Mixed connective tissue disease — enigma variations?

In 1972, Sharp and colleagues described a new autoimmune rheumatic disease which they called mixed connective tissue disease (MCTD), characterized by overlapping features of systemic sclerosis (SSc), systemic lupus erythematosus (SLE), polymyositis/dermatomyositis (PM/DM), high levels of anti-U1snRNP and low steroid requirement use with good prognosis. MCTD was proposed as a distinct disease. However, soon after the original description, questions about the existence of such a syndrome as well as disputes over the features initially described began to surface. The conundrum of whether MCTD is a distinct disease entity remains controversial. We undertook a literature review focusing on the articles reporting new data about MCTD published in the last decade, to determine whether any new observations help to answer the conundrum of MCTD. After reviewing recent data, we question whether the term MCTD is appropriately retained, preferring to use the term “undifferentiated autoimmune rheumatic disease”

Local wavefield velocity imaging for damage evaluation

Ultrasonic Propagation Imaging or Acoustic Wavefield Imaging has been widely used to evaluate structural damages and internal features. Inspecting complete wavefield time history for damage identification is tedious and error-prone. A more effective way is by extracting damage-related information into a single image. A wavefield velocity imaging method that maps the local estimates of group or phase velocity is proposed. Actual velocity values rather than arbitrarily-scaled intensities are mapped, enabling damage sizing without the need of supervised training or inspecting wavefield propagation video. Performance of the proposed method was tested by inspecting a 100 mm by 100 mm area of a 2 mm thick stainless steel specimen. Local phase velocity maps of A0 mode showed a half-thickness hole of 2 mm diameter as significant change in local phase velocity from the nominal 2 m/ms. Full width at half maximum of relevant velocity profiles proved the accuracy and consistency of the damage sizing

Detection of coupling misalignment by extended orbits

In this paper a ‘SpectraQuest’ demonstrator is used to introduce misalignment into a rotating machinery set-up. Depending on the coupling used in the set-up, angular and/or parallel misalignment can be brought in the rotating system. Traditionally, the data captured by accelerometers is transferred into the frequency domain in order to interpret the vibrations measured by the accelerometers. The frequency domain has proven its usefulness but even the time domain can come in handy to draw the right conclusions regarding to misalignment in a rotating set-up. Orbit plots display the integrated data captured by accelerometers, in order to display the movement of the rotating shaft. The influence of the misalignment and imbalance on these orbits will be discussed

Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoring

This is the post-print version of the final paper published in Renewable Energy. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.A review of current progress in Condition Monitoring (CM) of wind turbine gearboxes and generators is presented, as an input to the design of a new continuous CM system with automated warnings based on a combination of vibrational and Acoustic Emission (AE) analysis. For wind turbines, existing reportage on vibrational monitoring is restricted to a few case histories whilst data on AE is even scarcer. In contrast, this paper presents combined vibration and AE monitoring performed over a continuous period of 5 days on a wind turbine. The vibrational and AE signatures for a healthy wind turbine gearbox and generator were obtained as a function of wind speed and turbine power, for the full normal range of these operational variables. i.e. 5–25 m/s and 0–300 kW respectively. The signatures have been determined as a vital pre-requisite for the identification of abnormal signatures attributable to shaft and gearbox defects. Worst-case standard deviations have been calculated for the sensor data. These standard deviations determine the minimum defect signal that could be detected within the defined time interval without false alarms in an automated warning system.UK Northern Wind Innovation Program NWI

Wind turbine condition monitoring : technical and commercial challenges.

Deployment of larger scale wind turbine systems, particularly offshore, requires more organized operation and maintenance strategies to ensure systems are safe, profitable and cost-effective. Among existing maintenance strategies, reliability centred maintenance is regarded as best for offshore wind turbines, delivering corrective and proactive (i.e. preventive and predictive) maintenance techniques enabling wind turbines to achieve high availability and low cost of energy. Reliability centred maintenance analysis may demonstrate that an accurate and reliable condition monitoring system is one method to increase availability and decrease the cost of energy from wind. In recent years, efforts have been made to develop efficient and cost-effective condition monitoring techniques for wind turbines. A number of commercial wind turbine monitoring systems are available in the market, most based on existing techniques from other rotating machine industries. Other wind turbine condition monitoring reviews have been published but have not addressed the technical and commercial challenges, in particular, reliability and value for money. The purpose of this paper is to fill this gap and present the wind industry with a detailed analysis of the current practical challenges with existing wind turbine condition monitoring technology

Detection of fastener loosening in simple lap joint based on ultrasonic wavefield imaging

Joints in aero-mechanical structures are critical elements that ensure the structural integrity but they are prone to damages. Inspection of such joints that have no prior baseline data is really challenging but it can be possibly done using the Ultrasonic Propagation Imager (UPI). The feasibility of applying UPI for detection of loosened fastener is investigated in this study. A simple lap joint specimen made by connecting two pieces of 2.5mm thick SAE304 stainless steel plates using five M6 screws and nuts has been used in this study. All fasteners are tightened to 10Nm but one of them is completely loosened to simulate the damage. The wavefield data is processed into ultrasonic wavefield propagation video and a series of spectral amplitude images. The spectral images showed noticeable amplitude difference at the loosened fastener, hence confirmed the feasibility of using UPI for structural joints inspection. A simple contrast maximization method is also introduced to improve the result

Modelling wind turbine degradation and maintenance

degradation, inspection and maintenance processes. The model was developed based on the Petri net method that effectively captures the stochastic nature of the dynamic processes through the use of appropriate statistical distributions. The versatility of the method allows the details of the degradation and maintenance operations to be incorporated in the model. In particular, there are dependent deterioration processes between wind turbine subsystems, complex maintenance rules and the incorporation of condition monitoring systems for early failure indication to enable replacement prior to failure. The purposes of the model are to predict the future condition of wind turbine components and to investigate the effect of a specified maintenance strategy. The model outputs are statistics indicating the performance of the wind turbine components; these include the probability of being in different condition states, the expected number of maintenance actions and the average number and duration of system downtime under any maintenance strategy