Uncertainty informed anomaly scores with deep learning : robust fault detection with limited data

Best Paper Award Lizenzangabe: CC BY 3.0 United StatesQuantifying the predictive uncertainty of a model is an important ingredient in data-driven decision making. Uncertainty quantification has been gaining interest especially for deep learning models, which are often hard to justify or explain. Various techniques for deep learning based uncertainty estimates have been developed primarily for image classification and segmentation, but also for regression and forecasting tasks. Uncertainty quantification for anomaly detection tasks is still rather limited for image data and has not yet been demonstrated for machine fault detection in PHM applications. In this paper we suggest an approach to derive an uncertainty-informed anomaly score for regression models trained with normal data only. The score is derived using a deep ensemble of probabilistic neural networks for uncertainty quantification. Using an example of wind-turbine fault detection, we demonstrate the superiority of the uncertainty-informed anomaly score over the conventional score. The advantage is particularly clear in an "out-of-distribution" scenario, in which the model is trained with limited data which does not represent all normal regimes that are observed during model deployment

Cross-turbine training of convolutional neural networks for SCADA-based fault detection in wind turbines

Machine learning algorithms for early fault detection of wind turbines using 10-minute SCADA data are attracting attention in the wind energy community due to their cost-effectiveness. It has been recently shown that convolutional neural networks (CNNs) can significantly improve the performance of such algorithms. One practical aspect in the deployment of these algorithms is that they require a large amount of historical SCADA data for training. These are not always available, for example in the case of newly installed turbines. Here we suggest a cross-turbine training scheme for CNNs: we train a CNN model on a turbine with abundant data and use the trained network to detect faults in a different wind turbine for which only little data are available. We show that this scheme is able to considerably improve the fault detection performance compared to the scarce data training. Moreover, it is shown to detect faults with an accuracy and robustness which are very similar to the single-turbine scheme, in which training and detection are both done on the same turbine with a large and representative training set. We demonstrate this for two different fault types: abrupt and slowly evolving faults and perform a sensitivity analysis in order to compare the performance of the two training schemes. We show that the cross-turbine scheme works successfully also when training on turbines from another farm and with different measured variables than the target turbine

Transfer learning approaches for wind turbine fault detection using deep learning

Best Paper AwardImplementing machine learning and deep learning algorithms for wind turbine (WT) fault detection (FD) based on 10-minute SCADA data has become a relevant opportunity to reduce the operation and maintenance costs of wind farms. The development of practically implementable algorithms requires addressing the issue of their scalabililty to large wind farms. Two of the main challenges here are reducing the training times and enabling training with scarce or limited data. Both of these challenges can be addressed with the help of transfer learning (TL) methods, in which a base model is trained on a source WT and the learned knowledge is transferred to a target WT. In this paper we suggest three TL frameworks designed to transfer a semi-supervised FD task between turbines. As a base model we use a Convolutional Neural Network (CNN) which has been proven to perform well on the single turbine FD task. We test the three TL frameworks for transfer between WTs from the same farm and from different farms. We conclude that for the purpose of scaling up training for large farms, a simple TL based on linear regression transformation of the target predictions is an attractive high performance solution. For the challenging task of cross-farm TL based on scarce target data we show that a TL framework using combined linear regression and error-correction CNN outperforms the other methods. We demonstrate a scheme that enables the evaluation of different TL frameworks for FD without the need for labeled faults

Early fault detection based on wind turbine SCADA data using convolutional neural networks

Early fault detection in wind turbines using the widely available SCADA data has been receiving growing interest due to its cost-effectiveness. As opposed to the large variety of fault detection methods based on high resolusion vibration data, the use of 10-minute SCADA data alone does not require any additional hardware or data storage solutions and would be immediately implementable in most wind farms. However, the strong variability of these data is challenging and requires significant improvements of existing methods to ensure early and reliable fault detection and isolation. Here we suggest to use Convolutional Neural Networks (CNNs) to enhance the detection accuracy and robustness. We demonstrate the superiority of the CNN model over standard fully connected neural networks (FCNN) using examples for faults with very different time dependent characteristics: an abruptly evolving and a slowly degrading fault. We show that the CNN is able to detect the faults earlier and with a higher accuracy and robustness of prediction than the FCNN model. We then extend the CNN model to a multi-output CNN (CNNm) which provides early fault detection based on a multitude of output variables simultaneously. We show that with the same training time and a similar detection quality as the single output CNN, the CNNm model is an ideal candidate for a practical and scalable fault detection algorithm based on already available 10-minute SCADA data for wind turbines

Deep Learning und Predictive Maintenance : Anwendungsfall Windturbinen

Aus einer kürzlich erfolgten Zusammenarbeit zwischen Nispera, EKZ und dem Smart Maintenance Team der ZHAW ist ein neues, auf Deep-Learning-Algorithmen basierendes Softwaremodul zur vorausschauenden Wartung entstanden. Die Algorithmen sind in der Lage, Abweichungen von der normalen Aktivität der Windturbinen zu erkennen, zukünftige Wartungsmassnahmen planbar zu machen und unnötige Stillstandzeiten zu reduzieren

