Sources of vibration and their treatment in hydro power stations-A review

AbstractVibration condition monitoring (VCM) enhances the performance of Hydro Generating Equipment (HGE) by minimizing the damage and break down chances, so that equipment stay available for a longer time. The execution of VCM and diagnosing the system of an HPS includes theoretical and experimental exploitation. Various studies have made their contribution to find out the vibration failure mechanism and incipient failures in HPS. This paper gives a review on VCM of electrical and mechanical equipment used in the HPS along with a brief explanation of vibration related faults considering past literature of around 30years. Causes of the vibrations on rotating and non-rotating equipment of HPS have been discussed along with the standards for vibration measurements. Future prospectus of VCM is also discussed

WiCV@CVPR2023: The Eleventh Women In Computer Vision Workshop at the Annual CVPR Conference

In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2023, organized alongside the hybrid CVPR 2023 in Vancouver, Canada. WiCV aims to amplify the voices of underrepresented women in the computer vision community, fostering increased visibility in both academia and industry. We believe that such events play a vital role in addressing gender imbalances within the field. The annual WiCV@CVPR workshop offers a) opportunity for collaboration between researchers from minority groups, b) mentorship for female junior researchers, c) financial support to presenters to alleviate finanacial burdens and d) a diverse array of role models who can inspire younger researchers at the outset of their careers. In this paper, we present a comprehensive report on the workshop program, historical trends from the past WiCV@CVPR events, and a summary of statistics related to presenters, attendees, and sponsorship for the WiCV 2023 workshop

Thermal Imaging And Its Application In Defence Systems

Thermal imaging is a boon to the armed forces namely army, navy and airforce because of its day night working capability and ability to perform well in all weather conditions. Thermal detectors capture the infrared radiation emitted by all objects above absolute zero temperature. The temperature variations of the captured scene are represented as a thermogram. With the advent of infrared detector technology, the bulky cooled thermal detectors having moving parts and demanding cryogenic temperatures have transformed into small and less expensive uncooled microbolometers having no moving parts, thereby making systems more rugged requiring less maintenance. Thermal imaging due to its various advantages has a large number of applications in military and defence. It is popularly used by the army and navy for border surveillance and law enforcement. It is also used in ship collision avoidance and guidance systems. In the aviation industry it has greatly mitigated the risks of flying in low light and night conditions. They are widely used in military aviation to identify, locate and target the enemy forces. Recently, they are also being incorporated in civil aviation for health monitoring of aircrafts

Time‐Frequency Approach for Stochastic Signal Detection

The detection of events in a stochastic signal has been a subject of great interest. One of the oldest signal processing technique, Fourier Transform of a signal contains information regarding frequency content, but it cannot resolve the exact onset of changes in the frequency, all temporal information is contained in the phase of the transform. On the other hand, Spectrogram is better able to resolve temporal evolution of frequency content, but has a trade‐off in time resolution versus frequency resolution in accordance with the uncertainty principle. Therefore, time‐frequency representations are considered for energetic characterisation of the non‐stationary signals. Wigner Ville Distribution (WVD) is the most prominent quadratic time‐frequency signal representation and used for analysing frequency variations in signals.WVD allows for instantaneous frequency estimation at each data point, for a typical temporal resolution of fractions of a second. This paper through simulations describes the way time frequency models are applied for the detection of event in a stochastic signal

Moving target detection in thermal infrared imagery using spatiotemporal information

An efficient target detection algorithm for detecting moving targets in infrared imagery using spatiotemporal information is presented. The output of the spatial processing serves as input to the temporal stage in a layered manner. The spatial information is obtained using joint space–spatial-frequency distribution and Rényi entropy. Temporal information is incorporated using background subtraction. By utilizing both spatial and temporal information, it is observed that the proposed method can achieve both high detection and a low false-alarm rate. The method is validated with experimentally generated data consisting of a variety of moving targets. Experimental results demonstrate a high value of F-measure for the proposed algorithm

