Index for asset value measure obtained from condition monitoring digitalized data interpretation. A railway asset management application

Hosted by the Johannes Kepler University, Linz, Austria. May 23-24, 2019The objective of any asset is to provide value to the organization, being the corner stone to get a highest possible economic benefit in a sustainable way. An effective asset value management demands method that allow measuring and comparing the expected value with the real value realized at any time during its life cycle for value informed decision-making. Digitalization is providing new data about events and states related to asset condition and risk, information that can be reinterpreted to generate value measure strategies. This paper presents a proposal of TVO (Total Value of Ownership) model where it is possible to quantify and measure the value, including its monitoring throughout the life cycle of the asset and/or system. Proposed TVO model is focused on Safety, one of the most relevant value factors for Industry and Infrastructure sectors. Asset events and states are intrinsically linked to the defined failure modes. Consequently, it is necessary to structure the system information around the failure modes that have been defined, in order to obtain a value measurement index. A railway use case is presented

Implementing intelligent asset management systems (IAMS) within an industry 4.0 manufacturing environment

9th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2019; Berlin; Germany; 28 August 2019 through 30 August 2019. Publicado en IFAC-PapersOnLine 52(13), p. 2488-2493This paper aims to define the different considerations and results obtained in the implementation in an Intelligent Maintenance System of a laboratory designed based on basic concepts of Industry 4.0. The Intelligent Maintenance System uses asset monitoring techniques that allow, on-line digital modelling and automatic decision making. The three fundamental premises used for the development of the management system are the structuring of information, value identification and risk management

Criticality Analysis for Network Utilities Asset Management

The proposed work describes the main part of asset criticality analysis for Distribution Network Services Providers (DNSP), also known as Network Utilities, the severity-value factors definition. The methodology is based on the risk-based evaluation of assets, considering potential impacts of their failures on network value. Thus, it provides the capability to take maintenance management decision in terms of value and risk, considering the whole network under unique and homogeneous criteria. A hierarchy of assets ranked according to with value and risk will come out of this process, which represents a fundamental result serving as input of the subsequent steps of the asset management process. Specific attention is paid to network utilities issues, characterizing assets in these companies, and the services that they provide. In addition to this, high requirements established by the Service Level Agreements (SLA), that are characteristics of network services contracts, make this methodology especially suitable in this application. In order to illustrate method applicability, an example extracted from a real electrical network use case is included.Unión Europea 64573

Subjectivation machines, surveillance capitalism and algorithms: an approach from the brazilian case

Sob o big data e os algoritmos escondem-se complexas tecnologias de poder que compõem a racionalidade neoliberal, e que almejam prever e condicionar o comportamento coletivo tanto para a manutenção da ordem quanto para a produção de receitas econômicas. O trabalho objetiva analisar o capitalismo de vigilância, a extração e categorização de emoções e traços psíquicos a partir dos dados, ações e padrões comportamentais para o exercício de poder, compreendendo como se produzem essas máquinas de subjetivação, e de que maneira os desejos podem ser manejados para determinados fins políticos. Por fim, o contexto brasileiro encontra-se numa posição de destaque para este estudo, principalmente após as últimas eleições presidenciais e as atuais estratégias utilizadas pelo governo Bolsonaro.Under big-data and algorithms lurk the complex technologies of power that make up neoliberal rationality, and which aim to predict and condition collective behavior for the maintenance of order and the production of economic revenues. The paper aims to analyze surveillance capitalism, the extraction and categorization of emotions and psychic traits from the data, actions and behavioral patterns for the exercise of power, understanding the functioning of these subjectivation machines, and how desires can be channeled or transformed for economic or political purposes, that is, how these strategies of power work, resonate, and produce. Finally, the Brazilian context is in a prominent position for this study, especially after the latest electoral dynamics in 2018 and the current practices of the Bolsonaro government.Ministerio de Economía y Competitividad CSO2016-78386-

Understanding the new Context of Uncertainty and Risk under the 4th Industry Revolution

Proceedings of the 29th European Safety and Reliability Conference (ESREL), 22 – 26 September 2019, Hannover, Germany. Editors, Michael Beer and Enrico ZioThe revolution towards the Industry 4.0, requires as a fundamental challenge the advanced treatment of risk in physical assets according to this new context. This revolution also includes the transition towards a new concept of assets and production systems giving rise to those known as cyber-physical systems (CPS) where the available information and knowledge about the systems and its behaviour should promote a level of control of the risk not known until now. In this context, the transition from classical model for risk management to other concepts, more flexible and dynamic is needed. It is the context that this paper is intended to illustrate, approaching risk control to the available data and technology.Gobierno de España. FFI2017- 89639-P, “Mechanisms in the sciences: from the biological to the social

