# Human Factors Analysis of the Improved FRAM Method for Take-Off Quality Lateral Shift
## 3 Improved FRAM Method Analysis Steps Are Introduced  
The ETA, FTA, and FRAM methods analyze unsafe events through a network framework, while ETA starts the analysis from the initial events and finds the cause of unsafe events through induction and reasoning. The FTA method is based on the results of unsafe event analysis through the backward deduction of the causes of unsafe events. However, FRAM regards accident occurrence as resulting from the whole: as a type of systematic analysis method [19], it has both deductive reasoning logic and inductive reasoning. Therefore, the FRAM accident analysis model is more able to reflect the objective laws and accord with people's cognitive thinking. The FRAM model consists of several functional modules (as in Figure 2), which make up the whole system. Each module has six nodes, which are:  
1. I (Inputs): function module of the input node;
2. P (Preconditions): conditions required for function module operation;
3. R (Resources): resources required for function implementation;
4. T (Time): time required for function execution;
5. C (Control): constraints on the execution of a function;
6. O (Outputs): function module output node. The operational flow of the FRAM model is as follows. STEP 1: Identify and interpret functional modules; STEP 2: identify functional variability; STEP 3: focus on functional linkages and potential possibilities; STEP 4: manage and monitor functional variability. However, when using the traditional FRAM model for analysis, only the accident-related data are collected before the analysis, and in the process of analysis, the data are used for a single open-loop analysis; the results of the analysis cannot rule out contingency and specificity, and the analysis method depends on the knowledge and experience of experts. Therefore, on the basis of the traditional FRAM model, the method in this paper iterates the safety barrier based on the analysis results in a new round of event analysis and cyclically collects the data to carry out the comparison, tests the resonance relation between the functional modules, and improves the use of historical data. The improved FRAM process is shown in Figure 3.  
Figure 2: The FRAM functional module. **STEP 0:** Determine the objectives of the analysis and the boundaries of the problem, define the objectives and directions of the study, define the boundaries of the study, and prevent the boundaries from being unclear or too large; as a result, the number of functional modules and influencing factors related to the research problem increases geometrically, resulting in the problem of the coverage being too wide to obtain an effective solution. **STEP 1:** Identify and describe functional modules; based on the hexagonal graph model of the FRAM function module, the system sub-modules are described and analyzed according to the (I) input node, (P) operation conditions, (R) resources, (T) time, (C) constraint conditions, and (O) output node, and the function of each sub-module is defined. **STEP 2:** Identify the functional variability, clarify the relationship between sub-modules of the system, and determine the upstream module mutation and the impact of the downstream module. It should be noted that changes in environmental conditions have a certain relevance to the variation in each functional module. Therefore, after clarifying the upstream and downstream relationships of each functional module, the input and output data from the functional relationships or trends can be determined. **STEP 3:** Pay attention to the connection and potential relationship between functions, understand the resonance relationship between all function modules through data mining, and consider the contingency of low-probability events. A lack of sample data may lead to missed or mistaken judgments of resonance relations. Therefore, focusing on the connections and potential relationships between functions requires a large amount of sample data.