Fluid Flow Velocity Measurement in Active Wells Using Using Fiber Optic Distributed Acoustic Sensors

Real time monitoring of the behaviour of fluids along the whole length of fluid filled well pipes is important to the oil and gas industry as it enables well operators to maximize oil and gas production and optimize the quality of oil and gas produced, whilst reducing the cost. Flow speed measurement is one of the key approaches in fluid flow monitoring in wells. In this paper, three methods are designed, developed and demonstrated to estimate the speed and direction of flow at a range of depths in real world oil, gas and water wells using acoustic data set from distributed acoustic sensors that attached to the wells. The developed methods are based on a new combination of several techniques from signal processing, machine learning and physics. The Terabyte size acoustic dataset are recorded from each well as a two-dimensional function of both distance along the pipeline and time. The aim of the developed methods is estimating flow speed at each point along over 3000 meters pipelines and increasing the accurately and efficiently of the flow speed calculation compared to the existing method. The methods developed in this paper are computationally inexpensive, which make them suitable for real time well monitoring

Automatic diagnosis of electromechanical faults in induction motors based on the transient analysis of the stray flux via MUSIC methods

(c) 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] In the induction motor predictive maintenance area, there is a continuous search for new techniques and methods that can provide additional information for a more reliable determination of the motor condition. In this context, the analysis of the stray flux has drawn the interest of many researchers. The simplicity, low cost and potential of this technique makes it attractive for complementing the diagnosis provided by other well-established methods. More specifically, the study of this quantity under the starting has been recently proposed as a valuable tool for the diagnosis of certain electromechanical faults. Despite this fact, the research in this approach is still incipient and the employed signal processing tools must be still optimized for a better visualization of the fault components. Moreover, the development of advanced algorithms that enable the automatic identification of the resulting transient patterns is another crucial target within this area. This article presents an advanced algorithm based on the combined application of MUSIC and neural networks that enables the automatic identification of the time-frequency patterns created by the stray flux fault components under starting as well as the subsequent determination of the fault severity level. Two faults are considered in the work: rotor problems and misalignments. Also, different positions of the external coil sensor are studied. The results prove the potential of the intelligent algorithm for the reliable diagnosis of electromechanical faults.This work was supported in part by the Spanish "Ministerio de Ciencia Innovacion y Universidades" and in part by FEDER program in the "Proyectos de I+D de Generacion de Conocimiento del Programa Estatal de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I+D+i, Subprograma Estatal de Generacion de Conocimiento" (PGC2018-095747-B-I00).Zamudio-Ramírez, I.; Ramirez-Núñez, JA.; Antonino Daviu, JA.; Osornio-Rios, RA.; Quijano-Lopez, A.; Razik, H.; Romero-Troncoso, RDJ. (2020). Automatic diagnosis of electromechanical faults in induction motors based on the transient analysis of the stray flux via MUSIC methods. IEEE Transactions on Industry Applications. 56(4):3604-3613. https://doi.org/10.1109/TIA.2020.2988002S3604361356

Networked control system with MANET communication and AODV routing

The industries are presently exploring the use of wired and wireless systems for control, automation, and monitoring. The primary benefit of wireless technology is that it reduces the installation cost, in both money and labor terms, as companies already have a significant investment in wiring. The research article presents the work on the analysis of Mobile Ad Hoc Network (MANET) in a wireless real-time communication medium for a Networked Control System (NCS), and determining whether the simulated behavior is significant for a plant or not. The behavior of the MANET is analyzed for Ad-hoc on-demand distance vector routing (AODV) that maintenances communication among 150 nodes for NCS. The simulation is carried out in Network Simulator (NS2) software with different nodes cluster to estimate the network throughput, end-to-end delay, packet delivery ratio (PDR), and control overhead. The benefit of MANET is that it has a fixed topology, which permits flexibility since mobile devices may be used to construct ad-hoc networks anywhere, scalability because more nodes can be added to the network, and minimal operating expenses in that no original infrastructure needs to be developed. AODV routing is a flat routing system that does not require central routing nodes. As the network grows in size, the network can be scaled to meet the network design and configuration requirements. AODV is flexible to support different configurations and topological nodes in dynamic networks because of its versatility. The advantage of such network simulation and routing behavior provides the future direction for the researchers who are working towards the embedded hardware solutions for NCS, as the hardware complexity depends on the delay, throughput, and PDR

A review of intelligent methods for condition monitoring and fault diagnosis of stator and rotor faults of induction machines

Nowadays, induction motor (IM) is extensively used in industry, including mechanical and electrical applications. However, three main types of IM faults have been discussed in the literature, bearing, stator, and rotor. Importantly, stator and rotor faults represent approximately 50%. Traditional condition monitoring (CM) and fault diagnosis (FD) methods require a high processing cost and much experience knowledge. To tackle this challenge, artificial intelligent (AI) based CM and FD techniques are extensively developed. However, there have been many review research papers for intelligent CM and FD machine learning methods of rolling elements bearings of IM in the literature. Whereas there is a lack in the literature, and there are not many review papers for both stator and rotor intelligent CM and FD. Thus, the proposed study's main contribution is in reviewing the CM and FD of IM, especially for the stator and the rotor, based on AI methods. The paper also provides discussions on the main challenges and possible future works