Online Monitoring

Conference Record of the 2004 IEEE International Symposium on Electrical Insulation, Indianapolis. IN USA, 19-22 September 2004 ... A Procedure for Diagnosis and Condition based Maintenance for Power Transformers ... Alireza Setayeshmehr, Alireza Akbari, Hossein ...

Free-flow electrophoresis (FFE) separation methods have been developed and investigated for around 50 years and have been applied not only to many types of analytes for various biomedical applications, but also for the separation of inorganic and organic substances. Its continuous sample preparation and mild separation conditions make it also interesting for online monitoring and detection applications. Since 1994 several microfluidic, miniaturized FFE devices were developed and experimentally characterized. In contrast to their large-scale counterparts microfluidic FFE (μ-FFE) devices offer new possibilities due to the very rapid separations within several seconds or below and the requirement for sample volumes in the microliter range. Eventually, these μ-FFE systems might find application in so-called lab-on-a-chip devices for real-time monitoring and separation applications. This review gives detailed information on the results so far published on μ-FFE chips, comprising its four main modes, namely free-flow zone electrophoresis (FFZE), free-flow IEF (FFIEF), free-flow ITP (FFITP), and free-flow field-step electrophoresis (FFFSE). The principles of the different FFE modes and the basic underlying theory are given and discussed with special emphasis on miniaturization. Different designs as well as fabrication methods and applied materials are discussed and evaluated. Furthermore, the separation results shown indicate that similar separation quality with respect to conventional FFE systems, as defined by the resolution and peak capacity, can be achieved with μ-FFE separations when applying much lower electrical voltages. Furthermore, innovations still occur and several approaches for hyphenated, more integrated systems have been proposed so far, some of which are discussed here. This review is intended as an introduction and early compendium for research and development within this field.

The powder bed fusion additive manufacturing process enables fabrication of metal parts with complex geometry and elaborate internal features, the simplification of the assembly process, and the reduction of development time; however, its tremendous potential for widespread application in industry is hampered by the lack of consistent quality. This limits its ability as a viable manufacturing process particularly in the aerospace and medical industries where high quality and repeatability are critical. A variety of defects, which may be initiated during powder bed fusion additive manufacturing, compromise the repeatability, precision, and resulting mechanical properties of the final part. One approach that has been more recently proposed to try to control the process by detecting, avoiding, and/or eliminating defects is online monitoring. In order to support the design and implementation of effective monitoring and control strategies, this paper identifies, analyzes, and classifies the common defects and their contributing parameters reported in the literature, and defines the relationship between the two. Next, both defects and contributing parameters are categorized under an umbrella of manufacturing features for monitoring and control purposes. The quintuple set of manufacturing features presented here is meant to be employed for online monitoring and control in order to ultimately achieve a defect-free part. This categorization is established based on three criteria: (1) covering all the defects generated during the process, (2) including the essential contributing parameters for the majority of defects, and (3) the defects need to be detectable by existing monitoring approaches as well as controllable through standard process parameters. Finally, the monitoring of signatures instead of actual defects is presented as an alternative approach to controlling the process " indirectly."

The use of the internet of things (IoT) in solar photovoltaic (PV) systems is a critical feature for remote monitoring, supervising, and performance evaluation. Furthermore, it improves the long-term viability, consistency, efficiency, and system maintenance of energy production. However, previous researchers' proposed PV monitoring systems are relatively complex and expensive. Furthermore, the existing systems do not have any backup data, which means that the acquired data could be lost if the network connection fails. This paper presents a simple and low-cost IoT-based PV parameter monitoring system, with additional backup data stored on a microSD card. A NodeMCU ESP8266 development board is chosen as the main controller because it is a system-on-chip (SOC) microcontroller with integrated Wi-Fi and low-power support, all in one chip to reduce the cost of the proposed system. The solar irradiance, ambient temperature, PV output voltage and PV output current, are measured with photo-diodes, DHT22, impedance dividers and ACS712. While, the PV output power is a product of the PV voltage and PV current. ThingSpeak, an opensource software, is used as a cloud database and data monitoring tool in the form of interactive graphics. The results showed that the system was designed to be highly accurate, reliable, simple to use, and low-cost.

