## 4 Evaluation  
The evaluation of bearing fault detection technology requires consistent benchmarks. The ASTM standards ensure that every aspect of the manufacturing process, from material selection to final bearing testing, complies with internationally recognized standards. Some of the ASTM bearing standards followed in this document are listed in Table 1.  
The evaluation of bearing fault detection is usually based on multiple performance indicators and the actual effectiveness of detection methods [94].  
Some key evaluation indicators and commonly used evaluation methods for bearing fault detection methods include the following:  
* Accuracy;
* Robustness;
* Timeliness;
* Interpretability;
* Economy;
* Early stage of diagnosis.  
Accuracy is the most basic requirement in fault detection, reflecting the detection system's ability to identify different types of faults.  
Robustness refers to the stability of a detection system under different working conditions such as noise and load changes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number** & **Name** \\ \hline ANSI/ABMA B3.1-1992 (S2020) & Rolling Element Bearings—Aircraft Engine, Engine Gearbox, and Accessory Applications—Eddy Current Inspection \\ \hline ANSI/ABMA B3.2-1992 (S2020) & Rolling Element Bearings—Aircraft Engine, Engine Gearbox, and Accessory Applications—Surface Temper Etch \\ \hline ANSI/ABMA 9-2015 (R2020) & Load Ratings and Fatigue Life for Ball Bearings \\ \hline ONORM M 8128: 2017 & Tribotechnique—Test machines for rolling bearing grease R0F and R0F+—Evaluation of grease service life \\ \hline ANSI/ABMA/ISO 15243-2017 & Rolling Bearings—Damage And Failures—Terms, Characteristics, and Causes \\ \hline ANSI/ABMA/ISO 13411-1997 (S2010) & Aerospace–Airframe Needle Roller Cylindrical Roller and Track Roller Bearings—Technical Specification \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some of the ASTM bearing standards.  
Timeliness refers to whether the fault detection system can provide detection results within a short period of time when a fault occurs. For high-speed rotating bearing equipment, the processing speed directly affects the real-time performance of fault detection. When deep learning and machine learning methods are applied to fault detection, the inference speed and computational resource requirements of the model become key limiting factors for real-time performance.  
Interpretability is the basis and explanation for detecting whether a system can provide fault diagnosis, helping maintenance personnel understand the mechanism of fault occurrence. For complex deep learning models, model interpretability is a challenge, but it is crucial for determining fault types and analyzing fault causes.  
The economy of fault detection systems is also an important aspect of evaluation, including equipment costs, detection sensor costs, operating costs, data costs, etc.  
Early stage of diagnosis refers to whether the detection method can identify anomalies in the early stages of the fault, thereby achieving predictive maintenance. These evaluation dimensions provide specific guidance for the design and selection of fault detection systems.  
According to the literature, this article presents a quantitative comparison of certain performance data against the latest detection methods, as shown Table 2.