Flight State Information Inference with Application to Helicopter Cockpit Video Data Analysis Using Data Mining Techniques
The National Transportation Safety Board (NTSB) has recently emphasized the importance of analyzing flight data as one of the most effective methods to improve eciency and safety of helicopter operations. By analyzing flight data with Flight Data Monitoring (FDM) programs, the safety and performance of helicopter operations can be evaluated and improved. In spite of the NTSB's effort, the safety of helicopter operations has not improved at the same rate as the safety of worldwide airlines, and the accident rate of helicopters continues to be much higher than that of fixed-wing aircraft. One of the main reasons is that the participation rates of the rotorcraft industry in the FDM programs are low due to the high costs of the Flight Data Recorder (FDR), the need of a special readout device to decode the FDR, anxiety of punitive action, etc. Since a video camera is easily installed, accessible, and inexpensively maintained, cockpit video data could complement the FDR in the presence of the FDR or possibly replace the role of the FDR in the absence of the FDR. Cockpit video data is composed of image and audio data: image data contains outside views through cockpit windows and activities on the flight instrument panels, whereas audio data contains sounds of the alarms within the cockpit. The goal of this research is to develop, test, and demonstrate a cockpit video data analysis algorithm based on data mining and signal processing techniques that can help better understand situations in the cockpit and the state of a helicopter by efficiently and accurately inferring the useful flight information from cockpit video data. Image processing algorithms based on data mining techniques are proposed to estimate a helicopter's attitude such as the bank and pitch angles, identify indicators from a flight instrument panel, and read the gauges and the numbers in the analogue gauge indicators and digital displays from cockpit image data. In addition, an audio processing algorithm based on signal processing and abrupt change detection techniques is proposed to identify types of warning alarms and to detect the occurrence times of individual alarms from cockpit audio data. Those proposed algorithms are then successfully applied to simulated and real helicopter cockpit video data to demonstrate and validate their performance.
Hwang, Purdue University.
Engineering|Aerospace engineering|Information science
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