Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal

Intelligent fault diagnosis methods have replaced time consuming and unreliable human analysis, increasing anomaly detection efficiency. Deep learning models are clear cut techniques for this purpose. This paper’s fundamental purpose is to automatically detect leakage in tanks during production with more reliability than a manual inspection, a common practice in industries. This research proposes an inspection system to predict tank leakage using hydrophone sensor data and deep learning algorithms after production. In this paper, leak detection was investigated using an experimental setup consisting of a plastic tank immersed underwater. Three different techniques for this purpose were implemented and compared with each other, including fast Fourier transform (FFT), wavelet transforms, and time-domain features, all of which are followed with 1D convolution neural network (1D-CNN). Applying FFT and converting the signal to a 1D image followed by 1D-CNN showed better results than other methods. Experimental results demonstrate the effectiveness and the superiority of the proposed methodology for detecting real-time leakage inaccuracy

Deep downhole testing: procedures and analysis for high-resolution vertical seismic profiling

textA study was undertaken to improve the signal quality and the resolution of the velocity profile for deep downhole seismic testing. Deep downhole testing is defined in this research as measurements below 225 m (750 ft). The study demonstrated that current testing procedures can be improved to result in higher signal quality by customizing the excitation frequency of the vibrator to local site conditions of the vibrator-earth system. The earth condition beneath the base plate can be an important factor in the signal quality subject to variations with time when tests are repetitive. This work proposes a convenient method to measure the site localized natural frequency and damping ratio, and recommends using different excitation frequencies for P- and S-wave generation. Properly increasing the excitation duration of the source signal also contributes to the quality of the receiver signal. The source signature of sinusoidal vibratory source is identified. Conventional travel time analysis using vibratory source generally focuses on chirp sweeps. After testing with impulsive sources and chirp sweeps and comparing the results with the durational sinusoidal source, the sinusoidal source was then chosen. This work develops an approach to identifying the source signature of the sinusoidal source and concludes that the normalized source signature is relevant only to four parameters: the fixed-sine excitation frequency, the duration of excitation, the damping ratio of the vibrator-earth system, and the damped natural frequency of the vibrator-earth system. Two of the parameters are designated input to the vibrator and the other two parameters are measured in the field test using the proposed method in this work. A new wavelet-response technique based on deconvolution and consideration of velocity dispersion is explored in travel-time analyses. The wavelet-response technique is also used for development of a new approach to correcting disorientation of receiver tool. The improved downhole procedures and analyses are then used in the analysis of deep downhole test data obtained at Hanford, WA. Downhole testing was performed to a depth of about 420 m (1400 ft) at Hanford site. Improvements in resolving the wave velocity profiles to depths below 300 m (1000) ft are clearly shown.Civil, Architectural, and Environmental Engineerin

Spontaneous and explicit estimation of time delays in the absence/presence of multipath propagation.