Temocillin versus meropenem for the targeted treatment of bacteraemia due to third-generation cephalosporin-resistant Enterobacterales (ASTARTÉ): protocol for a randomised, pragmatic trial

Introduction: Alternatives to carbapenems are needed in the treatment of third-generation cephalosporin-resistant Enterobacterales (3GCR-E). Temocillin is a suitable candidate, but comparative randomised studies are lacking. The objective is to investigate if temocillin is non-inferior to carbapenems in the targeted treatment of bacteraemia due to 3GCR-E. Methods and analysis: Multicentre, open-label, randomised, controlled, pragmatic phase 3 trial. Patients with bacteraemia due to 3GCR-E will be randomised to receive intravenously temocillin (2 g three times a day) or carbapenem (meropenem 1 g three times a day or ertapenem 1 g once daily). The primary endpoint will be clinical success 7–10 days after end of treatment with no recurrence or death at day 28. Adverse events will be collected; serum levels of temocillin will be investigated in a subset of patients. For a 10% non-inferiority margin, 334 patients will be included (167 in each study arm). For the primary analysis, the absolute difference with one-sided 95% CI in the proportion of patients reaching the primary endpoint will be compared in the modified intention-to-treat population. Ethics and dissemination: The study started after approval of the Spanish Regulatory Agency and the reference institutional review board. Data will be published in peer-reviewed journals. Trial registration number: NCT04478721.Instituto de Salud Carlos III ICI19/00093Ministerio de Economía, Industria y Competitividad y Fondos FEDER RD16/0016/0001, 0002, 0004, 0008, 0009, 0010, 0011, 0013, 001

A comparison of machine learning techniques for LNG pumps fault prediction in regasification plants

Cuenta con otro editor: IFAC-PapersOnLine Se incluye en el Volumen 53, nº 3 Article number 168746We present a comparative study on the most popular machine learning methods applied to the challenging problem of Liquefied Natural Gas pumps fault prediction in regasification plants. The proposed solution tries to address the problem of pump failure during operation, this failure makes the pump unavailable, with a high cost of corrective maintenance. It must be taken into account that the condition monitoring may be insufficient because they are cryogenic and inaccessible equipment once the tanks have been started up. The use of machine learning techniques allows us to anticipate the response time by detecting anomalies in the operation, and to be able to do the maintenance before the failure occurs. In our experiments, we predict the power consumption based on the parameters captured in real time during operation. For the composition of the dataset, data was collected between 2007 and 2017, resulting in a dataset of over 15,000 lines for training and validation. First, all models were applied and evaluated on a dataset collected from a real case study. In the second phase, the performance improvement offered by boosting was studied. In order to determine the most efficient parameter combinations we compare Root Mean Squared Error, Absolute Error, Relative Error, Squared Error, Correlation, Training Time and Scoring Time. Our results demonstrate clear superiority of the boosted versions of the models against the plain (non-boosted) versions. The fastest scoring and total time was the Decision Tree and the best overall was Gradient Boosted Trees

A Model for Lifecycle Cost Calculation Based on Asset Health Index

During many years, asset management methodologies used in industry were focused on knowing and analysing the operational control of the daily work and the impact of the maintenance on the availability. Later, the costs turn into the priority, and strategies were focused on assessing a longer lifecycle and optimizing processes and contracts. Finally, recent normative have included concepts as “knowing and managing the risks” and the target is to prioritize the maintenance tasks to the critical assets. However, taking a balanced asset management model for the operational environment, quite a lot of facilities of Oil&Gas sector are reaching the end of their initially estimated lifecycle. New challenges are related to extend the life of the main items of the facilities or at least, to find the optimal replacement moment that guarantees that the total expenditure (TOTEX) is being optimized. Asset Health Index (AHI) methodology considers a theoretical lifecycle of an item, in which depending on the proximity to the end of the useful life, the probability of failure increases. But taking this theoretical lifecycle as a base, different operation location factors or operation and maintenance (O&M) aspects can modify this period. All these factors can be quantified and permit us to calculate a new theoretical profile. This paper is case study based on the theoretical procedure and methodology proposed in previous literature (i.e. De la Fuente, Candon et al., 2018) to assess the impact of future failure probabilities of assets on lifecycle cost. The paper shows the impact of the AHI into a profitability calculation. AHI has enabled to compare future alternative cost profiles and assess the impact in the failure probability of the item. As a result, we have been able to change the maintenance strategy and optimise the TOTEX of an industrial equipment