The performance of a tracking filter can be evaluated in terms of the filterpsilas optimality conditions. Testing for optimality is necessary because the estimation error covariance as provided by the filter is not a reliable indicator of performance, which is known to be ldquooptimisticrdquo (inconsistent) particularly when there are model mismatches and target maneuvers. The conventional root-mean square (RMS) error value and its variants are widely used for performance evaluation in simulation and testing but it is not feasible for real-time operations where the ground truth is hardly available. One approach for real-time reliability assessment is optimality self online monitoring (OSOM) investigated in this paper. Statistical tests for optimality conditions are formulated. Simulation examples are presented to illustrate their possible use in evaluation and adaptation.

Multivariable supervision systems for online monitoring of induction motors allow large versatility and diagnosis robustness. As regards rotor faults, the diagnostic procedure based on sideband current components may fail due to the presence of interbar currents that reduce the degree of rotor asymmetry and, thus, the amplitude of these spectral components. On the other hand, the interbar currents produce core

Gas stirring is commonly used in pyrometallurgical vessels to enhance mass and heat transfer and promote impurity removal. These processes can be difficult to monitor because of the severe operating conditions associated with these processes. However, experienced operators can make judgment of the process by observing the disturbance of the top surface and listening to the sound of the bubbling. In this article, we proposed a new approach of analyzing the stirring process by all the signals that could be reliably measured simultaneously, i.e., the image of the disturbed top surface, the sound of the bubbling process, and the vibrations on the wall of the vessel. The principal component analysis (PCA) results on the cold model data showed that the three types of signals are highly correlated, and they can be reduced to just one signal, which picks up most of the variation of the process and could be used to monitor the bubbling phenomena. The PCA results also showed that the combination of sound and vibration signals can still pick up most of the variations without combining the image signal from the disturbed top surface. This signal could be used as online monitoring of the stirring process and provide feedback signals for process control.

e-Health – health care through the internet e-Health, the employment of information and communication technology for health care, offers promising opportunities for quality enhancement and cost containment of health care delivery. In this paper we accumulate evidence for the efficacy of internet interventions designed to promote a healthy lifestyle (such as weight control and low risk alcohol consumption), to reinforce self-management of chronic somatic conditions (such as headache), and to overcome mental health problems (such as depression and anxiety disorders). Evidence suggests that internet interventions may be equally effective as regular face-to-face interventions. Current research aims at cost effectiveness, at mobile devices for online monitoring and coaching, at successful components of online-treatment, and at effective implementation strategies. We identified problems such as e-therapy adherence and challenges such as the delivery of e-health care per target population based upon a stepped care approach. We conclude that e-health is here to stay and will fully integrate into all aspects of health care delivery.

The online monitoring of induction motors is becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. A fuzzy logic approach may help to diagnose induction motor faults. This work presents a reliable method for the detection of stator winding faults (which make up 38% of induction motor

Guided elastic waves in the frequency range of a few hundred kHz, generated and detected by appro- priate transducer arrays, are used to monitor the structural integrity of pipes by comparing their actual state with a predefined reference state. For these purposes, theoretical, numerical, and experimental results are combined to study guided wave propagation and wave interaction with relevant defects in detail. Based on these findings, a guided wave based multi-channel structural health monitoring (SHM) system is designed and applied to identify and monitor structural defects in various piping components. The potential use of synthetic aperture techniques is discussed yielding spatial distributions of damage parameters along the pipe. The results reveal that guided wave based SHM in the kHz frequency regime has great potential for online monitoring of piping systems. It combines imaging techniques with long range detection capabilities and therefore closes the gap between local high...

The majority of the work performed for detecting eccentricity faults for permanent magnet synchronous motors (PMSM) focus on motor current signature analysis (MCSA), as it provides continuous on-line monitoring with existing current sensors. However, MCSA cannot be applied under nonstationary conditions and cannot distinguish faults with load torque oscillations, which are limitations for many PMSM drive applications. In this paper, it is shown that the d-axis inductance, Ld, decreases with increase in the severity of eccentricity due to the change in the degree of magnetic saturation, and it is proposed as a new fault indicator. The inverter can be used to perform a standstill test automatically whenever the motor is stopped, to measure Ld for eccentricity testing independent of load variations or oscillations, which is not possible with MCSA. An FE and experimental study on a 10hp PMSM verifies that eccentricity can be detected independent of the load with high sensitivity and reliability.