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

Advances in Machine Condition Monitoring and Fault Diagnosis

In the past few decades, with the great progress made in the field of computer technology, non-destructive testing, signal and image processing, and artificial intelligence, machine condition monitoring and fault diagnosis technology have also achieved great technological progress and played an active and important role in various industries to ensure the efficient and reliable operation of machines, lower the operation and maintenance costs, and improve the reliability and availability of large critical equipment [...

A Broad Learning Approach for Context-Aware Mobile Application Recommendation

With the rapid development of mobile apps, the availability of a large number of mobile apps in application stores brings challenge to locate appropriate apps for users. Providing accurate mobile app recommendation for users becomes an imperative task. Conventional approaches mainly focus on learning users' preferences and app features to predict the user-app ratings. However, most of them did not consider the interactions among the context information of apps. To address this issue, we propose a broad learning approach for \textbf{C}ontext-\textbf{A}ware app recommendation with \textbf{T}ensor \textbf{A}nalysis (CATA). Specifically, we utilize a tensor-based framework to effectively integrate user's preference, app category information and multi-view features to facilitate the performance of app rating prediction. The multidimensional structure is employed to capture the hidden relationships between multiple app categories with multi-view features. We develop an efficient factorization method which applies Tucker decomposition to learn the full-order interactions within multiple categories and features. Furthermore, we employ a group $\ell_{1}-$norm regularization to learn the group-wise feature importance of each view with respect to each app category. Experiments on two real-world mobile app datasets demonstrate the effectiveness of the proposed method

Study on order analysis for condition monitoring wind turbine gearbox

A complex 3-stage gearbox design, in which one planetary gear set at first stage and two sets of parallel gears at second and third stage, is conventionally applied on the large wind turbine configuration with Doubly Fed Induction Generator. In this variable-speed wind turbine, it is hard to directly apply conventional frequency analysis on condition monitoring of the gearbox planetary stage. Proper signal processing and analysis become crucial here for interpreting the conditions of gears and bearings. In this paper, order analysis is studied for condition monitoring the planetary stage of wind turbine gearbox. The approach takes advantage of angular resampling to achieve cyclo-stationary vibration signals and lessen the effects due to speed changes. One key element in the scheme is taking angular information to enable resampling the time-domain signals into angular-domain signals. A vibration signal model for gearbox planetary stage is firstly described, and the scheme is then tested on simulated signals to achieve fine frequency resolution. Time synchronous averaging is applied as a pre-processing technique to enhance the Signal to Noise Ratio (SNR) in order spectru

Hydrodynamic performance evaluation of a tidal turbine with leading-edge tubercles

This paper contributes to the investigations into the feasibility of improving the performance of a marine current turbine using a biomimetic concept inspired from the leading-edge tubercles on the flippers of humpback whales. An experimental test campaign was recently conducted in the Emerson Cavitation Tunnel at Newcastle University and details of this test campaign together with the findings are summarised in the paper A set of tidal turbines with different leading-edge profiles was manufactured and tested to evaluate the hydrodynamic performance. Various tests were conducted at different flow speed and different pitch angle settings of the turbine blades. The results showed that the models with the leading-edge tubercles had higher power coefficients at lower tip speed ratios (TSRs) and at lower pitch angle settings where the turbine blades were working under stall conditions. Therefore, the tubercles can reduce the turbines' cut-in speed to improve the starting performance. The biomimetic concept did not compromise the maximum power coefficient value of the turbine, being comparable to the device without the tubercles, but shifted the distribution of the coefficient over the range of the tip speed ratios tested

