To realize an early warning of unbalanced workload in the aircraft cockpit, it is required to monitor the pilot’s real-time workload condition. For the purpose of building the mapping relationship from physiological and flight data to workload, a multi-source data fusion model is proposed based on a fuzzy neural network, mainly structured using a principal components extraction layer, fuzzification layer, fuzzy rules matching layer, and normalization layer. Aiming at the high coupling characteristic variables contributing to workload, principal component analysis reconstructs the feature data by reducing its dimension. Considering the uncertainty for a single variable to reflect overall workload, a fuzzy membership function and fuzzy control rules are defined to abstract the inference process. An error feedforward algorithm based on gradient descent is utilized for parameter learning. Convergence speed and accuracy can be adjusted by controlling the gradient descent rate and error tolerance threshold. Combined with takeoff and initial climbing tasks of a Boeing 737–800 aircraft, crucial performance indicators—including pitch angle, heading, and airspeed—as well as physiological indicators—including electrocardiogram (ECG), respiration, and eye movements—were featured. The mapping relationship between multi-source data and the comprehensive workload level synthesized using the NASA task load index was established. Experimental results revealed that the predicted workload corresponding to different flight phases and difficulty levels showed clear distinctions, thereby proving the validity of data fusion.

Air data for civil aircraft provides important flight information and is the basis for guiding pilots to maneuver the aircraft. It is necessary to clarify the impact of air data failure on the aircraft and pilot operation. By formulating the principles of Cascading Effect Analysis(CEA) of air data failure, including failure scenario determination, direct impact judgment, indirect impact analysis, and comprehensive impact analysis of the whole aircraft, this paper studies the problem of grading the impact of air data failure based on CEA Method. The architecture of the air data system and the data voting logic, and the air data failure mechanism are analyzed so as to define the air data failure scenarios. Using CEA Method, this paper analyzes all the impacts of air data failure on aircraft systems iteratively, determines the impact on the whole aircraft and flight operations, and judges whether the impact results meet the analysis expectations.