A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies

CBM (Condition Based Maintenance) solutions are increasingly present in industrial systems due to two main circumstances: rapid evolution, without precedents, in the capture and analysis of data and significant cost reduction of supporting technologies. CBM programs in industrial systems can become extremely complex, especially when considering the effective introduction of new capabilities provided by PHM (Prognostics and Health Management) and E-maintenance disciplines. In this scenario, any CBM solution involves the management of numerous technical aspects, that the maintenance manager needs to understand, in order to be implemented properly and effectively, according to the company’s strategy. This paper provides a comprehensive representation of the key components of a generic CBM solution, this is presented using a framework or supporting structure for an effective management of the CBM programs. The concept “symptom of failure”, its corresponding analysis techniques (introduced by ISO 13379-1 and linked with RCM/FMEA analysis), and other international standard for CBM open-software application development (for instance, ISO 13374 and OSA-CBM), are used in the paper for the development of the framework. An original template has been developed, adopting the formal structure of RCM analysis templates, to integrate the information of the PHM techniques used to capture the failure mode behaviour and to manage maintenance. Finally, a case study describes the framework using the referred template.Gobierno de Andalucía P11-TEP-7303 M

Eddy current inspection of graphite fiber components

The recognition of defects in materials properties still presents a number of problems for nondestructive testing in aerospace systems. This project attempts to utilize current capabilities in eddy current instrumentation, artificial intelligence, and robotics in order to provide insight into defining geometrical aspects of flaws in composite materials which are capable of being evaluated using eddy current inspection techniques. The unique capabilities of E-probes and horseshoe probes for inspecting probes for inspecting graphite fiber materials were evaluated and appear to hold great promise once the technology development matures. The initial results are described of modeling eddy current interactions with certain flaws in graphite fiber samples

On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

Electricity clustering framework for automatic classification of customer loads

Clustering in energy markets is a top topic with high significance on expert and intelligent systems. The main impact of is paper is the proposal of a new clustering framework for the automatic classification of electricity customers’ loads. An automatic selection of the clustering classification algorithm is also highlighted. Finally, new customers can be assigned to a predefined set of clusters in the classificationphase. The computation time of the proposed framework is less than that of previous classification tech- niques, which enables the processing of a complete electric company sample in a matter of minutes on a personal computer. The high accuracy of the predicted classification results verifies the performance of the clustering technique. This classification phase is of significant assistance in interpreting the results, and the simplicity of the clustering phase is sufficient to demonstrate the quality of the complete mining framework.Ministerio de Economía y Competitividad TEC2013-40767-RMinisterio de Economía y Competitividad IDI- 2015004

Ayer Hitam Forest (AHFR) from space using satellite remote sensing

There is a high demand to map and monitor the land use and assess their condition for ecological and economic reasons. Information on existing land and cover and their spatial distribution is a pre-requisite for any planning, development and management programme. In this study, Landsat TM data of 1998 were acquired over the AHFR and it's vicinity which covers an area more than 1, 300 ha. The objective of this paper is to map AHFR and assess the land cover of AHFR in 1998 as well as its surrounding area using remote sensing technology. Digital data processing and analysis were carried out using PCI/EASI PACE software, version 6.2 available in Faculty of Forestry, UPM. A false Colour Composite (FCC) of Landsat TM band 4-5-3 (R-G-B) was used in supervised classification using Maximum Likelihood Classifier (MLC). From a visual interpretation, several features of AHFR could be identified such as federal road, forest road, cleared land, built-up area, oil palm, water bodies and rubber plantation etc. Meanwhile, digital classification showed that seven land use types surrounding AHFR such as forest, secondary forest/shrubs, oil palm, rubber, built-up area, cleared land and water bodies could a easily be mapped out. The mean overall classification accuracy obtained is 86.08 percent with an average accuracy o] 85.64 percent. Satellite map of AHFR is found to be useful for the macro planning and management purposes especially on the Environmental Impact Assessment (EIA) if further development on the area is to be politicized

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Understanding Hidden Memories of Recurrent Neural Networks

Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for NLP tasks. We propose a technique to explain the function of individual hidden state units based on their expected response to input texts. We then co-cluster hidden state units and words based on the expected response and visualize co-clustering results as memory chips and word clouds to provide more structured knowledge on RNNs' hidden states. We also propose a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN's hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts.Comment: Published at IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2017