by Hing-cheung So.Thesis (Ph.D.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 133-141).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Time Delay Estimation (TDE) and its Applications --- p.1Chapter 1.2 --- Goal of the Work --- p.6Chapter 1.3 --- Thesis Outline --- p.9Chapter 2 --- Adaptive Methods for TDE --- p.10Chapter 2.1 --- Problem Description --- p.11Chapter 2.2 --- The Least Mean Square Time Delay Estimator (LMSTDE) --- p.11Chapter 2.2.1 --- Bias and Variance --- p.14Chapter 2.2.2 --- Probability of Occurrence of False Peak Weight --- p.16Chapter 2.2.3 --- Some Modifications of the LMSTDE --- p.17Chapter 2.3 --- The Adaptive Digital Delay-Lock Discriminator (ADDLD) --- p.18Chapter 2.4 --- Summary --- p.20Chapter 3 --- The Explicit Time Delay Estimator (ETDE) --- p.22Chapter 3.1 --- Derivation and Analysis of the ETDE --- p.23Chapter 3.1.1 --- The ETDE system --- p.23Chapter 3.1.2 --- Performance Surface --- p.26Chapter 3.1.3 --- Static Behaviour --- p.28Chapter 3.1.4 --- Dynamic Behaviour --- p.30Chapter 3.2 --- Performance Comparisons --- p.32Chapter 3.2.1 --- With the LMSTDE --- p.32Chapter 3.2.2 --- With the CATDE --- p.34Chapter 3.2.3 --- With the CRLB --- p.35Chapter 3.3 --- Simulation Results --- p.38Chapter 3.3.1 --- Corroboration of the ETDE Performance --- p.38Chapter 3.3.2 --- Comparative Studies --- p.44Chapter 3.4 --- Summary --- p.48Chapter 4 --- An Improvement to the ETDE --- p.49Chapter 4.1 --- Delay Modeling Error of the ETDE --- p.49Chapter 4.2 --- The Explicit Time Delay and Gain Estimator (ETDGE) --- p.52Chapter 4.3 --- Performance Analysis --- p.55Chapter 4.4 --- Simulation Results --- p.57Chapter 4.5 --- Summary --- p.61Chapter 5 --- TDE in the Presence of Multipath Propagation --- p.62Chapter 5.1 --- The Multipath TDE problem --- p.63Chapter 5.2 --- TDE with Multipath Cancellation (MCTDE) --- p.64Chapter 5.2.1 --- Structure and Algorithm --- p.64Chapter 5.2.2 --- Convergence Dynamics --- p.67Chapter 5.2.3 --- The Generalized Multipath Cancellator --- p.70Chapter 5.2.4 --- Effects of Additive Noises --- p.73Chapter 5.2.5 --- Simulation Results --- p.74Chapter 5.3 --- TDE with Multipath Equalization (METDE) --- p.86Chapter 5.3.1 --- The Two-Step Algorithm --- p.86Chapter 5.3.2 --- Performance of the METDE --- p.89Chapter 5.3.3 --- Simulation Results --- p.93Chapter 5.4 --- Summary --- p.101Chapter 6 --- Conclusions and Suggestions for Future Research --- p.102Chapter 6.1 --- Conclusions --- p.102Chapter 6.2 --- Suggestions for Future Research --- p.104Appendices --- p.106Chapter A --- Derivation of (3.20) --- p.106Chapter B --- Derivation of (3.29) --- p.110Chapter C --- Derivation of (4.14) --- p.111Chapter D --- Derivation of (4.15) --- p.113Chapter E --- Derivation of (5.21) --- p.115Chapter F --- Proof of unstablity of A°(z) --- p.116Chapter G --- Derivation of (5.34)-(5.35) --- p.118Chapter H --- Derivation of variance of αs11(k) and Δs11(k) --- p.120Chapter I --- Derivation of (5.40) --- p.123Chapter J --- Derivation of time constant of αΔ11(k) --- p.124Chapter K --- Derivation of (5.63)-(5.66) --- p.125Chapter L --- Derivation of (5.68)-(5.72) --- p.129References --- p.13

Esquema para la cancelación de interferencias mediante un análisis de multiresolución

