The Study Of Hilbert-Huang Transform In Data Acquisition And Processing System For PHM

Prognostics and health management system is a key technology that can improve system performance and availability, reduce the maintenance costs and labor. A typical PHM system mainly consists of six parts, including sensor data acquisition, signal processing, status monitoring, health assessment, fault prediction and protection decision-making. Among them, sensor data acquisition system has the function of equipment status data collection, processing and transmission. Its output is the basis of judgment and processing for the following systems. It is the basis of the whole PHM system and plays a very important role in the correctness of the fault diagnosis result.The PHM data acquisition and processing system in this dissertation is an important part of the distributed sensing system on a certain aircraft and it can adapt to all signals from different kinds of sensors. It is mainly used to acquire, pre-process, compress and transmit the sensor data.The signal processed by the system is non-stationary, so the processing method must be in both time domain and frequency domain. Hilbert-Huang transform is a new time-frequency domain analysis method. It has the character of adaptation and is considered as a major breakthrough to the traditional signal processing methods based on the Fourier transform. This dissertation deeply studies the application of Hilbert-Huang transform in the PHM data acquisition and processing system, including the basic principle, the comparison with other time-frequency domain analysis method and the final implementation and optimization in DSP. After being optimized, its processing speed can reach to 5120 samples per second.There will be vast redundant data generated by the AD per second, but the system’s transmission bandwidth and storage space is limited. In order to solve this problem, LZ77 is chosen to compress the acquired data after analyzing several lossless data compressing algorithms that are used frequently. In the process of compression, the efficiency and ratio is contradictory. So in the implementation of the algorithm, an alterable sliding window is adopted to achieve the best overall compression result. The experimental result shows that it can remove the redundant data effectively and satisfy the real-time requirement of the system. When DSP works at the highest frequency, the data processing rate can reach 3MB/s and the compression ratio is 32.4%.