Maximising Insights from Data: Processing, Analysis and Beyond
This seminar offers a comprehensive exploration into the core principles and practices underlying the processing and analysis of data through various methodologies. Following on from our From Sensors to Certainty seminar, the day will look into what we do with our data once we have collected it, and how we can utilise it to answer the original question. Attendees will gain insight into the essentials of turning numbers and curves into answers and solutions, ultimately equipping them with the knowledge necessary to optimise data processing and analysis for enhanced confidence in decision-making.
Data pre-processing and denoising for damage detection - Andrew Halfpenny, HBK
An effective Structural Health Monitoring (SHM) system relies on good quality measurement data. All measured data are susceptible to noise or errors which are introduced at various stages along the signal path. Some errors gradually develop as the system ages, while others are apparent only under certain operating conditions. In this presentation we introduce the most common types of measurement errors and discuss methods for detecting and, where possible, removing them to recover the original measured signal. The presentation begins with an overview describing stages along the signal path and the types of error commonly introduced. We describe the most common configuration errors associated with SHM data acquisition and show how errors can be identified, remedied or avoided through good system design. The term ‘noise’ is used to describe an undesirable component within the signal. The presentation continues by categorizing the types of noise, identifying the cause of noise and presenting methods of denoising measured data. We conclude by describing techniques that are particularly suited to the analysis of rotating machinery.
Do you know your result is the right result? David Ensor - Consultant
We are all interested in collecting and analysing data as input to analysis, investigating problems or comparing results. In recent years it has become extremely easy to obtain transducers, data collection equipment and data analysis tools that are easy to use, generally are trouble free, and more importantly provide “instant” results and answers. Now there is nothing wrong with this, of course. Anything that makes Engineering easier, saving time/money and more consistent is great. However, as instrumentation and analysis become more like “black boxes” and analysis tools provide instant answers we also have to ask ourselves whether they are providing the best answer for me or the situation, or just an answer. A theoretically correct answer to the wrong problem is a case in point. Worse still is applying a default set of assumptions or settings to any problem. Rashly assuming what a problem is can often lead to a very good set of tests and analysis, that even if done correctly may not solve the problem. Similarly, there are many examples of data collection and analysis projects where two sets of engineers have produced very different results from the same data, test or analysis. I will use some simple real-world examples of very wrong results even when using very good equipment, and tools. There will also be some strategies and examples of how to ensure you can reduce the possibility of false results, and make sure your answers are believable and reproduceable.
Measurement Data Management & Validation - Eric Schwarzenberger, Kistler
The proliferation of modern data acquisitions systems in industry, generally lead to a large volume of measurement data. It becomes a challenge for organisations to efficiently check and store data. This presentation explores how this data can be managed, so that it is accessible and utilised by relevant engineers.
Beyond physical testing. The use of ML/AI and Virtual Sensing to get more out of your measurement - Bart Verrecas, Siemens
The worldwide trends of reducing the number of available prototypes, while at the same time creating more product variants, have an immense impact on how physical tests will be done in the future. This calls for innovative solutions, such as the use of Machine Learning or Virtual Sensing technologies. This presentation will demonstrate a number of solutions and research projects currently running that can help to significantly extend the amount of information obtained from a single physical test.
Vehicle Based Objective Tyre Test - Taking rig test out into the real-world - Philip Pavey & George Walker, JLR
Fusing best-in-class sensor technologies enables JLR to develop the in-house virtual tyre models used in our driving simulator and engineering toolsets. We give an overview of the data acquisition system, the considerations to integrate different sensor technologies and its influence on how we process the data to ultimately build our virtual tyre models.
Computational Science: Solutions for Every Engineering Challenge – Ivan Nikulin, Altair
Altair are a global technology company, driving the convergence of Simulation, Data Analytics, and High-Performance Computing. This talk delves into the practical aspects of applying these technologies within the engineering domain, alongside examples of real-world applications. We’ll explore key analytics components such as data preparation, predictive modelling, and prescriptive optimization. Additionally, we’ll discuss the integration of simulation, covering aspects including finite element analysis, design optimization, and virtual testing. To close, we will see how the convergence of these different technologies represent an exciting opportunity with the application of digital twins. Join us for a focused exploration of how these tools can be effectively implemented to enhance engineering processes and improve outcomes, whatever your data availability.
Vibration and Shock Test Profile Generation – Field Data To Laboratory Test (Rocket Science or Dark Arts?) - Marc Brown, Vibration Research
(A look into the magic used to generate vibration tests from field data – taking some of the dark arts out of vibration test generation).
A review of the various vibration and shock test types used in today’s vibration testing and how field data can be used to generate (and compare) vibration and shock tests. There are a number of different types of vibration test possible in the laboratory, but how can you generate your own vibration test from field data. This presentation will be of particular interest to anyone that has an interest in shaker based vibration testing, but specifically those wishing to generate their own tests from field data.
