The purpose of this paper is mainly aimed to provide an energy security strategy for the petroleum production and processing in the grand challenges. Fault detection and diagnosis is the central component of abnormal event management (AEM) [1]. According to the International Federation of Automatic Control (IFAC), a fault is defined as an unpermitted deviation of at least one characteristic property or parameter of the system from the acceptable/usual/standard condition [2-4]. Generally, there are three parts in a fault diagnosis system, detection, isolation, and identification [5, 6, 7]. Depending on the performance of fault diagnosis systems, they are called FD (for fault detection) or FDI (for fault detection and isolation) or FDIA (for fault detection, isolation and analysis) systems [5]. As the increasing needs for energy grows rapidly, energy security becomes an important issue especially in petroleum production and processing. The importance can be mainly considered in following perspectives: higher system performance, product quality, human safety, and cost efficiency [5, 8]. With this in mind, the purpose of this research is to develop a Fault Detection and Isolation (FDI) system which is capable to diagnosis multiple sensor faults in nonlinear cases. In order to lead this study closer to real world applications in oil industries, the system parameters of the applied system are assumed to be unknown. In the first step of the proposed method, phase space reconstruction techniques are used to reconstruct the phase space of the applied system. This step is aimed to infer the system property by the collected sensor measurements. The second step is to use the reconstructed phase space to predict future sensor measurements, and residual signals are generated by comparing the actually measured measurements to the predicted measurements. Since, in practice, residual signals will not perfectly equal to zero in the fault-free situation, Multiple Hypothesis Shiryayev Sequential Probability Test (MHSSPT) is introduced to further process those residual signals, and the diagnostic results are presented in probability. In addition, the proposed method is extended to a non-stationary case by using the conservation/dissipation property in phase space. The proposed method is examined by both of simulated data and real process data. The three tank model is modeled according to a nonlinear laboratory setup DTS200 and introduced for generating simulated data. On the other hand, the real process data collected from a sugar factory actuator system are also used to examine the proposed method. According to our results obtained from simulations and experiments, the proposed method is capable to indicate both of healthy and faulty situations. In the end, we have to emphasize that the proposed approach is not limited in the applications of petroleum production and processing. For example, our approach can also apply to enhance the quality of water and avoid the discharges, such as leakage, in the process of water resource management. Therefore, the proposed approach not only benefits the issue of energy security but also other issues in the grand challenges.


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