# Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios
## 3 Related Works  
### Data Collection  
According to [20], the types of data used for predictive maintenance are maintenance history (73%), asset usage (72%), asset condition (71%), condition data, and maintenance history of other assets within the company (42%), environmental data (29%), condition data, and maintenance history of other assets from other companies (9%), and others (7%). [PERSON] et al. [21] grouped the data required for predictive maintenance into four categories: (1) asset history data, which include historical records of failures, inspections, and the related costs; (2) usage and process data, which entail operational data, e.g., running hours, mileage, or tons produced; (3) stressor data, which include the operating conditions of the system, such as the load exerted on the system, and environmental data; and (4) condition monitoring data, which include signals able to reveal the system's health conditions and provide indication of failures. Although all these data may contribute to implementing a predictive maintenance strategy, intelligent fault diagnosis only relies on condition monitoring data in practice. In particular, vibration signals have been used most frequently for machinery health management, followed by acoustic emissions, force/torque, temperature, and electric signals [22]. In a few cases, the data are directly provided by machine log systems, without installing sensors for the collection of condition monitoring data [23]. However, the number of studies exploring other types of data for predictive maintenance and the industrial applications that exploit the Industry 4.0 technologies for intelligent maintenance is limited. Condition monitoring data, such as force, vibration, temperature, voltage, and others, reveal the health state of equipment working under certain operating condition, and are fundamental to building a fault diagnosis model, estimating its parameters, and verifying/validating its maturity [24]. However, raw data are redundant and noisy and cannot be directly used for fault diagnosis. Therefore, when using a data-driven approach for diagnostics and prognostics, processing and transforming raw signals is a fundamental step [25]. In particular, it is crucial to extract some characteristics from raw signals, named features, which must be relevant and nonredundant. The relevance of a feature concerns its ability to distinguish the different healthy and faulty conditions of the system under analysis. The set of features must contain the minimum informative content, i.e., it has to be nonredundant, to reduce the computational complexity of machine learning (ML) algorithms used for fault diagnosis. Feature extraction and dimensionality reduction notably reduce the amount of data, which may have an important implication from the data sharing and storage perspective [26; 27]. Through the IIoT architecture, it is possible to collect the data from all machines on a shop floor and query streaming data used for machine learning models [28]. They can be trained by resorting to the cloud computing paradigm, which represents a hosting platform that provides diagnosis solutions as a service [29]. However, the transmission of massive data to the cloud can generate high response times, which is unsuitable for fault detection systems for which real-time responses are essential. Performing data processing at the edge may solve this issue [30] because only features extracted at the edge may be sent to the cloud [31]. In addition, the reduced amount of data from the transmission into the cloud of relevant features may allow a more accessible aggregation of data collected from similar machines installed in different plants, obtaining larger datasets for model training. Therefore, a proper infrastructure should include sensor networks, edge devices, and cloud data centers to distribute the computing among different \"devices\" and reduce the latency of both transferring and inference and the required storage memory.