as covariance matrices, and interpret such matrices as points on Riemannian manifolds. As direct classification on such manifolds can be difficult, in this paper we propose to represent each manifold point as a vector of similarities to class representers, via a recently introduced form of Bregman matrix divergence known as the Stein divergence. This is followed by using a discriminative mapping of similarity vectors for final classification. The use of similarity vectors is in contrast to the traditional approach of embedding manifolds into tangent spaces, which can suffer from representing the manifold structure inaccurately. Comparative evaluations on benchmark ETHZ and iLIDS datasets for the person re-identification task show that the proposed approach obtains better performance than recent techniques such as Histogram Plus Epitome, Partial Least Squares, and Symmetry-Driven Accumulation of Local Features.

Noise is the outsider to be expelled

This was a catalogue essay for the emerging Brisbane artist Sarah Byrne's exhibition Trenchmouth at MetroArts from 31st October until 21st November 2009. The essay contextualised her practice in the history of experimental soundart and discussed her methods of practice and approach to sound from the postion of a multi-discplinary artist.The essay also discussed the way in which her practice engages with and recontextualises camp, trash, and lo-fi aspects of popular culture

Metal Skin and the cinema of noise

In traditional communication and information theory, noise is the demon Other, an unwelcome disruption in the passage of information. Noise is "anything that is added to the signal between its transmission and reception that is not intended by the source...anything that makes the intended signal harder to decode accurately". It is in Michel Serres' formulation, the "third man" in dialogue who is always assumed, and whom interlocutors continually struggle to exclude. Noise is simultaneously a condition and a by-product of the act of communication, it represents the ever present possibility of disruption, interruption, misunderstanding. In sonic or musical terms noise is cacophony, dissonance. For economists, noise is an arbitrary element, both a barrier to the pursuit of wealth and a basis for speculation. For Mick (Jeremy Sims) and his mate Kev (Ben Mendelsohn) in David Caesar's Idiot Box (1996), as for Hando (Russell Crowe) and his gang of skinheads in Geoffrey Wright's Romper Stomper (1992), or Dazey (Ben Mendelsohn) and Joe (Aden Young) in Wright's Metal Skin (1994) and all those like them starved of (useful) information and excluded from the circuit - the information poor - their only option, their only point of intervention in the loop, is to make noise, to disrupt, to discomfort, to become Serres' "third man", "the prosopopoeia of noise" (5).

Investigations on road noise level spatial variability within a specially designed acoustic balcony

An investigation into the spatial distribution of road traffic noise levels on a balcony is conducted. A balcony constructed to a special acoustic design due to its elevation above an 8 lane motorway is selected for detailed measurements. The as-constructed balcony design includes solid parapets, side walls, ceiling shields and highly absorptive material placed on the ceiling. Road traffic noise measurements are conducted spatially using a five channel acoustic analyzer, where four microphones are located at various positions within the balcony space and one microphone placed outside the parapet at a reference position. Spatial distributions in both vertical and horizontal planes are measured. A theoretical model and prediction configuration is presented that assesses the acoustic performance of the balcony under existing traffic flow conditions. The prediction model implements a combined direct path, specular reflection path and diffuse reflection path utilizing image source and radiosity techniques. Results obtained from the prediction model are presented and compared to the measurement results. The predictions are found to correlate well with measurements with some minor differences that are explained. It is determined that the prediction methodology is acceptable to assess a wider range of street and balcony configuration scenarios.

Effects of different surfaces on the perception of prey-generated noise by the Indian false vampire bat Megaderma lyra.

The low- and high-frequency components of a rustling sound, created when prey (freshly killed frog) was jerkily pulled on dry and wet sandy floors and asbestos, were recorded and played back to individual Indian false vampire bats (Megaderma lyra). Megaderma lyra responded with flight toward the speakers and captured dead frogs, that were kept as reward. The spectral peaks were at 8.6, 7.1 and 6.8 kHz for the low-frequency components of the sounds created at the dry, asbestos and wet floors, respectively. The spectral peaks for the high-frequency sounds created on the respective floors were at 36.8,27.2 and 23.3 kHz. The sound from the dry floor was more intense than that of from the other two substrata. Prey movements that generated sonic or ultrasonic sounds were both sufficient and necessary for the bats to detect and capture prey. The number of successful prey captures was significantly greater for the dry floor sound, especially to its high-frequency components. Bat-responses were low to the wet floor and moderate to the asbestos floor sounds. The bats did not respond to the sound of unrecorded parts of the tape. Even though the bats flew toward the speakers when the prey generated sounds were played back and captured the dead frogs we cannot rule out the possibility of M. lyra using echolocation to localize prey. However, the study indicates that prey that move on dry sandy floor are more vulnerable to predation by M. lyra.

Modeling kinect sensor noise for improved 3D reconstruction and tracking

We contribute an empirically derived noise model for the Kinect sensor. We systematically measure both lateral and axial noise distributions, as a function of both distance and angle of the Kinect to an observed surface. The derived noise model can be used to filter Kinect depth maps for a variety of applications. Our second contribution applies our derived noise model to the KinectFusion system to extend filtering, volumetric fusion, and pose estimation within the pipeline. Qualitative results show our method allows reconstruction of finer details and the ability to reconstruct smaller objects and thinner surfaces. Quantitative results also show our method improves pose estimation accuracy. © 2012 IEEE.

