Cause Identification of Electromagnetic Transient Events using Spatiotemporal Feature Learning

This paper presents a spatiotemporal unsupervised feature learning method for cause identification of electromagnetic transient events (EMTE) in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurement, and using the convolutional neural network (CNN) as the spatiotemporal feature representation along with softmax function. Despite the existing threshold-based, or energy-based events analysis methods, such as support vector machine (SVM), autoencoder, and tapered multi-layer perception (t-MLP) neural network, the proposed feature learning is carried out with respect to both time and space. The effectiveness of the proposed feature learning and the subsequent cause identification is validated through the EMTP simulation of different events such as line energization, capacitor bank energization, lightning, fault, and high-impedance fault in the IEEE 30-bus, and the real-time digital simulation (RTDS) of the WSCC 9-bus system.Comment: 9 pages, 7 figure

Applied Sensor Fault Detection, Identification and Data Reconstruction

Sensor fault detection and identification (SFD/I) has attracted considerable attention in military applications, especially when safety- or mission-critical issues are of paramount importance. Here, two readily implementable approaches for SFD/I are proposed through hierarchical clustering and self-organizing map neural networks. The proposed methodologies are capable of detecting sensor faults from a large group of sensors measuring different physical quantities and achieve SFD/I in a single stage. Furthermore, it is possible to reconstruct the measurements expected from the faulted sensor and thereby facilitate improved unit availability. The efficacy of the proposed approaches is demonstrated through the use of measurements from experimental trials on a gas turbine. Ultimately, the underlying principles are readily transferable to other complex industrial and military systems

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Vibration-based adaptive novelty detection method for monitoring faults in a kinematic chain

Postprint (published version

Anomaly Detection using Autoencoders in High Performance Computing Systems

Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or statistical regression models in a supervised fashion, meaning that the detection tool is trained to distinguish among a fixed set of behaviour classes (healthy and unhealthy states). We propose a novel approach for anomaly detection in High Performance Computing systems based on a Machine (Deep) Learning technique, namely a type of neural network called autoencoder. The key idea is to train a set of autoencoders to learn the normal (healthy) behaviour of the supercomputer nodes and, after training, use them to identify abnormal conditions. This is different from previous approaches which where based on learning the abnormal condition, for which there are much smaller datasets (since it is very hard to identify them to begin with). We test our approach on a real supercomputer equipped with a fine-grained, scalable monitoring infrastructure that can provide large amount of data to characterize the system behaviour. The results are extremely promising: after the training phase to learn the normal system behaviour, our method is capable of detecting anomalies that have never been seen before with a very good accuracy (values ranging between 88% and 96%).Comment: 9 pages, 3 figure

Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

Peer reviewedPostprin

Enhanced Industrial Machinery Condition Monitoring Methodology based on Novelty Detection and Multi-Modal Analysis

This paper presents a condition-based monitoring methodology based on novelty detection applied to industrial machinery. The proposed approach includes both, the classical classification of multiple a priori known scenarios, and the innovative detection capability of new operating modes not previously available. The development of condition-based monitoring methodologies considering the isolation capabilities of unexpected scenarios represents, nowadays, a trending topic able to answer the demanding requirements of the future industrial processes monitoring systems. First, the method is based on the temporal segmentation of the available physical magnitudes, and the estimation of a set of time-based statistical features. Then, a double feature reduction stage based on Principal Component Analysis and Linear Discriminant Analysis is applied in order to optimize the classification and novelty detection performances. The posterior combination of a Feed-forward Neural Network and One-Class Support Vector Machine allows the proper interpretation of known and unknown operating conditions. The effectiveness of this novel condition monitoring scheme has been verified by experimental results obtained from an automotive industry machine.Postprint (published version

Online coherency identification and stability condition for large interconnected power systems using an unsupervised data mining technique

Identification of coherent generators and the determination of the stability system condition in large interconnected power system is one of the key steps to carry out different control system strategies to avoid a partial or complete blackout of a power system. However, the oscillatory trends, the larger amount data available and the non-linear dynamic behaviour of the frequency measurements often mislead the appropriate knowledge of the actual coherent groups, making wide-area coherency monitoring a challenging task. This paper presents a novel online unsupervised data mining technique to identify coherent groups, to detect the power system disturbance event and determine status stability condition of the system. The innovative part of the proposed approach resides on combining traditional plain algorithms such as singular value decomposition (SVD) and K -means for clustering together with new concept based on clustering slopes. The proposed combination provides an added value to other applications relying on similar algorithms available in the literature. To validate the effectiveness of the proposed method, two case studies are presented, where data is extracted from the large and comprehensive initial dynamic model of ENTSO-E and the results compared to other alternative methods available in the literature