# Distributed Fault Detection and Isolation Approach for Oil Pipelines
###### Abstract  
Fault detection and isolation (FDI) in oil pipeline systems (OPS) is a very critical issue because faults in these systems such as leaks or equipment malfunctions may cause significant safety accidents and economic losses. These are the challenging factors, along with the environmental regulations for developing efficient FDI approaches for OPS. This paper proposes a model-based distributed FDI approach, which uses a structural model of the system in conjunction with algorithms to generate diagnostic tests that may be implemented in local diagnosers along the OPS. The proposed approach allows detection and isolation of faults in pipeline sections (pipeline segments), pump stations, as well as process control equipment. In this way, simulation of the obtained diagnostic tests in a benchmark application shows that all faults of interest (pipeline segment faults and sensor faults) are detected and isolated. distributed model based fault diagnosis; structural analysis; diagnostic test; oil pipelines  
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For pipeline leak detection, the most common classification is based on the detection technology characteristics and can be divided into hardware-based and software-based methods [18; 19]. In hardware-based approaches, different sensing devices are used to detect failures [12]. Likewise, in software-based approaches, diverse soft computing methods have been developed for leak detection in oil pipelines networks [13; 20]. However, many of these methods show shortcomings, such as, e.g., long response times and incidence of false alarm reports [21]. On the other hand, FDI of plant components could use data-based methods that exploit available experimental (historical) data [22]; model-based methods that are based on comparing the actual outputs of the monitored system with the outputs obtained from an analytical mathematical model [23]; and knowledge-based methods which generally use the relationships between faults and symptoms in the form of rules [24]. Particularly, FDI in large scale distributed plants is a difficult task due not only to the large amount of components, but also to its interconnected subsystems with coupled variables between them [25], here by using model-based approaches, a centralized or a distributed architecture in the design of the FDI system may be considered. The centralized architecture requires a global model and may be an impractical option (when implementing a global diagnoser) because of the amount of needed communication [23]. Distributed diagnosis using a structural model has been proposed in [26], a distributed diagnosis approach with a set of local diagnosers was presented in [16; 17], and, in [27], a decentralized FDI system based on structural analysis of a large distributed plant was designed. Distributed FDI architecture consists of subsystem models and is implemented with local diagnosers that together guarantee the same diagnosability as a regular centralized architecture, allowing high performance in scalability, reliability, communication, and reduced computation costs [27; 28; 29]. In this sense, given that oil pipelines are large-scale distributed plants [4; 30] with many components, it would be more convenient to apply the distributed FDI approach, in which, unlike centralized approaches, knowing the model of the global plant is not mandatory [17; 25; 31]. Additionally, the main performance indexes of FDI methods in OPS are positioning accuracy, response time, sensitivity, and false alarm rate [32]. Nevertheless, as [PERSON] et al. [14] pointed out, no single method can always meet all the requirements, and each technique has its advantages and disadvantages in different circumstances. Moreover, recent surveys about existing FDI methods that can be used in oil and gas pipelines [32; 33] concluded that the combination of two or more methods is the future course in FDI in OPS. In this context, the use of a FDI method for OPS components (e.g., pipeline segment fault, sensor fault, valve fault, etc.)