## 2 The Personalized Diagnosis Methodology  
### The Basic Idea for the Personalized Diagnosis Methodology  
Personalized medicine is a kind of accurate diagnosis technology based on individual differences with the development of molecular biology in recent years [33], which provides a new idea for condition monitoring and fault diagnosis in mechanical systems. Recently, the intelligent diagnosis methods have become one of the hot and promising research areas [34]. The valuable application of intelligent methods has reflected and provided the realization of personalized fault diagnosis in mechanical systems. The early development of mechanical diagnosis and condition monitoring was based on the faulty samples extracted from actual faults in mechanical systems. However, for the complex mechanical system and complicated fault mechanism, there are too many complexity vibration signals to be analyzed. Furthermore, in many faulty cases, it is very difficult to obtain all of the faulty samples when the machine is running. Therefore, the lack of faulty samples will lead to the failure to detect faults in real-world mechanical systems. Recently, it has been possible to conduct a simulation model of a mechanical system with faults, such as shafts in a rotor-bearing system, bearings and gears in a gearbox, etc., to obtain the faulty samples with various faults of different types using numerical simulation. Based on the above technique development, the personalized diagnosis methodology is proposed, and the fundamental procedure is shown in Figure 1. In order to realize personalized diagnosis, the present methodology combines the numerical simulation model and intelligent diagnosis methods to detect faults in mechanical systems. Numerical simulation plays a key role in the present methodology to obtain the simulated faulty samples to replace the measured ones. Therefore, the damage location prediction procedure is as follows:  
(1) Construct the FE model of mechanical systems with faults:  
In the simulation, the faulty model (e.g., unbalance, misalignment, rub-impact, damages, etc.) and the dynamic model of the mechanical system are constructed using finite element method (FEM). To decrease the difference between the measured vibration signals and the computed (by the FE model) ones of the same mechanical systems, the FE model updating technique using the experimental investigation of the intact mechanical systems [35] should be applied to reduce the difference. After the updating process, the resulting FE model of the mechanical systems with faults can be used to calculate the simulation vibration signals. (2)  
Obtain the faulty samples:  
The FE model of the mechanical systems with faults is calculated for the vibration signal in the time domain. The WPT is then applied to decompose the original vibration signals into multi-layers, and the corresponding sub-signals (components) are finally obtained. To generate the faulty training samples, the time domain indexes, such as the standard deviation (SD), peak, kurtosis, clearance factor (CF), impulse factor (IF), etc. (summarized in Table 1), are calculated from the sub-signals. Finally, the faulty training samples formed by the time domain indexes severe as inputs to train the intelligent diagnosis models using intelligent diagnosis methods. (3)  
The personalized fault diagnosis methodology:  
The measured vibration signals of the real-world mechanical systems and the corresponding time domain indexes are calculated using the same procedures described in Step (2). Then, the time domain indexes of the real-world mechanical systems are input into the trained intelligent diagnosis models. The outputs indicate the healthy condition or the types of faults. It is worth pointing out that if the precision of the FE model of the mechanical systems can be guaranteed, the present fault diagnosis methodology could be applied to any type of real-world mechanical system for any working conditions. Therefore, the present methodology could be called the personalized fault diagnosis methodology.