Marc Brown, Vibration Research
Marc has over 30 years of experience working in UKAS accredited vibration test facilities across the UK and is currently an applications engineer working for Vibration Research. He started his career as an apprentice at British Aerospace but has since worked within environmental and vibration testing laboratories across the globe in industries including aerospace, defence and automotive as well as commercial test facilities. He has been involved with testing products ranging from precision inertial navigation systems to large ISO containers using some of the smallest and largest shaker systems available in the world today. At Vibration Research, Marc is responsible for running all aspects of the UK Office including business development, technical support and training. Marc is particularly keen to promote new innovations and best practise in shaker based vibration and shock testing.
(.....and he also enjoys riding horses in his spare time and is currently doing research with his daughter into hoof impact analysis with Cambridge University)
David Ensor - Consultant
David has some 40 years’ experience in the automotive & supplier industry gained in many companies worldwide. He is a specialist in fatigue analysis, instrumentation, durability, data analysis & RLD and has worked in component durability & full vehicle durability development. He managed development departments until becoming a Senior Consultant at HORIBA-MIRA. David has developed durability schedules for numerous world markets and delivered durability programs for a range of OEMs & Tier 1 & 2 Suppliers. In addition he has produced many technical papers advancing fatigue & analysis processes.
Eric Schwarzenberger - Kistler Instruments
Eric started his working life as a software developer for web-based business applications. Through many years of developing and designing established and new applications, he gained experience in managing and processing data all the way from databases and file systems, through application logic to user interfaces. His career took him to Kistler in Germany, where he was working on a distributed software for managing, searching and analysing measurement data. After starting as part of the development team, he then focused on working more closely with users to understand their needs and wishes to shape the product and lead the development roadmap as a Product Manager.
Andrew Halfpenny, HBK
Andrew heads technology and innovation for HBK's nCode product brand. He holds a PhD in Mechanical Engineering and a Masters’ degree in Civil and Structural Engineering. Over the years he has introduced many new technologies to the automotive, aerospace and power generation sectors. These include: customer usage monitoring, target customer analysis, proving ground correlation, accelerated laboratory testing and mathematical simulation. His most recent work has been developing methods to measure and improve the performance, durability and reliability of electric vehicles. Andrew holds a European patent for the ‘Damage Monitoring Tag’, and developed the new vibration methods used for qualifying UK military helicopters. He has worked in consultancy with “blue chip” customers across the UK, Europe, Americas and the Far East, and has written publications on Fatigue, Structural Health Monitoring and Digital Signal Processing. Andrew is a founding member of the NAFEMS PSE (Professional Simulation Engineer) Certification scheme, and sits on the NAFEMS committee for Dynamic Testing. He is also a visiting lecturer on structural dynamics and structural health monitoring with the University of Sheffield.
Ivan Nikulin, Altair
Ivan is an Application Engineer at Altair after graduating from Queen Mary University of London with an MSc in Aerospace Structures and Materials. Altair is a global software company and Ivan currently focuses on developing CAE methodologies using finite element analysis software for structural applications, optimisation, and computational fluid dynamics. He works with many engineers across multiple disciplines and sectors. Ivan finds his interest in the convergence areas of topology optimisation, additive manufacturing, and data science.
With a degree in Theoretical Physics from the University of Birmingham, Phil joined JLR's Road Load Data and Analysis group focussed primarily on the durability and robustness attribute. Now, with over a decade of experience, Phil has been responsible for technical liaison and process leadership with international suppliers, supported JLRs durability attribute with real-world correlation for whole vehicle test and vibration testing. During a period outside the Automotive Sector, Phil derived environmental vibration tests for UK military vehicles. In 2021 Phil rejoined JLR and is coordinating physical measurements from a wide range of applications to generate data feeding into JLR's virtual engineering toolsets.
George Walker - JLR
Graduating with a master’s in mechanical engineering from Heriot-Watt University in 2019, George was a part of the Formula Student team HWRacing. He was a member of the suspension team designing the knuckles for two years and team captain in his final year. Towards the end of the graduate scheme, George joined the Tyre CAE team working as a lead on the Vehicle Based Objective Tyre Test (VBOTT) rig. For the past two years, he has worked to broaden the test surfaces that VBOTT can test on as well as improve the data processing toolset that the department uses to produce tyre models.
20 years of experience at Siemens, where he built up his expertise in NVH testing with a focus on the automotive industry. Today, Bart Verrecas is global Business Development Team Manager for Automotive Testing. In this function he and his team work with numerous automotive companies on implementation of new technologies and increasing overall testing efficiency.