PHM in practice
Background and motivation
In the field of Prognostics and Health Management (PHM), a lot of methods, techniques and (AI) algorithms are available, and many more are currently being developed. However, when applying these techniques in (industrial) practice, a lot of hurdles are faced, meaning that, on many occasions, the original enthusiasm changes into frustration, and implementations are not completed. Typical challenges are the lack of (relevant) data (or missing, inaccurate, non-labeled data), limited number of real failures due to preventive maintenance (which makes training of algorithms and validation difficult), missing guidance in the selection of proper parameters / suitable components / subsystems for PHM, etc.
In this set of tutorials, these practical issues are addressed from different viewpoints. They will make both researchers and potential users aware of the challenges and limitations. At the same time, they will also provide potential solutions or ways to get around. This will hopefully inspire people from other fields to tackle their specific issues in a similar way.
Format and planning
The tutorial program during PHME21 contains three sessions. After a short introduction, the speakers will cover their topic in an online presentation. However, ways to interact with the speakers will be provided, e.g. for discussion or asking questions.
To allow attendants to maximize the benefits of the tutorials, some preparatory material will be provided in the weeks before the conference: links to relevant papers, books or websites will allow participants to assess the topic and already gain some basic understanding on that. Further, suggestions will be given for presentations / papers in the regular sessions of PHME21 that align with specific topics in the tutorials and can thus be followed for further details on those topics.
The content of the three tutorials is explained below.
Practical issues and challenges in Predictive Maintenance
Prof. dr. ir. Tiedo Tinga – professor at the University of Twente and Netherlands Defence Academy
In this tutorial the practical issues and challenges of applying Predictive Maintenance (or Prognostics) in industrial practice will be discussed. After a short introduction of Predictive Maintenance and its two underlying approaches, i.e. data-driven and (physical) model-based, the main challenges will be identified. These challenges, e.g. the lack of relevant data, limited number of failures, component vs. system level methods and the selection of suitable methods, will be discussed one-by-one, using examples and case studies from practice to illustrate them. Also, directions for solving these challenges will be provided. Examples from our research, with applications in the military field, but also in wind energy, rail and infrastructure will be used to clarify the challenges and solutions.
Challenges in data science application in healthcare
Huiqi (Yvonne) Lu, Ph.D, MIEEE, FHEA – Research Fellow at the Computational Health Informatics Lab, University of Oxford
Dr. Samaneh Kouchaki – Lecturer in Machine Learning for Healthcare, Centre for Vision, Speech, and Signal Processing, University of Surrey
Ever-increasing qualities and quantities of clinical data are routinely collected concerning all aspects of patient care. Electronic health records offer a rich source of clinical information. Developments in wearable sensors, smart home technologies, and the Internet of Things provide the health industry with opportunities to monitor patients either in hospital or in home settings without significant disruption to their everyday activities. Innovations arising from machine learning approaches can facilitate rapid clinical intervention, transform a hospital-only treatment pathway into a cost-effective, home-based, combined alternative, and thus improve the overall quality of patient healthcare. However, analysing real-time collected data poses several challenges as the data can have substantial artefacts, might be highly imbalanced and incomplete, and might contain high variations. Moreover, data labelling can be expensive and time-consuming if there is an insufficient number of high-quality labels. This tutorial will introduce several machine learning techniques to tackle these issues and to provide robust solutions to address such challenges.
Domain Adaptation for Fault Diagnosis with Deep Learning
Mr. Qin Wang – PhD student at the Chair of Intelligent Maintenance Systems, ETH Zürich (ETHZ)
Thanks to the digitization of industrial assets and the large amount data coming with it, deep-learning-based fault diagnosis models nowadays are showing promising results for all kinds of fault diagnosis applications. Despite of the great potential of these methods, their ability to generalize on new machines and new working conditions are currently limited because of their tendency to overfit to the training set. This domain shift between the training and testing data can lead to poor test performance. In this tutorial, we will introduce one promising solution to this problem: domain adaptation (DA). DA methods can alleviate the distribution difference between the source and target domains, thus improve the performance on the target. We will first introduce the basic concepts and traditional methods for DA. We will then carefully justify the applicability of these methods in realistic fault diagnosis settings. In addition, we will show some common challenges we face when adopting DA methods to the fault diagnosis context. Finally, we will give perspectives and outlook of DA for fault diagnosis in the deep learning era.
The final part of this tutorial will be a hands-on session, where the participants can work in a Jupyter notebook along with the lecturer, demonstrating some of the techniques discussed.