En general, en el análisis y procesamiento de señales biomédicas es inevitable la presencia de señales de interferencia, que se traslapar temporal y espectralmente con la señal deseada, y los sonidos respiratorios no son la excepción. La auscultación pulmonar surge como una técnica clínica primordial en la evaluación y seguimiento de enfermedades pulmonares. Actualmente, entre los profesionales de la medicina, el inter& en el análisis de los sonidos respiratorios mediante la técnica de auscultación permanece vigente debido a la información que los sonidos contienen acerca de la condición pulmonar y su característica no invasiva. En el análisis de los sonidos respiratorios, los' ruidos cardiacos representan una fuente de ruido ineludible que modifica, en algunas ocasiones severamente, la información referente al estado pulmonar. Estudios relacionados con la cancelación de interferencias, en diversos campos de la ingeniería, indican que el desempeño de los esquemas de cancelación radica fuertemente en la estimación adecuada del retraso temporal entre la señal de referencia y la señal primaria. En consecuencia, el objetivo de la presente investigación es desarrollar un esquema que permita la cancelación de señales de interferencia (ruidos cardiacos) presentes en la adquisición de los sonidos respiratorios. Para minimizar los efectos de las señales de interferencia, el esquema propuesto para la estimación conjunta de la señal de interferencia y su posición temporal, denominado ‘‘joint time delay and signal estimation (JTDSE)”, utiliza un análisis de multiresolución como marco de referencia. En una primera etapa, el esquema estima la ubicación temporal, “time delay estimation (TDE)”, de la señal de interferencia cardíaca y posteriormente, realiza el filtrado de la señal de interferencia mediante técnicas no convencionales. El análisis de multiresolución de las señales de referencia cardíaca y de sonido respiratorio adquirido se efectúa mediante la transformada discreta de ondillas, “discrete wavelet transform (DWT)”, utilizando varios niveles de descomposición.Como consecuencia del análisis de multiresolución, la metodología propuesta posee importantes beneficios tales como la incorporación de información complementaria en múltiples subbandas, robustez en presencia de ruido, y disponibilidad de un procedimiento de validacibn para los retrasos estimados. El esquema en subbandas JTDSE emplea diferentes mecanismos de adaptación para el retraso temporal y para el proceso de filtrado de la señal de interferencia. La adaptación del retraso se lleva a cabo mediante el algoritmo del gradiente descendente (GD) G mediante el algoritmo de Levenberg-Marquardt (LM), mientras que el proceso de filtrado se basa en el filtro transversal rápido a bloques, “block fast transversal filter (BFTF)”. El desempeño del esquema JTDSE, en sd fase de estimación de la ubicación temporal de interferencias cardiacas, se evalúa utilizando señales sintetizadas que simulan la morfología de la señal respiratoria y la presencia de señales de interferencia con múltiples ruidos cardiacos. La robustez del esquema propuesto se evalúa involucrando diferentes condiciones de la relación señal a interferencia. Señales respiratoriz; adquiridas de sujetos sanos y pacientes asmáticos muestran que el esquema JTDSE representa una alternativa en el análisis de sonidos respiratorios. Además, los resultados del esquema JTDSE se comparan con los resultados obtenidos mediante un esquema de cancelación propuesto previamente, esquema basado en el filtro de Kalman de orden reducido “reduced order Kalman filter (ROKF)”. La utilidad del esquema propuesto no se limita al campo biomédico. En la detección submarina de objetos, “underwater targ target detection (UTD)”, con el propósito de analizar la informacibn relevante relacionada con el objeto bajo estudio, es importante ubicar temporalmente y eliminar la presencia de información no deseada en la señal acústica reflejada. En la evaluación del desempeño del esquema JTDSE, en el campo de la detección submarina de objetos, se emplearon señales sintetizadas simulando la presencia de múltiples componentes no deseados y señales adquiridas de objetos bajo el agua. Los resultados incluidos en eí presente documento se obtuvieron utilizando la versión programada del esquema JTDSE, en la plataforma proporcionada por “Matlab”. La aplicación clínica del esquema propuesto posiblemente requiera la versión en circuitería que en su diseño considere las bondades del procesamiento en paralelo de varias subbandas, inherente a la descomposición por multiresolución

