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Evolutionary Multi-Objective Optimization : Current and Future Research Trends Plenary Talk
| Content Provider | Semantic Scholar |
|---|---|
| Author | Bonissone, Piero P. |
| Copyright Year | 2009 |
| Abstract | Prognostics and Health Management (PHM) is a multidiscipline field, as it includes facets of Electrical Engineering (reliability, design, service), Computer Science and Decision Sciences (Computational Intelligence, Artificial Intelligence, Soft Computing, Machine Learning, Statistics, OR), Mechanical Engineering (geometric models for fault propagation), Material Sciences, etc. Within this talk we will focus on the role that Computational Intelligence (CI) plays in PHM for assets such as locomotives, medical scanners, aircraft engines, etc. functionalities. The main goal of PHM is to maintain these assets’ operational performance over time, improving their utilization while minimizing their maintenance cost. This tradeoff is typical of long-term service agreements offered by OEM’s to their valued customers. The main goal of PHM for assets such as locomotives, medical scanners, and aircraft engines is to maintain these assets’ operational performance over time, improving their utilization while minimizing their maintenance cost. This tradeoff is critical for the proper execution of Contractual Service Agreements (CSA) offered by OEM’s to their valued customers. When addressing real-world PHM problems, we usually deal with systems that are difficult to model and possess large solution spaces. So we augment available physicsbased models, which are usually more precise but difficult to construct, customize, and adapt, with approximate solutions derived from Computational Intelligence methodologies. In this process we leverage two types of resources: problem domain knowledge of the process (or product) and field data that characterize the system’s behavior. The relevant available domain knowledge is typically a combination of first principles and empirical knowledge. This knowledge is often incomplete and sometimes erroneous. The available data are typically a collection of input-output measurements, representing instances of the system's behavior, and are generally incomplete and noisy. Computational Intelligence is a flexible framework in which we can find a broad spectrum of design choices to perform the integration of knowledge and data in the construction of approximate models. To better understand PHM requirements, we introduce a decision-making framework in which we analyze PHM decisional tasks. This framework is the cross product of the decision’s time horizon and the domain knowledge used by CI models. Within such a framework, we analyze the progression from simple to annotated lexicon, morphology, syntax, semantics, and pragmatics. We use this metaphor to monitor the leverage of domain knowledge in CI to perform anomaly detection, anomaly identification, failure mode analysis (diagnostics), estimation of remaining useful life (prognostics), on-board control, and off board logistics actions. This is shown in the following figure. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.computer.org/csdl/proceedings/isda/2009/3872/00/3872z038.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
