# Application of the Residue Method in Steam Superheater Fault Detection
## 2 Methods of Fault Detection
The performance of a detection and diagnosis system is given by:  
* Timeliness of detection--detection of faults soon after they occur;
* Defect sensitivity--the ability of the system to detect relatively small deviations;
* Robustness--the ability of the system to operate in the presence of noise, disturbances, and modeling errors, causing as few false alarms as possible;
* Accuracy in avoiding incorrect identification of faulty components. The general concept of fault detection and diagnosis can be divided into fault detection by generating fault symptoms, analytically and heuristically, and fault diagnosis. The detection and diagnosis procedure always centers around the following steps:  
* Extraction of information necessary to put into some form the characteristics associated with normal and abnormal operation, starting from appropriate means of measurement and practical observations provided by the personnel operating and supervising the plant or process;
* The development of characteristics and signatures associated with symptoms indicative of malfunctions to detect a malfunction;
* Detection of a malfunction by comparison with signatures associated with normal operating states and definition of detection confidence indicators;
* The use of a method of fault diagnosis based on the use of cause-effect relationships;
* Decision-making based on the consequences of detected faults. This stage may lead to shutting down the installation if the consequences are severe or to reconfigure the operation of the installation. Analytical symptom generation means the quantification of analytical knowledge about the process. To this end, processing of the data resulting from measurements on the process must be carried out to first generate the characteristic values by:  
* Checking the limit values of directly measurable signals. Characteristic values are the signal tolerance overruns;
* Analysis of the directly measurable signals uses signal models such as correlation functions, frequency spectrum, or autoregressive sliding averages (ARMA). Characteristic values are, for example, variances, amplitudes, frequencies, or model parameters;
* Process analysis uses mathematical models of the process and parameter estimation methods, state estimation, or parity equations. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Title 1** & **Additive** & **Multiplicative** \\ \hline \multirow{4}{*}{Faults} & \(-\) sensor faults & \multirow{2}{*}{ faults in the installation} \\  & \(-\) faults in the components & \\  & \(-\) faults in the installation & \\  & (breakages) & \\ \hline Disturbances & \(-\) installation disturbances & \(-\) modeling errors \\ \hline \multirow{4}{*}{Noses} & data- sensor noises & \multirow{2}{*}{
\begin{tabular}{c}  
In some cases, particular information can be derived from these characteristic values, such as physically defined process coefficients or specially filtered or transformed residuals. These characteristics are then compared with the usual characteristics of the defect-free process. For this purpose, methods are applied to detect and classify the changes produced by the defect. The resulting changes (discrepancies) in directly measurable signals or process patterns are considered analytical symptoms. A first classification of these detection and diagnosis methods can be made into two main families:  
* According to whether or not a mathematical model of the process is used: \(\circ\) Internal methods; \(\circ\) External methods. * According to the use of cause-effect relationships: \(\circ\) Inductive methods; \(\circ\) Deductive methods. Internal methods are mainly derived from techniques used in automation. Physical parameters evolve from physical or behavioral models validated by parameter identification techniques. This requires a thorough knowledge of the operation of the process in the form of mathematical models, and if no model is available, then human experience of the process must be used, and external methods of detection and diagnosis are resorted to.