# Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios
## 2 Research Questions and Contributions  
The core of a data-driven PdM strategy is transforming raw data into useful information to support the decision-making process. Although ML and DL methods are powerful, industrial contexts impose some constraints on their use in the PdM field for several reasons. The first limitation is the lack of a systematic methodology for collecting and managing the massive data available from one or more plants. The second limitation is the lack of labeled historical data and industrial environments' dynamic and heterogeneous nature. Concerning the first point, the main issues concern the types of data that need to be available for building accurate and robust models for fault detection and diagnosis, the time at which the data are required, and how they can be organized for facilitating the preprocessing phase. Indeed, the data are often physically taken from the machinery and stored in a PC, in which ML models for fault diagnosis are built. However, these datasets are unlabeled, i.e., the health condition associated with the condition monitoring data and the system working condition, and are not included. Therefore, a time-consuming phase is necessary for assigning the correct label to all observations. In addition, the massive amount of condition monitoring data leads to collecting only a few minutes of data per day, limiting the use of complex classification models and the analysis of the system behavior. Concerning the second point, the main issues concern data availability and the ability to learn new behaviors as they occur. Indeed, during the training phase, classification models for fault diagnosis learn the relationships between the condition monitoring data and the corresponding health condition. Therefore, when applied to new data, they can only classify the observations into the classes involved in the learning process. However, not all fault conditions are known in advance or can be simulated because of safety issues. Hence, these models fail to recognize and classify observations corresponding to a health condition not included in the training set. In addition, industrial contexts are inherently dynamic, which makes classification models trained on historical data obsolete and less accurate as the operating condition of a system changes or a modification of the context of use is introduced. Given these issues, the research questions (RQs) that this paper wants to address can be summarized as follows:  
1. RQ1. Which data are needed for building an accurate and robust predictive model for diagnostics? How should the dataset be organized? 2. RQ2. How can labels be automatically obtained, considering that novel behaviors can occur during machine functioning? Underpinned by RQ1, the first purpose of this paper is to exploit the experience derived from several collaborations with industries in the Emilia Romagna region (Italy) and the knowledge acquired from the existing literature for proposing a classification of the available data and explaining how each kind of extracted data can contribute to building a health management system. RQ2 requires acting at the data analysis level, focusing on different approaches and learning paradigms to integrate historical information with streaming data to provide real-time feedback on the machinery's health condition for the maintenance actions scheduling. Hence, the second purpose of this paper is to validate a general method for the automatic labeling of streaming data that can reduce the data preprocessing and facilitate the data analysis that creates value and supports the decision-making process. Finally, a transversal goal of this paper is to provide knowledge to scholars and practitioners on the criticalities affecting the realization of a PdM strategy for the health management of industrial assets. The main contributions of the present work may be summarized as follows:  
1. The definition of the data and information flows in an edge-cloud-based PdM system;2.