Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction

This document is the Accepted Manuscript of the following article: Mohammed Chalouli, Nasr-eddine Berrached, and Mouloud Denai, ‘Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction’, Journal of Failure Analysis and Prevention, Vol. 17 (5): 1053-1066, October 2017. Under embargo. Embargo end date: 31 August 2018. The final publication is available at Springer via DOI: https://doi.org/10.1007/s11668-017-0343-y.Finding reliable condition monitoring solutions for large-scale complex systems is currently a major challenge in industrial research. Since fault diagnosis is directly related to the features of a system, there have been many research studies aimed to develop methods for the selection of the relevant features. Moreover, there are no universal features for a particular application domain such as machine diagnosis. For example, in machine bearing fault diagnosis, these features are often selected by an expert or based on previous experience. Thus, for each bearing machine type, the relevant features must be selected. This paper attempts to solve the problem of relevant features identification by building an automatic fault diagnosis process based on relevant feature selection using a data-driven approach. The proposed approach starts with the extraction of the time-domain features from the input signals. Then, a feature reduction algorithm based on cross-correlation filter is applied to reduce the time and cost of the processing. Unsupervised learning mechanism using K-means++ selects the relevant fault features based on the squared Euclidian distance between different health states. Finally, the selected features are used as inputs to a self-organizing map producing our health indicator. The proposed method is tested on roller bearing benchmark datasets.Peer reviewe

Applied fault detection and diagnosis for industrial gas turbine systems

The paper presents readily implementable approaches for fault detection and diagnosis (FDD) based on measurements from multiple sensor groups, for industrial systems. Specifically, the use of hierarchical clustering (HC) and self-organizing map neural networks (SOMNNs) are shown to provide robust and user-friendly tools for application to industrial gas turbine (IGT) systems. HC fingerprints are found for normal operation, and FDD is achieved by monitoring cluster changes occurring in the resulting dendrograms. Similarly, fingerprints of operational behaviour are also obtained using SOMNN based classification maps (CMs) that are initially determined during normal operation, and FDD is performed by detecting changes in their CMs. The proposed methods are shown to be capable of FDD from a large group of sensors that measure a variety of physical quantities. A key feature of the paper is the development of techniques to accommodate transient system operation, which can often lead to false-alarms being triggered when using traditional techniques if the monitoring algorithms are not first desensitized. Case studies showing the efficacy of the techniques for detecting sensor faults, bearing tilt pad wear and early stage pre-chamber burnout, are included. The presented techniques are now being applied operationally and monitoring IGTs in various regions of the world

Techniques for detecting bearings and gears defects

International audienc

Towards Big Data Analytics in Large-Scale Federations of Semantically Heterogeneous IoT Platforms

Part 1: SEDSEALInternational audienceThe technological advances in the Internet-of-Things (IoT) have led to the generation of large amounts of data and the production of a large number of IoT platforms for their management. The abundance of raw data necessitates the use of data analytics in order to extract useful patterns for decision making. Current architectures for big data analytics in the IoT domain address the large volume and velocity of the produced data. However, they do not address the semantic heterogeneity in the data models used by diverse IoT platforms, which emerges when large-scale deployments, spanning across multiple deployment sites, are considered. This paper proposes an architecture for big data analytics in the context of large-scale IoT systems consisting of multiple IoT platforms. A Semantic Interoperability Layer (SIL) handles the interoperability among the data models of the individual platforms, using semantic mappings between them and a unified ontology. Data queries to the SIL and result collection is handled by a cloud-based data management layer, namely the Data Lake, along with storage of metadata needed by data analytics methods. Based on this infrastructure, web-based data analytics and visual analytics methods are used to analyze the collected data, while being agnostic of platform-specific details. The proposed architecture is developed in the context of healthcare provision for older people, although it can be applied to any IoT domain

ETFE-based anion-exchange membrane ionomer powders for alkaline membrane fuel cells: A first performance comparison of head-group chemistry

In the last few years, the development of radiation-grafted powder-form anion-exchange ionomers (AEI), used in combination with anion-exchange membranes (AEM), has led to the assembly of AEM-based fuel cells (AEMFC) that routinely yield power densities ranging between 1-2 W cm-2 (with a variety of catalysts). However, to date, only benzyltrimethylammonium-type powder AEIs have been evaluated in AEMFCs. This study presents an initial evaluation of the relative AEMFC power outputs when using a combination of ETFE-based radiation-grafted AEMs and AEIs containing three different head-group chemistries: benzyltrimethylammonium (TMA), benzyl-N-methylpyrrolidinium (MPY), and benzyl-N-methylpiperidinium (MPRD). The results from this study strongly suggest that future research should focus on the development and operando long-term durability testing of AEMs and AEIs containing the MPRD head-group chemistryThe research was funded by the Engineering and Physical Sciences Research Council (EPSRC grants EP/M014371/1, EP/M022749/1, and EP/M005933/1). ALGB's exchange was funded by FAPESP grants 2016/13277-9 and 2015/09210-3, while EIS' exchange was funded by FAPESP grants 2015/23621-6, 2014/09087-4 and 2014/50279-4. DH's student-exchange was funded by a PDIF Short Stay Scholarship of the Autonomous University of Madrid. GS' 2015 exchange was funded by the ERASMUS+work placement schem

ETFE-based anion-exchange membrane ionomer powders for alkaline membrane fuel cells: a first performance comparison of head-group chemistry

In the last few years, the development of radiation-grafted powder-form anion-exchange ionomers (AEI), used in combination with anion-exchange membranes (AEM), have led to the assembly of AEM-based fuel cells (AEMFC) that routinely yield power densities ranging between 1 – 2 W cm-2 (with a variety of catalysts). However, to date, only benzyltrimethyammonium-type powder AEIs have been evaluated in AEMFCs. This study presents an initial evaluation of the relative AEMFC power outputs when using a combination of ETFE-based radiation-grafted AEMs and AEIs containing three different head-group chemistries: benzyltrimethylammonium (TMA), benzyl-N-methylpyrrolidinium (MPY), and benzyl-N-methylpiperidinum (MPRD). The results from this study strongly suggest that future research should focus on the development and operando long-term durability testing of AEMs and AEIs containing the MPRD head-group chemistry