# Health State Prediction Method Based on Multi-Featured Parameter Information Fusion
## 1 Introduction
The advantage of the expert knowledge-based prediction method over the model-based prediction method is that it can estimate the future development trend without establishing an accurate physical model by utilizing the known information and adopting predictive reasoning, but its main problem is that the expert system is susceptible to the incomplete influence of the expert's experience and knowledge base, leading to the miscalculation, which cannot reflect the failure process of the mechanical components in real time, affecting the real-time prediction, and making the inference results also have uncertainty. The results are also uncertain. Therefore, data-driven methods are widely used, which only need to analyze a large amount of historical data to provide decision support based on facts and evidence. Commonly used prediction algorithms mainly include recurrent neural networks (RNN) [19] and long short-term memory neural networks (LSTM) [20]. Recurrent neural networks (RNN) are known for their ability to handle temporal predictions [21]. [PERSON] et al. [22] used RNN prediction models to predict transformer failures. However, RNNs have significant challenges in recording long-term information and are prone to gradient vanishing and explosion problems. Long Short-Term Memory (LSTM) [23] is a variant of RNN, a mechanism that manages the accumulation of information by selectively forgetting past data and integrating new data, effectively solving the problem of long-term dependency. [PERSON] et al. [24] built an LSTM prediction model for the prediction of diesel engine exhaust temperatures using a prediction of whether the diesel engine is in an abnormal working state that is inferred in advance. Based on the research conclusions of the above scholars. This paper proposes a health state prediction method for rotating equipment based on vibration signals, and the technical flow chart (Figure 1). It is used to solve two main problems in predicting the health state of key components based on vibration signals: (1) The working environment of rotating equipment is harsh, and a single vibration signal feature is susceptible to the influence of the harsh working environment and cannot completely reflect the fault information. (2) The vibration signals are unlabeled time-series data, so how can we accurately convert them into the health state of key components? Firstly, the original vibration signal is feature extracted; secondly, the extracted features are fused by SOM neural network features to get the health state indicator (HI), and then, the health indicators are classified by the clustering method to get the health state of each stage. The optimized Bi-LSTM model is used to predict the health state, and the validity of the proposed method in this paper is verified using the dataset of bearings. The method realizes the fusion of multi-feature parameters and obtains optimal health state prediction results. The paper is organized as follows:Section 2 describes the basic theory used in this paper; Section 3 describes the improved BiLSTM model; Section 4 wraps up data description and preprocessing; Section 5 describes experimental analysis; and Section 6 is the conclusion. Figure 1: Sketch map of the proposed.