以 模 式 方 法 為 基 礎 之 程 序 錯 誤 診 斷

本論文主要注重在發展出新的模式方法對於加成性 (additive) 以及乘積性(multiplicative) 的錯誤進行全盤式的診斷。這些建立的錯誤診斷模型具備有相當多樣化的診斷能力，從簡單的錯誤偵測 (detection) 乃至於到細部的錯誤孤立 (isolation) 以及錯誤大小的識別 (identification)。在一開始，論文中會回顧一些常用於錯誤診斷的多變量統計方法，並且利用一個簡單的範例來說明此類方法在錯誤孤立先天上的限制。然後對於這些常用的統計方法提出一些修正，以便將此類傳統的技術能夠延伸至監控與時間相關 (time-dependent) 的程序，或者能夠在開環的情況之下，正確地孤立出一些事先指定 (specified) 的錯誤型態。然後，文中將會介紹一些基本的靜態與動態程序模型識別的方法。基於部分最小平方法 (PLS)，本文將提出一個藉由合併部分最小平方法的子模型 (sub-models) 而得到程序的整體模型 (global model) 的效率模型識別方法，並且利用一個簡單數值的範例來說明此方法的實用性。藉由程序的子模型以及合併後的整體模型，文中亦提出一個另類型態的分散式 (decentralized) 錯誤診斷方案，用以孤立可能的錯誤原因。另外，在模式識別之中，對於動態模式參數的變異以及共變結構的估計，亦會作出解析式的推導。另外，一項新型與全域型的感測器 (sensor) 錯誤診斷方法亦在此論文被提出，此方法可用來診斷任意多維的感測器故障。基於這個提出的感測器錯誤診斷方法，有錯誤的感測器可以被輕易地偵測，孤立，而且錯誤的大小亦可被識別。基於之前所推導的動態程序參數之變異數，論文中會定義一系列的模式參數的相似度 (similarity)。乘積性的程序錯誤可以利用這些新定義的相似度來偵測與孤立。對於一些特定種類的乘積性錯誤，例如程序增益 (gain) 錯誤以及程序時延 (deadtime) 錯誤，其錯誤的大小可以利用這些相似度來識別。文中將會利用一些說明用的範例研究來展示上述理論概念的可行性。

關鍵字

模型識別；錯誤診斷；感知器錯誤；乘積性錯誤；參數相似度

並列摘要

The focus of this dissertation is on developing novel model-based approaches for additive and multiplicative fault diagnosis (FD). The identified process diagnostic models can be extended to have varying fault diagnostic capabilities, from simple fault detection to detailed fault isolation and identification. Some frequently used multivariate statistical methodologies for FD are reviewed, and their major limitations in fault isolation are demonstrated. Novel modifications of conventional statistical techniques are proposed, and extended to monitor time dependent processes and to isolate some specified faulty types under open-loop conditions. Some basic static and dynamic process model identification approaches are reviewed. An efficient model identification method by merging PLSR sub-models is presented; and a numerical application is used to illustrate the practicality of this method. An alternative decentralized FD scheme is also proposed based on the sub-models and merged global model. Moreover, the parameter variance and covariance structures are investigated analytically for dynamic process representation. A novel and unified sensor FD approach is constructed to arbitrary multiple sensor failure scenarios. Based on the proposed methodology, the faulty sensors can be easily detected, isolated and identified. A variety of parameter similarities for dynamic processes are defined based on the derived parameter variances. With the use of these similarities, the multiplicative faults of processes can be detected and isolated. For some multiplicative faults, e.g. changes in gain and deadtime, the faulty parameter can be specified, and the fault magnitude can be identified. Illustrative case studies are included to demonstrate these theoretical ideas in this thesis.

並列關鍵字

Model identification ； Fault diagnosis ； Sensor fault ； Multiplicative fault ； Parameter similarity

參考文獻

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國際替代計量

以模式方法為基礎之程序錯誤診斷

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