Simulating eddy current sensor outputs for blade tip timing

Blade tip timing is a contactless method used to monitor the vibration of blades in rotating machinery. Blade vibration and clearance are important diagnostic features for condition monitoring, including the detection of blade cracks. Eddy current sensors are a practical choice for blade tip timing and have been used extensively. As the data requirements from the timing measurement become more stringent and the systems become more complicated, including the use of multiple sensors, the ability to fully understand and optimize the measurement system becomes more important. This requires detailed modeling of eddy current sensors in the blade tip timing application; the current approaches often rely on experimental trials. Existing simulations for eddy current sensors have not considered the particular case of a blade rotating past the sensor. Hence, the novel aspect of this article is the development of a detailed quasi-static finite element model of the electro-magnetic field to simulate the integrated measured output of the sensor. This model is demonstrated by simulating the effect of tip clearance, blade geometry, and blade velocity on the output of the eddy current sensor. This allows an understanding of the sources of error in the blade time of arrival estimate and hence insight into the accuracy of the blade vibration measurement

Defining the causes of sporadic Parkinson’s disease in the global Parkinson’s genetics program (GP2)

\ua9 2023, Springer Nature Limited. The Global Parkinson’s Genetics Program (GP2) will genotype over 150,000 participants from around the world, and integrate genetic and clinical data for use in large-scale analyses to dramatically expand our understanding of the genetic architecture of PD. This report details the workflow for cohort integration into the complex arm of GP2, and together with our outline of the monogenic hub in a companion paper, provides a generalizable blueprint for establishing large scale collaborative research consortia

Multi-ancestry genome-wide association meta-analysis of Parkinson’s disease

\ua9 2023, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply. Although over 90 independent risk variants have been identified for Parkinson’s disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson’s disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations

Defining the causes of sporadic Parkinson’s disease in the global Parkinson’s genetics program (GP2)

The Global Parkinson’s Genetics Program (GP2) will genotype over 150,000 participants from around the world, and integrate genetic and clinical data for use in large-scale analyses to dramatically expand our understanding of the genetic architecture of PD. This report details the workflow for cohort integration into the complex arm of GP2, and together with our outline of the monogenic hub in a companion paper, provides a generalizable blueprint for establishing large scale collaborative research consortia

Author Correction: Elucidating causative gene variants in hereditary Parkinson’s disease in the Global Parkinson’s Genetics Program (GP2)

Correction to: s41531-023-00526-9 npj Parkinson’s Disease, published online 27 June 2023 In this article the Global Parkinson’s Genetics Program (GP2) members names and affiliations were missing in the main author list of the Original article which are listed in the below

The use of eddy current sensor based blade tip timing for FOD detection

Deterioration of rotor blades due to foreign object damage (FOD), erosion by sand/water, low cycle fatigue (LCF) and high cycle fatigue (HCF) all limit blade life, but cannot always be detected before a failure. The advent of tip-timing systems makes it possible to assess turbomachinery blade vibration using non-contact systems. However, these systems are still largely optical based and therefore suffer from contamination problems, further development of these systems is difficult due to problems associated with keeping the sensors clean. Experimental measurements have been carried out using an alternative eddy current sensor that has been validated in a series of laboratory and engine tests to measure rotor blade arrival times. A series of engine trials have been conducted to assess their capability for detection of pre-existing damage and the capture of dynamic foreign object damage (FOD) events. The results show that it is possible to acquire high quality blade timing data for use in engine condition monitoring. In addition for the detection of FOD created damage and FOD damage as it occurs. Copyright © 2008 QinetiQ Ltd

Development of a combined eddy current and pressure sensor for gas turbine blade health monitoring

Gas turbine engine health monitoring systems play an active role to ensure timely maintenance and prevention of failures. Tip-timing and tip clearance measurements form a major part of gas turbine health monitoring systems. They are used to assess turbomachinery blade vibrations using non-contact systems such as optical, capacitive, Hall effect, eddy current etc. Most of these sensors are prone to contamination, non-linearity and cannot measure both tip-timing and tip-clearance together. Eddy current sensors are found to be robust and can measure both tip-timing and tip-clearance simultaneously. They are already being used in gas turbine health monitoring systems to assess compressor and turbine blade vibrations. Apart from assessing blade vibrations, it will be quite beneficial to predict and prevent surge and stall of compressors in an engine. Surge and stall can be disastrous for an aircraft during flight as it can cause severe damage to the engine. Pressure sensors are generally used to study the variations in the inlet flow for surge and stall protection in an engine and play an important role in health monitoring. A new combined sensor that can measure tip-timing, tip-clearance and dynamic pressure was developed at the University of Oxford for use in gas turbine engine health monitoring. The combined sensor uses a pressure sensor in the centre and is enclosed by an eddy current sensor forming a compact single package. The pressure sensor used here is a fast response optical based sensor that is known to work at high temperatures and is less noisy compared to piezo based pressure sensors. The pressure sensor can also measure the steady state temperature of the casing. The combined sensor is found to be quite robust and is able to operate in harsh environments without any loss in accuracy. Due to the combined package, the space occupied is much less compared to that required by two separate sensors. The sensor has many applications that include measuring vibrations, active flow control, stall/surge of compressors etc. The paper presents the design and development of this combined sensor along with experimental results on tip timing and unsteady pressures from gas turbine engine fan blades. The engine tests included looking at the effects of squeezing the inlet casing and to study the effect of distorting the inlet flow on blade vibrations by placing varying number of bars in the inlet duct of the engine

A novel sensor for detection of oil condition and contamination based on a thermal approach

Oil condition and contamination can be a major issue in lubrication systems such as aircraft, automobiles etc. Lubricating or cooling oil contamination occurs when metallic or non-metallic particles are produced due to wear of machine components and these are not captured by the filter system. Furthermore, thermal stressing causes oxidation and thereby degradation of the oil. Liquid contamination can occur from water condensation or fuel from heat exchangers. These can cause degradation of the oil and reduce the lubricating properties and clogg oil paths and accelerate the wear of moving parts. On-line oil condition monitoring systems are important for preventive maintenance especially for aircraft engine bearings, aviation gearboxes etc. Current on-line oil condition monitoring sensors are mainly eddy current, optical based. These sensors have a major drawback that they are prone to surface contamination and non-linearity. The gauges are also not sensitive enough to detect extremely small particulates or are prone to false detection such as trapped bubbles. A new sensor has been developed using thin film heat transfer sensors that can detect any form of contamination in oil such as metal, nonmetals, oxidation, liquids etc. The sensor works on the principle of measuring the thermal product of the material, as the composition of the material in contact with the sensor changes the thermal product changes and can be detected. The sensor can be used for both on-line and in-line condition monitoring and has been demonstrated to be robust. Initially, lab based tests were carried out to optimise the system for sensitivity and signal to noise ratio. The sensor has been demonstrated to detect 0.25% of contaminants by mass. Experiments were carried out by seeding metallic and non-metallic particles of various sizes to an engine oil system to validate its performance