Multiharmonic Resonance Control Testing of an Internally Resonant Structure

The experimental characterisation of a nonlinear structure is a challenging process, particularly for multiple degree of freedom and continuous structures. Despite attracting much attention from academia, there is much work needed to create processes that can achieve characterisation in timescales suitable for industry, and a key to this is the design of the testing procedure itself. This work proposes a passive testing method that seeks a desired degree of resonance between forcing and response. In this manner, the process automatically seeks data that reveals greater detail of the underlying nonlinear normal modes than a traditional stepped sine method. Furthermore, the method can target multiple harmonics of the fundamental forcing frequency, and is therefore suitable for structures with complex modal interactions. The method is presented with some experimental examples, using a structure with a 3:1 internal resonance.

Multi-Sensor Acoustic Parameter Analysis System for Monitoring and Evaluation of Deep Drilling, Jetting, and Stimulation Operations

<p>Acoustic Emission (AE) based systems have been under development and used in this research at Fraunhofer – IEG to monitor, evaluate, and control conventional and novel drilling processes and their pertinent equipment used in geothermal applications. Moreover, new stimulation and high pressure (radial) jetting and drilling operations in deep geothermal reservoirs do heavily rely on such new technologies in order to be able to control them properly and thus, to generate an optimal connection between the main wellbore and the reservoir. As Service intervals and lifetime of machines have long been predicted and monitored via Acoustic Emission (AE) systems, and it is becoming a standard in numerous other industrial operations, AE is known as being a promising technique to be used for such monitoring purposes. AE monitoring is based on the detection and conversion of elastic waves into electrical signals, which are typically associated with a rapid release of localized stress-energy propagating within a given material. Thus, it is passive testing, logging, and analysis method to evaluate changes in the properties and behavior of machines and also mineral type materials such as rocks during operations. Such changes may be induced by drilling, jetting, or other drilling methods and being recorded, located, and evaluated via an AE system. This is the core of Fraunhofer – IEG’S new development, the AE based, so-called Multi-Sensor acoustic parameter analysis (MoUSE) as the primary control and monitoring mechanism during rock breaking, drilling, jetting, and stimulation. AE signals generated during jetting or bit-rock interaction are being monitored and analyzed extensively using novel numerical methods, based on sound analysis and engineering applications. The objective of this paper is to present an alternative approach for QA and QC during drilling, jetting, and stimulation operations based on AE waveforms generated during such continuous processes, including jetting and thermal drilling processes. Initial results of rock breaking tests, including mechanical, and non-contact drilling or jetting, will be presented.</p>

Combining Model Inference and Passive Testing in the Same Framework to Test Industrial Systems

Many software engineering approaches often rely on formal models to automate some steps of the software life cycle, particularly the testing phase. Even though automation sounds attractive, writing models is usually a tedious and error-prone task. In addition, with industrial software systems, models are often not up-to-date. Hence, testing these systems becomes problematic. In this context, this article proposes a framework called Autofunk to test production systems by combining two approaches: model generation and passive testing. Given a large set of events collected from a production system, Autofunk combines an expert system, formal models and machine learning to infer symbolic models while preventing over-generalisation. Afterwards, these models are considered to passively test whether another system is conforming to the models. As the generated models do not express all the possible behaviours that should happen, we define conformance with four specialised implementation relations.