## 2 Related Works  
Significant efforts have been carried out by researchers to improve risk estimation:1. **Focus on the advancement of risk matrices and the optimization of risk estimation tools.** Reference [16] took additional severity factors into consideration, including employee factors, workspace factors, worksite location, etc., and proposed the Accident Severity Grade (ASG) method based on employee and workspace risk factors to quantify the injury risk. Reference [17] proposed a three-dimensional risk assessment matrix--injury frequency, severity, and the new dimension--preventability, to improve risk estimation. Reference [18] developed a proposed risk estimation tool with five risk parameters, severity of harm, frequency of exposure to the hazard, duration of exposure to the hazard, probability of occurrence of a hazardous event, possibility of avoidance. However, given that risk estimation is an intricate and extensive cognitive process, it is unavoidable that the subjective factors of risk assessors have a bearing on the results of risk estimation.
2. **With the development of AI, machine learning techniques are also finding their way into the field of safety [19]**. [PERSON], [PERSON] et al. used a Deep Neural Network (DNN) model to predict risk increase or decrease as the target system conditions changed [20]. Natural language-based probabilistic risk assessment models applying deep learning algorithms were developed to emulate experts' quantified risk estimates, which allowed the risk analyst to obtain an a priori risk assessment [21].
3. **Authors have made recommendations and proposed other tools or other methods.** [PERSON], [PERSON] et al. integrated a Logical Analysis of Data (LAD)-based dynamic experience feedback into quantitative risk estimation to identify and update the risks, guiding safety practitioners to evaluate whether the current state of machine leads to an accident or not [22]. Bayesian networks (BN) and a Fuzzy Bayesian Network (FBN) were applied for predictive analysis and probability updating to improve risk assessment [23; 24]. Those methods were based on strong models and little data, leading to large sets of assumptions and simulations for their environment.  
The research mentioned above has significantly contributed to enhancing risk estimation practices. However, it is unavoidable that the subjective factors of risk assessors have a bearing on the results of risk estimation. It is also challenging to build a uniform benchmark for risk estimation, causing disagreements over the risk estimation results. Reference [25] observed that in several other companies, a standard corporate risk matrix for risk estimation shall be applied to both continuous operations and construction and maintenance projects. In addition, throughout the utilization of machinery, it is challenging for manufacturers, users, integrators, and other stakeholders to establish a consensus on safety awareness because of the disagreements, which hampers the collaborative implementation of more effective risk reduction measures by all parties.  
In this study, a deep learning algorithm, Long Short-Term Memory(LSTM) [26], was introduced to learn and use those historical risk estimation information. This article is organized as follows: In Section 3 (Materials and Methods), the comprehensive framework of the methodology, how to create hazard event dataset, and the principle of LSTM-based risk estimation algorithm are presented. The following section, Section 4 (Results) validates the new risk estimation methodology on the raw, 2\(\times\), and 6\(\times\) hazard event dataset of industrial robot. Section 5 (Discussion) briefly discusses the performance of the LSTM-based deep learning risk estimation method, and Section 6 (Conclusions) presents the conclusion.