For a successful realization of prognostics and health management (PHM), the availability of sufficient run-to-failure data sets is a crucial factor. The sheer number of given data points holds less importance than the full coverage of the potential state space. However, full coverage is a major challenge in most industrial applications. Among other things, high investment and operating costs as well as the long service life of many technical systems make it difficult to acquire complete run-to-failure data sets. Consequently, in industrial applications data sets with specific deficiencies are frequently encountered. The development of appropriate methods to address such data scenarios is a fundamental research issue. Therefore, the purpose of this paper is to provide facilitation for this research. Accordingly, the paper starts by specifying the value and availability of data in PHM. Subsequently, criteria for characterizing data sets are defined independent of the actual PHM application. The criteria are used to identify typical data scenarios with specific deficiencies that possess significant relevance for industrial applications. Thereafter, the most comprehensive overview of data sets suitable for PHM and currently publicly accessible is provided. Thereby, not all previously identified data scenarios with their specific deficiencies are addressed by at least one data set. A program is established for the aforementioned facilitation of further research. One objective of the program is to create data sets reflecting these data scenarios using a test bench. First, possible applications and their degradation processes to be studied on the test bench are briefly characterized. Thereby, the final decision to select filtration as a test bench application is argued. Subsequently, the test bench created is introduced, including a description of the functional concept, pneumatic layout and components involved, as well as the filter media and test dusts employed. Typical run-to-failure trajectories are illustrated. Thereafter, the data set published under the name Preventive to Predictive Maintenance is presented. Additionally, a schedule for future releases of data sets on further industry-relevant data scenarios is sketched.
Approaches for diagnosis and prognosis of the health of engineering systems are divided into data-driven, model-based, and hybrid methods. Data-driven methods depend on the availability of data. Model-based methods require knowledge of the degradation process. A great effort for data generation along with the high complexity of degradation processes often limits both approaches. To mitigate these limitations, the combination of data and knowledge through hybrid methods is examined in this paper. This approach is compared to the alternative approach of reducing the effort for generating training data, as both are gaining importance in diagnostics and prognostics. A new categorization of hybrid prognostic methods for combining data-driven and physics-based models is presented, along with references to existing realizations of these methods. Based on the categorization, a case study on the hybrid remaining useful life prediction of a filtration process is conducted. Several hybrid methods are implemented and tested in this study. Through the combination of models, an improvement in predictive accuracy is achieved. In addition, the paper examines systematic attributes of the individual hybrid methods. Statements on the influence of data scarcity on the predictive accuracy, data-driven models with high variance, and the computational efficiency of the hybrid methods are made. It is shown that these statements are supported by the case study's results.
In Prognostics and Health Management, there are three main approaches for implementing diagnostic and prognostic applications. These approaches are data-driven methods, physical model-based methods, and combinations of them, in the form of hybrid methods. Each of them has specific advantages but also limitations for their purposeful implementation. In the case of data-driven methods, one of the main limitations is the availability of sufficient training data that adequately cover the relevant state space. For model-based methods, on the other hand, it is often the case that the degradation process of the considered technical system is of significant complexity. In such a scenario physics-based modeling requires great effort or is not possible at all. Combinations of data-driven and model-based approaches in form of hybrid approaches offer the possibility to partially mitigate the shortcomings of the other two approaches, however, require a sufficiently detailed data-driven and physics-based model. This paper addresses the transitional field between data-driven and hybrid approaches. Despite the issues of formulating a physics-based model that provides a representation of the degradation process, basic knowledge of the considered system and of the laws governing its degradation process is usually available. Integration of such knowledge into a machine learning process is part of a research field that is either called theory-guided data science, (physics) informed machine learning, physics-based learning or physics guided machine learning. First, the state of research in Prognostics and Health Management on methods of this field is presented and existing research gaps are outlined. Then, a concept is introduced for incorporating fundamental knowledge, such as monotonicity constraints, into data-driven diagnostic and prognostic applications using approaches from theory-guided data science. A special aspect of this concept is its cross-application usability through the consideration of knowledge that repeatedly occurs in diagnostics and prognostics. This is, for example, knowledge about physically justified boundaries whose compliance makes a prediction of the data-driven model plausible in the first place.
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