Research On Synchronous Acquisition And Data Compression Methods For Mechanical Vibration Wireless Sensor Networks

Mechanical vibration wireless sensor networks can effectively remedy the defects of traditional cable connection condition monitoring system in some applications, expand the scope of application of mechanical vibration condition monitoring system. For instance, it can implement the mechanical vibration condition monitoring of the key components in sealed environment or mechanical rotating environment. But due to the high sampling frequencies that are often required in mechanical vibration monitoring, the higher requirements of synchronous acquisition accuracy is required in mechanical vibration wireless sensor networks. Meanwhile, applying the higher sampling frequency would demand large amounts of vibration signal acquisition data, but the channel bandwidth and storage capacity of wireless sensor network nodes are limited, which lead to achieve a high efficient mechanical vibration data compression method in acquisition nodes.Mechanical vibration wireless sensor network monitoring system as a distributed system, each independent acquisition node can only control the acquisition timing by maintaining a local clock because of its physical dispersion, it is difficult to ensure the synchronous acquisition. Moreover, the mechanical vibration monitoring applications require higher synchronous acquisition accuracy than other low sampling frequency applications. If the mechanical vibration monitoring applications only utilize the method of clock synchronization, its requirements would not be met. This issue should be solved beginning with research on the underlying mechanical vibration signal sampling. The mechanical vibration signal acquisition will produce large number of original data in a short time, because of its high sampling frequency. It is difficult to compress the mechanical vibration signals on the account of the variations between the adjacent data are relatively large. Some time domain statistical or predicted data compression methods are not suitable for mechanical vibration signal compression. In addition, due to the deficiency of computing resource of wireless sensor network node, mechanical vibration data compression method need to take the efficiency of data compression, the signal integrity, and the computational complexity into consideration.Aiming at these problems, such as spatial jitter and temporal jitter in synchronous acquisition, and mechanical vibration signal data compression in mechanical vibration wireless sensor networks, this thesis studies the mechanical vibration wireless sensor network synchronization acquisition and data compression methods. The concrete research contents are as follows:1) Aiming at the current issue of synchronous sampling trigger accuracy between the mechanical vibration wireless sensor network acquisition nodes, the synchronization acquisition spatial jitter control method based on hardware cross-layer design is proposed. The start of frame delimiter(SFD) in beacon is used as the key synchronization information. The hardware cross-layer transmission of SFD signal is designed, whose purpose is to avoid the random delays of synchronization command transmission between network protocol stack layers and acquisition node modules. By applying the mechanism of first collecting and then parsing command, the synchronous sampling trigger accuracy in single hop network is improved. Furthermore, the beacon collision avoidance method based on dynamic frame structure of routing node is proposed, and the synchronous sampling trigger accuracy in multi-hop network is improved by adopting the delay error compensation method.2) Aiming at the current issue of unstable sampling intervals of acquisition nodes in continuous acquisition process, the temporal jitter suppression method based on crystal frequency offset dynamic compensation is proposed. The wireless sensor network acquisition node with dual processor architecture is adopted, it uses the dual processor division of labor and cooperation mechanism to enhance the system real-time ability on acquisition task, avoid the sampling interval jitters caused by preemptive multitasking. Moreover, the SFD signal in beacon broadcasted by sink node is used to estimate the crystal frequency offset of acquisition node. The crystal frequency offset dynamic compensation is utilized in continuous acquisition process to reduce the temporal jitters.3) Aiming at the issue of mechanical vibration signal data compression, the subband energy adaptive quantization mechanical vibration data compression method is proposed. Considering the limited memory space of acquisition node, the discrete cosine transform(DCT) is utilized to transform original data in blocks, and the energy aggregation characteristic of DCT is used to preprocess the original data. The subband energy adaptive quantization is researched to allocate quantization bits according to the energy of DCT coefficient of each subband, to reduce the distortion effect of the reconstructed signal. The data compression method combined with linear prediction, zero run length coding, and Range encoding is studied to effectively reduce the amount of data in acquisition node. Based on the subband energy adaptive quantization data lossy compression method, to achieve mechanical vibration data block-based lossless compression method by calculating and encoding the prediction difference. For the requirements of data transmission time in some applications, data single block divide-and-compress lossless compression method is proposed, which can reduce the total time for data compression and transmission by uploading the compressed data while in the process of data compression.4) A mechanical vibration wireless sensor network monitoring system with independent intellectual property rights is developed in this research. This system includes some function modules, such as the wireless sensor network management and the data synchronous acquisition. The performance of monitoring system is tested and the effectiveness of the proposed synchronous acquisition and data compression methods are validated through experiments and applications.