https://www.ece.lsu.edu/mcu/lawss/add_materials/FaultDetectionPart1.pdf

A Review of Process Fault Detection and Diagnosis Part I : Quantitative Model-Based Methods

A review of process fault detection and diagnosis: Part III: Process history based methods

An algorithm is developed for computing which sensors to add to meet a diagnosis requirement specification concerning fault detectability and fault isolability. The method is based only on the structural information in a model, which means that possibly large and nonlinear differential-algebraic models can be handled in an efficient manner. The approach is exemplified on a model of an industrial valve where the benefits and properties of the method are clearly shown.

Sensor Placement for Fault Diagnosis

Application of signed digraphs-based analysis for fault diagnosis of chemical process flowsheets

https://www.ece.lsu.edu/mcu/lawss/add_materials/FaultDetectionFuzzyLogic.pdf

Fuzzy-logic based trend classification for fault diagnosis of chemical processes

Fault diagnosis is an important task for the safe and optimal operation of chemical processes. Hence, this area has attracted considerable attention from researchers in the past few years. A variety of approaches have been proposed for solving this problem. All approaches for fault detection and diagnosis in some sense involve the comparison of the observed hehavior of the process to a reference model. The process behavior is inferred using sensors measuring the important variables in the process. Hence, the efficiency of the diagnostic approach depends critically on the location of sensors monitoring the process variables. The emphasis of most of the work on fault diagnosis has been more on procedures to perform diagnosis given a set of sensors and less on the actual location of sensors for efficient identification of faults. A digraph-based approach is proposed for the problem of sensor location for identification of faults. Various graph algorithms that use the developed digraph in deciding the location of sensors based on the concepts of observability and resolution are discussed. Simple examples are provided to explain the algorithms, and a complex FCCU case study is also discussed to underscore the utility of the algorithm for large flow sheets. The significance and scope of the proposed algorithms are highlighted.

Rao Raghuraj

Papers

Locating sensors in complex chemical plants based on fault diagnostic observability criteria