Implementing AI-based innovation in industry

The Supervisory Control and Data Acquisition (SCADA) system installed on every wind turbine collects performance and condition data from various components of the turbine in time intervals of 10 minutes. The data is stored and has been used primarily for performance monitoring (identifying losses in the power production) until now. Realizing that this vast amount of historical data from all turbines has a much bigger potential, Nispera decided to launch an innovation project to harvest this potential and offer its clients a new platform for automated detection and localization of technical anomalies and faults in various turbine components. Early detection of faults allows for an intelligent planning of maintenance activities, leading to considerable reduction in the Operation and Maintenance (O&M) expenses of the wind farm operator. “Predictive maintenance” approaches start to replace reactive and preventive approaches to maintenance in a large variety of application fields, ranging from the aircraft industry, through trains, large production machines and public infrastructures. Deploying predictive maintenance algorithms is becoming increasingly attractive owing to the huge progress of the last years regarding machine data availability, cost-effective storage solutions and efficient intelligent algorithms for data analytics, including machine learning and deep learning methods. For Nispera’s clients, predictive maintenance is even more attractive because this service is offered within a more generic platform, which has access to the SCADA data without the need for any new hardware installation. This makes Nispera’s solution cost-effective com-pared to other condition monitoring solutions available on the market. In this way, Nispera directly addresses the needs of wind park owners and operators for continuous monitoring of their turbines, independent of the OEMs

Deep learning for fault detection : the path to predictive maintenance of wind turbines

We demonstrate the deployment of a novel deep learning algorithm enabling smart maintenance of wind turbines based on 10 minute SCADA data. The newly developed algorithm has the following advantages over existing solutions: • The algorithms are based on the already available 10-minute SCADA data and do not require any additional hardware installations. • The algorithm has been proven to detect various fault types earlier and more accurately than previous methods in the scientific literature. Incipient faults would have been detected weeks or even months prior to known events of a turbine stoppage. • The method is designed to not only detect faults but also specify their localization within the main critical turbine components. • The algorithm does not require a large amount of historical data for its training. Several months of SCADA data are sufficient. This is enabled due to the possibility to adapt the trained algorithm to detect faults on turbines from different wind farms. As such, it is applicable also to newly installed wind turbines and farms. • The method has proven to be robust against parameter variations and to have short training times. As such, it is an optimal practical and scalable solution for high confidence fault detection and diagnostics for wind turbines based on already available 10-minute SCADA data

An AI-based fault detection model using alarms and warnings from the SCADA system

Predictive maintenance is a key element for lowering Operation and Maintenance (O&M) costs of wind turbines. Predictive maintenance models are usually based on drivetrain vibration data or operational timeseries from the Supervisory Control And Data Acquisition (SCADA) system, while readily available alarms and warnings from the SCADA system are typically not utilized. In this work we present a novel Artificial Intelligence (AI) based approach for early fault detection of wind turbines using alarms and warnings from the SCADA system

Three-dimensional simulations of premixed hydrogen/air flames in microtubes

ISSN:0022-1120ISSN:1469-764

Exploring the Role of GMMA Components in the Immunogenicity of a 4-Valent Vaccine against <i>Shigella</i>

Shigellosis is the leading cause of diarrheal disease, especially in children of low- and middle-income countries, and is often associated with anti-microbial resistance. Currently, there are no licensed vaccines widely available against Shigella, but several candidates based on the O-antigen (OAg) portion of lipopolysaccharides are in development. We have proposed Generalized Modules for Membrane Antigens (GMMA) as an innovative delivery system for OAg, and a quadrivalent vaccine candidate containing GMMA from S. sonnei and three prevalent S. flexneri serotypes (1b, 2a and 3a) is moving to a phase II clinical trial, with the aim to elicit broad protection against Shigella. GMMA are able to induce anti-OAg-specific functional IgG responses in animal models and healthy adults. We have previously demonstrated that antibodies against protein antigens are also generated upon immunization with S. sonnei GMMA. In this work, we show that a quadrivalent Shigella GMMA-based vaccine is able to promote a humoral response against OAg and proteins of all GMMA types contained in the investigational vaccine. Proteins contained in GMMA provide T cell help as GMMA elicit a stronger anti-OAg IgG response in wild type than in T cell-deficient mice. Additionally, we observed that only the trigger of Toll-like Receptor (TLR) 4 and not of TLR2 contributed to GMMA immunogenicity. In conclusion, when tested in mice, GMMA of a quadrivalent Shigella vaccine candidate combine both adjuvant and carrier activities which allow an increase in the low immunogenic properties of carbohydrate antigens