A Methodology for the Diagnostic of Aircraft Engine Based on Indicators Aggregation

Aircraft engine manufacturers collect large amount of engine related data during flights. These data are used to detect anomalies in the engines in order to help companies optimize their maintenance costs. This article introduces and studies a generic methodology that allows one to build automatic early signs of anomaly detection in a way that is understandable by human operators who make the final maintenance decision. The main idea of the method is to generate a very large number of binary indicators based on parametric anomaly scores designed by experts, complemented by simple aggregations of those scores. The best indicators are selected via a classical forward scheme, leading to a much reduced number of indicators that are tuned to a data set. We illustrate the interest of the method on simulated data which contain realistic early signs of anomalies.Comment: Proceedings of the 14th Industrial Conference, ICDM 2014, St. Petersburg : Russian Federation (2014

An intelligent information forwarder for healthcare big data systems with distributed wearable sensors

© 2016 IEEE. An increasing number of the elderly population wish to live an independent lifestyle, rather than rely on intrusive care programmes. A big data solution is presented using wearable sensors capable of carrying out continuous monitoring of the elderly, alerting the relevant caregivers when necessary and forwarding pertinent information to a big data system for analysis. A challenge for such a solution is the development of context-awareness through the multidimensional, dynamic and nonlinear sensor readings that have a weak correlation with observable human behaviours and health conditions. To address this challenge, a wearable sensor system with an intelligent data forwarder is discussed in this paper. The forwarder adopts a Hidden Markov Model for human behaviour recognition. Locality sensitive hashing is proposed as an efficient mechanism to learn sensor patterns. A prototype solution is implemented to monitor health conditions of dispersed users. It is shown that the intelligent forwarders can provide the remote sensors with context-awareness. They transmit only important information to the big data server for analytics when certain behaviours happen and avoid overwhelming communication and data storage. The system functions unobtrusively, whilst giving the users peace of mind in the knowledge that their safety is being monitored and analysed

Improving the altimetric rain record from Jason-1 & Jason-2

Dual-frequency rain-flagging has long been a standard part of altimetric data analysis, both for quality control of the data and for the study of rain itself, because altimeters can provide a finer spatial sampling of rain than can passive microwave instruments. However, there have been many varied implementations, using different records of the surface backscatter and different thresholds. This paper compares four different measures available for the recently-launched Jason-2. The evaluation compares these measures against clearly desired properties, finding that in most cases the adjusted backscatter and that from the ice retracker perform much better than that recommended in the users' handbook. The adjusted backscatter measure also provides a much better link to observations from Jason-1, opening up a much longer period for consistent rain investigations, and enabling greatly improved analysis of the short-scale variability of precipitation. Initial analysis shows that although the spatial and temporal gradients of backscatter increase at very low winds, the spatial gradients in rain attenuation are concentrated where rainfall is greatest, whilst the temporal changes have a simple broad latitudinal pattern