# Health State Prediction Method Based on Multi-Featured Parameter Information Fusion
## 1 Introduction  
The advantages of rotating equipment include high power-to-weight ratios, excellent energy conversion rates, compactness, smooth operation, low maintenance costs and a long service life. A variety of emerging and traditional industrial fields have been widely used, such as centrifugal pumps, compressors, fans, etc., which are widely used in petroleum, chemical industry, mining, natural gas, metallurgy, and other fields. These pieces of equipment need to operate for extended periods under high temperatures and high-pressure conditions. These poor working conditions will inevitably affect the service life of important parts. The failure of important parts not only has a significant negative impact on operational efficiency and cost control, but it can even seriously jeopardize the lives of staff. Therefore, to reduce the hazards of rotating equipment failures and improve the safe operation and full life-cycle operation of the system, it is becoming more and more urgent to accurately obtain and predict the health status of rotating equipment [1]. When rotating equipment malfunctions or abnormal conditions occur, the characteristics of its vibration signals can change significantly. For example, an unbalanced rotor cancause the system to produce periodic vibrations, the frequency and amplitude of which increase as the degree of unbalance increases. Similarly, misaligned shafts and couplings can cause periodic vibrations that manifest themselves in rotational speed frequencies and their harmonic components. Loose components can cause non-linear vibrations characterized by irregular variations in vibration amplitude and frequency. The wear of components such as bearings and gears can introduce shock and random vibration components into the vibration signal. Cracks, on the other hand, may lead to significant changes in the periodic and non-periodic components of the vibration signal of a structural member or rotating shaft. It is thus clear that vibration signals can characterize the state of health of a component. In general, vibration signals can be used for fault feature extraction from two different perspectives: the time domain (parameters such as mean, variance, kurtosis, waveform, etc.) and the frequency domain (gravitational frequency, mean-square frequency, frequency variance, etc.) [2]. [PERSON] et al. [3] extracted the time-domain features superimposed on vibration signals as inputs to be transported into the GRU model for fault diagnosis. Although the time-domain features of vibration signals can be used as the basis for condition monitoring and fault diagnosis of mechanical components, the time-domain features are poorly resistant to interference in a strong noise background and are prone to misjudgment. Therefore, sometimes it is necessary to extract the features of the signal in the frequency domain, the Fourier transform of the signal, from the time domain to the frequency domain, to obtain the frequency information of the signal. Then, using this information, determine the fault characteristics and extract the fault features on this basis. [PERSON] et al. [4] extracted the fault features from the frequency domain samples of the gears by using sparse filtering, and then used softmax regression as a classifier to categorize the different types of faults learned and used the dataset to prove its validity and reasonableness, as well as its effectiveness and rationality. Although the above methods perform fault diagnosis to some extent by characterizing sound signals, most of them only use the time or frequency domain for fault feature extraction. However, different feature extraction perspectives can reflect different health conditions, and there are both intersections and complementarities between them. Therefore, multi-feature fusion technology is used to fuse multiple features to obtain more comprehensive information.