感測系統之即時錯誤鑑別及更正方法

Abstract

在一個採用多餘元件構成的感測系統中，錯誤元件的鑑別(faulty device identification)是透過個別元件輸出所組成的關係式“voting equations”來進行。然而當元件輸出帶有雜訊(noise)時，此鑑別方式無法達成“即時”的錯誤訊號鑑別，更無法達成“即時”的錯誤訊號更正。本論文針對此問題，提出一解決方式來達成“即時”的錯誤訊號鑑別，並可進一步法達成“即時”的錯誤更正。 所提出的解決方法如下：在傳統之“voting equations”外，新增數個個別元件輸出間之關係式，之後再將錯誤元件的鑑別問題轉化成一非線性系統的狀態估測(states estimation)問題。如此一來，便可以“即時”的獲得每一元件的誤差量，並加以補償，達成“即時”之錯誤更正。所新增之數個個別元件輸出之關係式乃是此“即時”的錯誤鑑別之所以可行之關鍵，亦是本論文與其它錯誤鑑別方法最大不同之處。我們將狀態估測的問題用儲存記憶褪去式卡曼濾波器(Kalman filter with fading memory)作為系統的觀察器，進而估測出狀態值以及配合狀態回授法達成更正某些出錯元件的訊號飄移現象(time-varying or drift)，使更正後的單元可繼續存在於感測系統中運作，以提高系統的輸出的精度。 藉由三個感測器的模擬結果印證本法可成功估測出錯誤訊號，其估測標準差約 ，對於訊號漂移亦可估測至標準差為 ，此外，可估測到最小的錯誤量約為雜訊1/2大小。相關模擬探討於本文中詳見。

In a sensing system that was constructed by employing redundant devices (components), the conventional approach for the fault-identification was done through the “voting equations”. However, when the outputs of the devices were contaminated by noise, the conventional fault-finding measure had to set up a threshold values and an observation periods along with voting equations. Due to the setup of an observation period, the conventional approach can not be done in a real-time manner. As a consequence, the real-time fault-correction was not possible. In this thesis, we proposed a novel real-time fault-identification method to solve the problem above. Furthermore, the proposed method can combine with various feedback techniques to achieve real-time fault-correction. The proposed method uses the novel “output equations” along with “voting equations” to describe the relationship between each device output. After that, the real-time fault-identification problem was formulated into a nonlinear state estimation problem. The method of the newly added “output equations” was the key to the success of the proposed real-time fault-identification method. Furthermore, in order to handle the sensor drift (or time-varying fault) problem, we use the “Kalman filter with fading memory” techniques for the state observer. Moreover, we use state feedback techniques for the purpose of the correction of fault, and the corrected device can be kept in the “fault-tolerant of sensor system” to increase the accuracy of system output. We can estimate fault signals successfully by simulation of 3 sensors of the system, and its error standard deviation is about .For drift, its standard deviation of estimation is about .The minimum fault value that estimated is equals to 1/2 times of standard deviation of noise approximately. Besides, the state feedback technique give a fault correction to sensor output which has fault and, thus, a corrected signal is approximate to ideal signal. More facts shows in the thesis.