Active Acoustics for Monitoring Unreported Leaks in Water Distribution Networks

Water distribution networks (WDNs) are critical infrastructure elements conveying water through thousands of kilometers of pipes. Pipes - one of the most critical elements in such systems - are subjected to various structural and environmental degradation mechanisms, eventually leading to leaks and breaks. Timely detection and localization of such leaks and bursts is crucial to managing the loss of this valuable resource, maintaining hydraulic capacity, and mitigating serious health risks which can potentially arise from such events. Much of the literature on leak detection has focused on passive methods; recording and analyzing acoustic signatures produced by leak(s) from passive piezo acoustic or pressure devices. Passive acoustic methods have received disproportionate attention both in terms of research as well as practical implementation for leak (or, bursts) detection and localization. Despite their popularity, passive methods have shown not to be reliable in detecting and localizing small leaks in full-scale systems, primarily due to acoustic signal attenuation and poor signal-to-noise ratios, especially in plastic materials. In this dissertation, an active method is explored, which uses an acoustic source to generate acoustic signatures inside a pipe network. A combination of active source and hydrophone receivers is demonstrated in this thesis as a viable method for monitoring leaks in water distribution pipes. The dissertation presents experimental results from two layouts of pipes, one a simple straight section and another a more complex network with tees and bends, with an acoustic source at one end, and hydrophones at strategic locations along the pipe. For leak detection, the measured reflected and transmitted energy using hydrophone receivers is used to determine the presence of a leak. To this effect, new leak indicators such as power reflection and transmission coefficients, power spectral density, reflected spectral density, and transmission loss are developed. Experimental results show that the method developed in this thesis can detect leaks robustly and has significant potential for use in pressurized water distribution systems. This thesis also presents a new framework for active method-based localization. Starting with a simple straight section for a proof of concept study and moving to lab-based WDNs, several methods are explored that simultaneously detect and locate a leak. The primary difficulty in detecting and estimating the location of a leak is overcome through a statistical treatment of time delays associated with multiple acoustic paths in a reverberant environment and estimated using two approaches: (i) classical signal decomposition technique (Prony's / matrix pencil method (MPM)) and (ii) a clustering pre-processing approach called mean-shift clustering. The former works on the cross-correlation of acoustic data recorded at two locations, while the latter operates on acoustic sensor data from a single location. Both methods are tested and validated using experimental data obtained from a laboratory testbed and are found to detect and localize leaks in plastic pipes effectively. Finally, time delay estimates obtained from Prony's / MPM are used in conjunction with the multilateration (MLAT) technique and extended Kalman filter (EKF) for localization in more complex WDNs. This study shows that the proposed active technique can detect and reliably localize leaks and has the potential to be applied to complex field-scale WDNs

Bimodal sound source tracking applied to road traffic monitoring

The constant increase of road traffic requires closer and closer road network monitoring. The awareness of traffic characteristics in real time as well as its historical trends, facilitates decision-making for flow regulation, triggering relief operations, ensuring the motorists’ safety and contribute to optimize transport infrastructures. Today, the heterogeneity of the available data makes their processing complex and expensive (multiple sensors with different technologies, placed in different locations, with their own data format, unsynchronized, etc.). This leads metrologists to develop “smarter” monitoring devices, i.e. capable of providing all the necessary data synchronized from a single measurement point, with no impact on the flow road itself and ideally without complex installation. This work contributes to achieve such an objective through the development of a passive, compact, non-intrusive, acoustic-based system composed of a microphone array with a few number of elements placed on the roadside. The proposed signal processing techniques enable vehicle detection, the estimation of their speed as well as the estimation of their wheelbase length as they pass by. Sound sources emitted by tyre/road interactions are localized using generalized cross-correlation functions between sensor pairs. These successive correlation measurements are filtered using a sequential Monte Carlo method (particle filter) enabling, on one hand, the simultaneous tracking of multiple vehicles (that follow or pass each other) and on the other hand, a discrimination between useful sound sources and interfering noises. This document focuses on two-axle road vehicles only. The two tyre/road interactions (front and rear) observed by a microphone array on the roadside are modeled as two stochastic, zero-mean and uncorrelated processes, spatially disjoint by the wheelbase length. This bimodal sound source model defines a specific particle filter, called bimodal particle filter, which is presented here. Compared to the classical (unimodal) particle filter, a better robustness for speed estimation is achieved especially in cases of harsh observation. Moreover the proposed algorithm enables the wheelbase length estimation through purely passive acoustic measurement. An innovative microphone array design methodology, based on a mathematical expression of the observation and the tracking methodology itself is also presented. The developed algorithms are validated and assessed through in-situ measurements. Estimates provided by the acoustical signal processing are compared with standard radar measurements and confronted to video monitoring images. Although presented in a purely road-related applied context, we feel that the developed methodologies can be, at least partly, applied to rail, aerial, underwater or industrial metrology