## 1 Introduction  
In the field of Prognostics and Health Management (PHM), the key is to be capable of predicting the Remaining Useful Life (RUL) of a system. Even knowing that it is possible to establish prognostics without identifying the root cause, the understanding of the way the system is degrading gives an advantage in terms of health monitoring system development. Knowing the physical phenomena allows us to introduce a degrading model to support the data acquired during system utilization and better tuning the algorithms in charge of fault detection [1].  
Without the degrading model, it is also possible to use statistics to learn the degradation behavior of a system of interest. Still, it would be necessary to have data available in many different situations. The data-driven approach, in this case, could manage the lack of the physics-model, but an anomaly may not be satisfactory classified.  
To have both approaches helps in troubleshooting and also in predictive maintenance of assets. The recent development of the PHM field brought a series of new information to support maintainers in the task of identifying/isolating the root causes of a specific failure The hybrid approach, using both model-based (physics model) and data-driven (collected data during operation) approaches, enhances a health monitoring system to accomplish the goal of detecting faults and generating alerts [1, 2].  
Since predictive maintenance makes use of fault detection to help RUL prediction, the goal is to establish in advance the discovery of a faulty condition on the target system. The earlier the detection, the more time to plan resource allocation and schedule maintenance tasks [3]. The process to detect the fault and identify its root cause is called Fault-Detection and Isolation (FDI) [4]. An anomalous behavior is not necessarily a fault, and the present work uses an FDI method to establish a classification algorithm. The approach is capable of distinguishing faults from abnormal behaviors that are not faulty conditions of the system but can produce a system degradation and also impact the system's RUL.  
The FDI method makes use of sensory data that can bring noise, bias and sensor calibration problems [3]. Because each system has its characteristics, it is hard to develop a solution that can be widely adopted. The typical approach is to have algorithms adapted for each new case, even if the system is similar to another one that already has an implemented solution. If the system is not installed in the same way, with the same conditions, it is possible to have different noise and environment conditions that give different readings to the sensor.  
The present work is an attempt to help the reuse of an FDI infrastructure and is the initial result of a hybrid approach called the Generic Anomaly Detection Hybridization Algorithm (GADHA). The next section will provide a literature review of solutions in the FDI field using hybrid methods. Then, the Material and Methods section describes the GADHA approach, and the results section shows the results achieved in a study case of fault detection and isolation on a spring-mass system. The discussion section brings the main achievements, and a brief conclusion section provides the main contributions of the work.