# A Time-Frequency Image Quality Evaluation Method Based on Improved LIME
## 1 Introduction  
Rotating machinery is an important part of mechanical systems, and monitoring its health status is of great significance to the overall operation and maintenance of mechanical systems. Fault diagnosis is an important means to establish the relationship between monitoring data and machine conditions [1]. Since the 21 st century, the deep learning method based on artificial intelligence has been widely used in the fault diagnosis of rotating machinery for its intelligent and efficient characteristics [2; 3; 4; 5]. In these studies, most researchers focused on the design of the network structure, to some extent ignoring the important influence of the input on the diagnosis outcome. But input is crucial for nerve training [6; 7; 8]. Not only does it affect the time required for network training, but the description of the features in the data set also affects the accuracy of the network results [9; 10]. Vibration signal is the most widely used monitoring data in rotating machinery fault diagnosis. In engineering practice, mechanical equipment has many parts and complex structures, and its operating conditions often change with time, so its vibration signal will change nonlinearly. Compared with simple time domain data or frequency domain data, time-frequency image data obtained through time-frequency transformation can express both global and local features of signals in time domains and frequency domains and accurately reveal changes in vibration signals over time, so these data are widely used as the input of deep learning models [11]. Since the 1940s, the pace of development of time-frequency transfer technology has never diminished [12; 13], and in the last decade, it has seen more rapid development. Several new time-frequency transfer methods have been proposed to achieve higher time-frequency aggregation [14; 15; 16]. This goal is undoubtedly important in time-frequency transformation research, but when it is used as an input todeep learning for fault diagnosis, this goal sometimes does not lead to good results; images with higher time-frequency aggregation as the input do not necessarily lead to higher diagnostic accuracy. This is because the fault information is often contained in the sideband of the characteristic frequency. Whether it is the preprocessing method of time-frequency transformation represented by mode decomposition or the postprocessing method of time-frequency transformation represented by energy rearrangement, the time-frequency image will lose part of the fault information while improving the time-frequency aggregation. Therefore, in the field of intelligent diagnosis of rotating machinery, how to evaluate the quality of time-frequency images becomes an urgent problem to be solved. Under the same network structure, diagnostic accuracy is an important index to measure input quality. However, past examples show that high accuracy does not always mean machine learning is really good. Sometimes, machine learning's predictions are highly accurate but for the wrong reasons [17]. There is a conflict between predictive accuracy and descriptive accuracy of machine learning, and the decision-making process of machine learning is often unable to be described in a clear logical language. This limits the expert's understanding of the model's behavior. This leads to the fact that when deep learning models are applied to decision making in real engineering problems, people often fail to trust the models based on diagnostic accuracy alone. Only when the model is explained and the interpretation results are consistent with the prior knowledge can the engineers' trust in the model be improved [18; 19]. Aiming to solve this problem, this paper proposed an improved LIME method to evaluate the time-frequency image quality of vibration signals of rotating machinery from two aspects: the accuracy of the diagnosis results and the consistency of the interpretation results with prior knowledge.