Time–frequency analysis based robust vehicle detection using seismic sensor

This paper presents a robust time–frequency approach based on pseudo-Wigner–Ville distribution assisted Rényi entropy (PWVD-RE) for vehicle detection. Seismic sensors are used to capture the ground vibrations generated by moving vehicles. One of the challenging tasks is to accurately localize a seismic event with minimal or no false alarm. PWVD gives the energy distribution of a non-stationary signal in the time–frequency plane. This energy distribution can be interpreted as probability density function (pdf). Rényi entropy is used as localized measure of the energy distribution. A higher value of entropy indicates the likehood of a possible seismic event. An optimized Constant False Alarm Rate (CFAR) detector is used for detection of events caused by moving vehicles. Experiments were performed with civilian vehicles for validation of the proposed method. The performance is compared with the classical spectrogram based approach. The results show significant improvement in false alarm rate and reasonable enhancement in detection rate

Effect of Sampling Frequency on Acoustic Emission Onset Determination using Fractal Dimension

Acoustic emission is the generation of transient stress waves by rapid release of energy from localized sources giving rise to a versatile non-destructive testing technique. Numerous researchers have tried to exploit the fractal nature of acoustic emission for analysis purposes. The analysis of acoustic emission signature typically consists of four stages namely damage detection, damage localization, damage characterization and health prediction. The determination of arrival of first energy of particular phase at sensor, called onset determination, is an important prerequisite for source localization and source mechanism analysis because the accuracy in determination of onset time directly translates to accuracy in source localization and source mechanism analysis. However, the effect of acquisition parameters on the analysis is not well understood. In this present work, the objective is to understand the effect of one of the important acquisition parameters, sampling frequency. Choice of low sampling frequency might lead to loss of signal information while a high sampling frequency increases storage and computation, hence identifying the appropriate sampling frequency is a critical factor for onset determination. In the present work, the effect of sampling frequency on onset determination using fractal dimension has been assessed. Two open source datasets available at Acoustic emission portal set-up by Muravin have been employed for the research work. The first dataset pertains to a concrete beam while the second data is generated from a carbon fiber woven fabric plate. The acoustic emission events have been generated by using pencil lead break tests. The sampling frequency has been varied synthetically from 0.8 to 2.0 MHz in the steps of 100 kHz. The onset is determined for each individual value of sampling acquisition, by detecting change of fractal dimension estimated using Higuchi’s method. It has been observed by analyzing the onset values determined at different sampling frequencies that the onset does not always converge to the onset having high accuracy with increase of sampling frequency. This observation has been further verified by calculating Spearman Rank correlation values. Also, the results depict the dependence of technique on material in which emissions are generated. The results may be useful for the design of instrumentation for acoustic emission as well as development of computational methods for analysis

Hybrid multi-resolution detection of moving targets in infrared imagery

A hybrid moving target detection approach in multi-resolution framework for thermal infrared imagery is presented. Background subtraction and optical flow methods are widely used to detect moving targets. However, each method has some pros and cons which limits the performance. Conventional background subtraction is affected by dynamic noise and partial extraction of targets. Fast independent component analysis based background subtraction is efficient for target detection in infrared image sequences; however the noise increases for small targets. Well known motion detection method is optical flow. Still the method produces partial detection for low textured images and also computationally expensive due to gradient calculation for each pixel location. The synergistic approach of conventional background subtraction, fast independent component analysis and optical flow methods at different resolutions provide promising detection of targets with reduced time complexity. The dynamic background noise is compensated by the background update. The methodology is validated with benchmark infrared image datasets as well as experimentally generated infrared image sequences of moving targets in the field under various conditions of varying illumination, ambience temperature and the distance of the target from the sensor location. The significant value of F-measure validates the efficiency of the proposed methodology with high confidence of detection and low false alarms

Adaptive contour-based statistical background subtraction method for moving target detection in infrared video sequences

A robust contour-based statistical background subtraction method for detection of non-uniform thermal targets in infrared imagery is presented. The foremost step of the method comprises of generation of background frame using statistical information of an initial set of frames not containing any targets. The generated background frame is made adaptive by continuously updating the background using the motion information of the scene. The background subtraction method followed by a clutter rejection stage ensure the detection of foreground objects. The next step comprises of detection of contours and distinguishing the target boundaries from the noisy background. This is achieved by using the Canny edge detector that extracts the contours followed by a k-means clustering approach to differentiate the object contour from the background contours. The post processing step comprises of morphological edge linking approach to close any broken contours and finally flood fill is performed to generate the silhouettes of moving targets. This method is validated on infrared video data consisting of a variety of moving targets. Experimental results demonstrate a high detection rate with minimal false alarms establishing the robustness of the proposed method