TÜV (Austria)

Acoustic emission (AE) is a phenomenon that occurs in materials that are exposed to stress. Defects in the material, such as cracks can grow and release energy in the form of a mechanical wave that propagates through the material. With the help of piezoelectric sensors, the mechanical wave is converted into an electrical signal. The signals are recorded by the measuring system and are available for further processing. Acoustic emission as a method that records and processes the incoming signals in real time is therefore very suitable for 24/7 long-term structural health monitoring. With new approaches in data processing, acoustic emission can be moved from the classic periodic test approach to an online monitoring process. The in-service monitoring brings measuring challenges with it, like application on hot surfaces and inside explosion hazard areas. New developments in hardware and software provide tailor-made solutions for a variety of monitoring tasks. The use of artificial intelligence algorithms helps to improve the analysis of measurement data and guarantees safe operation of pressure equipment and infrastructure around-the-clock. Some examples of continuous monitoring are shown on pressure vessels from the industry sector and on infrastructure buildings e.g., steel bridges. Also, a new lean system approach in structural health monitoring with acoustic emission is presented.

This paper describes the performance of a laboratory burst test on a test object with a severe real-world crack, the interpretation and evaluation of the acoustic emission (AE) data in general and in particular the results of further AE data trend analysis utilising the failure forecast method at different sections in testing time. The objective of this exercise is to gain experience in application of this method and to check whether or not it could be employed for testing and/or monitoring purposes. The burst pressure prediction as well as the ratio between the actual test pressure and the burst pressure prediction, the failure convergence, are consistent with the usual AE data evaluation throughout the test. Test sequences with a strong progressive increase of activity trend in AE data are highlighted by a gradually increasing failure convergence. This method has the potential to complement state-of-the-art evaluation techniques for testing applications, in particular with activity trend analysis. Due to the good results obtained already with a rather simple approach, remaining useful life prediction for on-line monitoring applications seems to be within reach.

According to the European Commission, Austria is obliged to reduce ammonia emissions by 12% between 2005 and 2030. Agriculture, in particular livestock buildings and the spreading of manure, is the predominant source of ammonia in most countries, calling for stringent mitigation measures in this area. This study investigated a combination of measures implemented in a newly constructed fattening piggery in Styria (Austria) for reducing ammonia, particulate matter (not subject of this publication), and odour emissions. Additionally, the livestock building should meet standards to enhance animal welfare as well. Based on observed ammonia concentrations at several locations in the vicinity of the farm as well as field inspections for odour according to EN 16841-1, corresponding emission factors were derived using the Lagrangian particle model GRAL and in situ measurements of meteorology. The resulting emission factor for ammonia was found to be 80% lower compared to the standard emission factor of 3.64 kg a−1 for fattening piggeries according to the German guideline VDI 3894-1. Moreover, the emission factor for odour was 95% lower than the standard factor of 0.180 ouE kg−1 s−1 used in Styria for conventional fattening piggeries without any reduction techniques.

<div>Connected vehicles and intelligent transportation systems are currently evolving into highly interconnected digital environments. Due to the interconnectivity of different systems and complex communication flows, a joint risk analysis for combining safety and security from a system perspective does not yet exist. We introduce a novel method for joint risk assessment in the automotive sector as a combination of the Diamond Model, Failure Mode and Effects Analysis (FMEA), and Factor Analysis of Information Risk (FAIR). These methods have been sequentially composed, which results in a comprehensive risk management approach to information security in an intelligent transport system (ITS). The Diamond Model serves to identify and structurally describe threats and scenarios, the widely accepted FMEA provides threat analysis by identifying possible error combinations, and FAIR provides a quantitative estimation of probabilities for the frequency and magnitude of risk events. We present the methodology and its step-by-step application on a practice-oriented automotive use case. As a result of this risk management approach, we can finally provide quantitative values from FAIR instead of a qualitative categorization, enabling a more accurate assessment of risks and prioritization of their mitigation. Simultaneously, the FMEA ensures complete risk identification at a component level. The approach is transparent, reusable, and can be adjusted to new estimations or insights easily and at any time, thus addressing the complexity and diversity of services in the transportation domain.</div>

Convex algebras, also called (semi)convex sets, are at the heart of modelling probabilistic systems including probabilistic automata. Abstractly, they are the Eilenberg-Moore algebras of the finitely supported distribution monad. Concretely, they have been studied for decades within algebra and convex geometry. In this paper we study the problem of extending a convex algebra by a single point. Such extensions enable the modelling of termination in probabilistic systems. We provide a full description of all possible extensions for a particular class of convex algebras: For a fixed convex subset $D$ of a vector space satisfying additional technical condition, we consider the algebra of convex subsets of $D$. This class contains the convex algebras of convex subsets of distributions, modelling (nondeterministic) probabilistic automata. We also provide a full description of all possible extensions for the class of free convex algebras, modelling fully probabilistic systems. Finally, we show that there is a unique functorial extension, the so-called black-hole extension.

The aim of the research project EINSTEIN was the development of a technology prototype for the optimization of maintenance strategies for traffic infrastructure based on risk management principles. In addition to the current condition based structural maintenance planning it was aspired to broaden mid-term budget forecasting for existing infrastructure based on the developed risk models in order to evaluate the impact of different maintenance strategies and methodological approaches in terms of direct and indirect Life Cycle Costs. Subsequently the results of this analysis are used to determine the most favourable option from the operational and economic point of view in terms of calculated Life Cycle Management (LCM) proposals. The developed models were implemented into an asset management application and tested by means of two pilot projects for road and railway infrastructure for both clients - ASFINAG (Federal Austrian Highways) and ÖBB (Federal Austrian Railways). The current publication focuses on the comparative analysis of 90 railway bridges along a track section of 54km length.