**1. Introduction**

### **1.1 Research background and significance**

In economic construction, as the main production equipment, it is inevitable that large-scale machinery will fail because of factors such as work difficulty or service time. However, the economic losses and casualties caused by the failure will make people lament [1]. In the 1990s, a power plant turbine unit caused great damage to the Korean high-speed rail derailment, causing many injuries and even deaths. Rolling bearing, as an important rotating part, has a long-term high-speed rotation, coupled with the precise and complex structure, and the failure may be quite high. Reliable data show that the motor faults caused by bearing faults are very frequent [2].

In the large or small machinery in various fields, the position of rolling bearings in it is indispensable and can be said to play an important role in production and life. Rolling bearing fault is the most common fault of rotating machinery, and once it happens, it will seriously affect the normal work of the whole machinery, so it is very important to study the fault diagnosis technology of rolling bearing [3–6]. For such an important rolling bearing, its fault diagnosis must be valued. Specific to the rolling body, inner circle, or external circle failures, we need to make subsequent improvements. If only one standard is conducted to detect and repair, not only the accuracy is low but also the cost of manpower and material resources [7]. If the fault diagnosis can be accurately conducted on prevention and moderate maintenance to avoid adverse effects. It will inevitably play a very important role in promoting the economic and social aspects [8].

#### **1.2 Current status of fault diagnosis technology**

Research on fault diagnosis of rolling bearings started around 1960. Overall, there can be divided into five stages [9].

The first stage is spectral analysis in the 1950s. Spectrum analysis methods have attracted attention. However, due to the immature technology at that time, the spectrum analysis was not widely used in the field of bearing fault diagnosis technology because the results were widely affected by the interference noise, expensive price, and complex operation.

In the second stage of the 1960s, the impact impulse meter detection method appeared, and the effect was obviously better than the spectral analysis, which could directly save the complicated steps, and was still widely used in the fault diagnosis of roller capital bearings.

The third stage in the 1960s to 1980s, computers and signals in the trend of The Times, the more prominent is resonance demodulation technology, because of the advent of this technology, makes the rolling bearing fault diagnosis technology to a higher level, gradually from the beginning to maturity.

After the 1980s, the emergence of artificial intelligence provided a new soil for the rolling bearing fault diagnosis and the emergence of an intelligent diagnosis system greatly improved the accuracy of the fault diagnosis. Due to intelligence, the influence of human factors is greatly reduced, which has been applied in engineering practice.

The fifth stage is after the 21st century, that is, we are now, the rolling bearing fault diagnosis technology has taken an epoch-making step, more and more high-tech development, through the fault diagnosis of virtual instruments, has become a new pointing mark, has an important practical value.

At present, around the world, we are constantly studying the rolling bearing fault diagnosis, using a large number of different research fields. According to the most popular classification methods, it is divided into three categories, namely, modelbased fault diagnosis technology, knowledge-based fault diagnosis technology, and data-based fault diagnosis technology.

Because of its national conditions, China began to study fault diagnosis much later than that in other countries. In the late 1970s, it was not first available in the early 1980s and formal research began. However, it is gratifying that under the hard work of Chinese researchers, in the 1990s, the field of fault research has been on the right track, and it has made great breakthroughs in both theory and practice and can be applied in production and life. But compared with other countries, China still has a long way to go.

*Perspective Chapter: On Rolling Bearing Fault Feature Extraction Based on Entropy Feature DOI: http://dx.doi.org/10.5772/intechopen.105095*

#### **1.3 Main work of the paper**

In this paper, we study joint analysis of rolling bearings based on approximate entropy, sample, and information entropy.

It roughly describes the historical background and practical significance of fault diagnosis research in the 21st century today and briefly expounds on the current situation of the global research on fault diagnosis.

The universal structure of the rolling bearing is introduced and the relevant parameters are marked in the plane structure diagram. The most common form of rolling bearing failure is described, and the characteristics and hazards of the bearing are also mentioned.

The cause and mechanism of the bearing vibration are expounded in detail. A theoretical method for calculating the characteristic frequency of the rolling bearing is presented. Bearing fault diagnosis experimental equipment (Western Reserve University) is introduced, and its relevant basic parameters and bearing fault setting form are introduced.

Concept definitions and calculations for approximate entropy sample and information entropy are given. A single entropy feature of the rolling bearing vibration signal in different failure modes is extracted, combined with data to verify the feasibility of approximate entropy and sample entropy in terms of failure features. The three entropy features are jointly analyzed to distinguish the different fault modes of bearings.

In the end, the full research work is summarized, the deficiencies are proposed, and the future research is preliminary.