ACE research vignette 037 : Bill Gates’ mug shot : Is there a secret link between rule-breaking, crime, and entrepreneurship?

This series of research vignettes is aimed at sharing current and interesting research findings from international entrepreneurship researchers. In this vignette, Dr. Martin Obschonka, considers the relationship between entrepreneurship and rule-breaking.

Corner detection in images under different noise levels

Corner detection has shown its great importance in many computer vision tasks. However, in real-world applications, noise in the image strongly affects the performance of corner detectors. Few corner detectors have been designed to be robust to heavy noise by now, partly because the noise could be reduced by a denoising procedure. In this paper, we present a corner detector that could find discriminative corners in images contaminated by noise of different levels, without any denoising procedure. Candidate corners (i.e., features) are firstly detected by a modified SUSAN approach, and then false corners in noise are rejected based on their local characteristics. Features in flat regions are removed based on their intensity centroid, and features on edge structures are removed using the Harris response. The detector is self-adaptive to noise since the image signal-to-noise ratio (SNR) is automatically estimated to choose an appropriate threshold for refining features. Experimental results show that our detector has better performance at locating discriminative corners in images with strong noise than other widely used corner or keypoint detectors.

A modified Density-Based Scanning Algorithm with Noise for spatial travel pattern analysis from Smart Card AFC data

Smart Card Automated Fare Collection (AFC) data has been extensively exploited to understand passenger behavior, passenger segment, trip purpose and improve transit planning through spatial travel pattern analysis. The literature has been evolving from simple to more sophisticated methods such as from aggregated to individual travel pattern analysis, and from stop-to-stop to flexible stop aggregation. However, the issue of high computing complexity has limited these methods in practical applications. This paper proposes a new algorithm named Weighted Stop Density Based Scanning Algorithm with Noise (WS-DBSCAN) based on the classical Density Based Scanning Algorithm with Noise (DBSCAN) algorithm to detect and update the daily changes in travel pattern. WS-DBSCAN converts the classical quadratic computation complexity DBSCAN to a problem of sub-quadratic complexity. The numerical experiment using the real AFC data in South East Queensland, Australia shows that the algorithm costs only 0.45% in computation time compared to the classical DBSCAN, but provides the same clustering results.

Real-time assessment of GNSS observation noise with single receivers

Stochastic modelling is critical in GNSS data processing. Currently, GNSS data processing commonly relies on the empirical stochastic model which may not reflect the actual data quality or noise characteristics. This paper examines the real-time GNSS observation noise estimation methods enabling to determine the observation variance from single receiver data stream. The methods involve three steps: forming linear combination, handling the ionosphere and ambiguity bias and variance estimation. Two distinguished ways are applied to overcome the ionosphere and ambiguity biases, known as the time differenced method and polynomial prediction method respectively. The real time variance estimation methods are compared with the zero-baseline and short-baseline methods. The proposed method only requires single receiver observation, thus applicable to both differenced and un-differenced data processing modes. However, the methods may be subject to the normal ionosphere conditions and low autocorrelation GNSS receivers. Experimental results also indicate the proposed method can result on more realistic parameter precision.

Noise Filtering of Process Execution Logs based on Outliers Detection

This paper presents a technique for the automated removal of noise from process execution logs. Noise is the result of data quality issues such as logging errors and manifests itself in the form of infrequent process behavior. The proposed technique generates an abstract representation of an event log as an automaton capturing the direct follows relations between event labels. This automaton is then pruned from arcs with low relative frequency and used to remove from the log those events not fitting the automaton, which are identified as outliers. The technique has been extensively evaluated on top of various auto- mated process discovery algorithms using both artificial logs with different levels of noise, as well as a variety of real-life logs. The results show that the technique significantly improves the quality of the discovered process model along fitness, appropriateness and simplicity, without negative effects on generalization. Further, the technique scales well to large and complex logs.

Noise in my head: Voices from the ugly Australian underground

The Ugly Australian Underground documents the music, songwriting, aesthetics and struggles of fifty of Australia’s most innovative and significant bands and artists currently at the creative peak of their careers. The book provides a rare insight into the critically heralded cult music scene in Australia. The author, Jimi Kritzler, is both a journalist and a musician, and is personally connected to the musicians he interviews through his involvement in this music subculture. The interviews are extremely personal and reveal much more than any interview granted to street press or blogs. They deal with not only the music and songwriting processes of each band, but in some circumstances their struggles with drugs, involvement in crime and the death of band members.

Hepatitis C and initiates into injecting drug use among young people: Part I: The first shot

Explores how young people in Australia first come to inject drugs and how they learn about hepatitis C and sterile injecting drug use. Background on hepatitis C; Reasons for injecting drugs; Selection criteria for young people's participation in the i2i Project.