The paper illustrates the design and the implementation of a Fault Detection and Isolation (FDI) system to a rotary machine like a multishaft centrifugal compressor. A model-free approach, that is, the Principal Component Analysis (PCA), has been employed to solve the fault detection issue. For the fault isolation purpose structured residuals have been adopted while an adaptive threshold has been designed in order to detect and to isolate the faults. To prove the goodness of the proposed FDI system, historical data of a nitrogen centrifugal compressor employed in a refinery plant are considered. Tests results show that detection and isolation of single as well as multiple faults are successfully achieved. 1. Introduction In oil and gas plants many processes such as refinery, natural gas extraction/compression, energy production, and gasification, involve the use of rotary machines like centrifugal pumps, turbines, and centrifugal compressors. Given the importance and the crucial role played by these machines, in the literature several approaches for their control and supervision have been proposed. For control purposes, the compressor regulation distant from the surge point has been discussed in [1–5] while the influence of compressors working conditions which can greatly affect the overall efficiency of the plant is considered in [6]. In recent years the attention has been focused on the prevention of possible recurrent malfunctions and potential faults which may cause equipment inactivity or even their complete break. In fact a prompt identification of perturbations on the working conditions by a diagnostic system may increase the availability of the machines and improve plant safety while achieving functioning costs reduction as well. For the fault detection on pumps machines several studies have been carried out: Dalton and Patton in [7] have proposed a model-based fault diagnosis system of a two-pump systems. Nold and Isermann in [8] have developed a fault detection system for centrifugal pumps and AC drives also derived from a model-based approach. Dealing with other rotary machines, various works can be found in the literature to diagnose possible faults on centrifugal compressor: in [9] the authors suggest using the time series analysis with neural network to realize a fault diagnosis system for monitoring a centrifugal compressor; in [10] the monitoring of the bearing temperature employed in a centrifugal compressor has been achieved. Another study concerning the detection of the fault occurring on the compressor bearings has been discussed in
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