Performance evaluation of lossy quality compression algorithms for RNA-seq data

Background Recent advancements in high-throughput sequencing technologies have generated an unprecedented amount of genomic data that must be stored, processed, and transmitted over the network for sharing. Lossy genomic data compression, especially of the base quality values of sequencing data, is emerging as an efficient way to handle this challenge due to its superior compression performance compared to lossless compression methods. Many lossy compression algorithms have been developed for and evaluated using DNA sequencing data. However, whether these algorithms can be used on RNA sequencing (RNA-seq) data remains unclear. Results In this study, we evaluated the impacts of lossy quality value compression on common RNA-seq data analysis pipelines including expression quantification, transcriptome assembly, and short variants detection using RNA-seq data from different species and sequencing platforms. Our study shows that lossy quality value compression could effectively improve RNA-seq data compression. In some cases, lossy algorithms achieved up to 1.2-3 times further reduction on the overall RNA-seq data size compared to existing lossless algorithms. However, lossy quality value compression could affect the results of some RNA-seq data processing pipelines, and hence its impacts to RNA-seq studies cannot be ignored in some cases. Pipelines using HISAT2 for alignment were most significantly affected by lossy quality value compression, while the effects of lossy compression on pipelines that do not depend on quality values, e.g., STAR-based expression quantification and transcriptome assembly pipelines, were not observed. Moreover, regardless of using either STAR or HISAT2 as the aligner, variant detection results were affected by lossy quality value compression, albeit to a lesser extent when STAR-based pipeline was used. Our results also show that the impacts of lossy quality value compression depend on the compression algorithms being used and the compression levels if the algorithm supports setting of multiple compression levels. Conclusions Lossy quality value compression can be incorporated into existing RNA-seq analysis pipelines to alleviate the data storage and transmission burdens. However, care should be taken on the selection of compression tools and levels based on the requirements of the downstream analysis pipelines to avoid introducing undesirable adverse effects on the analysis results. Document type: Articl

Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transport

Human activity recognition (HAR) with wearables is one of the serviceable technologies in ubiquitous and mobile computing applications. The sliding-window scheme is widely adopted while suffering from the multi-class windows problem. As a result, there is a growing focus on joint segmentation and recognition with deep-learning methods, aiming at simultaneously dealing with HAR and time-series segmentation issues. However, obtaining the full activity annotations of wearable data sequences is resource-intensive or time-consuming, while unsupervised methods yield poor performance. To address these challenges, we propose a novel method for joint activity segmentation and recognition with timestamp supervision, in which only a single annotated sample is needed in each activity segment. However, the limited information of sparse annotations exacerbates the gap between recognition and segmentation tasks, leading to sub-optimal model performance. Therefore, the prototypes are estimated by class-activation maps to form a sample-to-prototype contrast module for well-structured embeddings. Moreover, with the optimal transport theory, our approach generates the sample-level pseudo-labels that take advantage of unlabeled data between timestamp annotations for further performance improvement. Comprehensive experiments on four public HAR datasets demonstrate that our model trained with timestamp supervision is superior to the state-of-the-art weakly-supervised methods and achieves comparable performance to the fully-supervised approaches.Comment: Under Review (submitted to IEEE TMC

Model based wind turbine gearbox fault detection on SCADA data

Developing effective wind turbine fault detection algorithm is not only meaningful for improving wind turbine reliability but also crucial for future intelligent wind farm operation and management. Typical wind turbine gearbox condition monitoring is based on vibration signals, which is effective to detect failures with high frequency signal range. But it may not be effective on low speed components which have low frequency signal characteristic of different failure modes. SCADA system collecting multiple low frequency signals provides a cost-effective way to monitor wind turbines health and performance, while its capability on fault detection is still an open issue. To systematic understand wind turbine systems, this paper presents research results of model based wind turbine gearbox fault detection. Through a detail analysis of thermodynamic process of gearbox lubrication system, a wind turbine drive train model which considers heat transferring mechanism in gearbox lubrication system is built to derive robust relationships between transmission efficiency, temperature, and rotational speed signals of wind turbine gearbox and suggest useful information for lubrication system design and optimization. The result obtained in this work is useful for wind turbine gearbox design and effective algorithm development of fault detection

TCF1 and LEF1 Control Treg Competitive Survival and Tfr Development to Prevent Autoimmune Diseases.

CD4+ Foxp3+ T regulatory (Treg) cells are key players in preventing lethal autoimmunity. Tregs undertake differentiation processes and acquire diverse functional properties. However, how Treg's differentiation and functional specification are regulated remains incompletely understood. Here, we report that gradient expression of TCF1 and LEF1 distinguishes Tregs into three distinct subpopulations, particularly highlighting a subset of activated Treg (aTreg) cells. Treg-specific ablation of TCF1 and LEF1 renders the mice susceptible to systemic autoimmunity. TCF1 and LEF1 are dispensable for Treg's suppressive capacity but essential for maintaining a normal aTreg pool and promoting Treg's competitive survival. As a consequence, the development of T follicular regulatory (Tfr) cells, which are a subset of aTreg, is abolished in TCF1/LEF1-conditional knockout mice, leading to unrestrained T follicular helper (Tfh) and germinal center B cell responses. Thus, TCF1 and LEF1 act redundantly to control the maintenance and functional specification of Treg subsets to prevent autoimmunity