Conditional Gaussian network as PCA for fault detection

International audienc

Fault detection with Conditional Gaussian Network

The main interest of this paper is to illustrate a new representation of the Principal Component Analysis (PCA) for fault detection under a Conditional Gaussian Network (CGN), a special case of Bayesian networks. PCA and its associated quadratic statistics such as T2 and SPE are integrated under a sole CGN. The proposed framework projects a new observation into an orthogonal space and gives probabilities on the state of the system. It could do so even when some data in the sample test are missing. This paper also gives the probabilities thresholds to use in order to match quadratic statistics decisions. The proposed network is validated and compared to the standard PCA scheme for fault detection on the Tennessee Eastman Process and the Hot Forming Process

A Bayesian network dealing with measurements and residuals for system monitoring

The purpose of this paper is to present an original method for system monitoring with Bayesian networks. Our proposal is to associate a data-driven method to another model-based under a common tool. The two methods are first modeled under a Bayesian network (conditional Gaussian network), and then combined to evaluate the system state. In the proposed framework the residuals and measures coexist under a probabilistic framework. This approach is tested on a simulation of a water heater process under some various circumstances and shows better results than the two methods used alone

Environmental Impact Assessment of a Flood Control Channel in Sfax City, Tunisia

The objective of this study is to evaluate water and sediment quality in the southern branch of a flood control channel in Sfax city, as well as its neighboring sites. This artificial channel, located 4km away from downtown Sfax, was implemented in 1984 to protect the city against floods. Even though it contributed to reduce the harmful flood effects, this channel also resulted in new environmental problems that may cause a public health threat. Indeed, artificial surfaces pose a greater risk of infection due to bacteria, fungi and other microorganisms. A total of 19 water samples (9 from the channel and 10 from groundwater wells) were collected in a dry period and analyzed in the laboratory. Furthermore, 12 sediment samples were taken from the bottom of the channel. Water quality data were used to examine the spatial variability of the different water quality parametrs. The resultant maps revealed an important contamination and illustrated that the degree of contamination differs from one site to another, depending on the distance from the pollution source (industrial, domestic or agricultural activity), the depth of the groundwater table and also the maintenance of the well and its surroundings

False synergy between vancomycin and β-lactams against glycopeptide-intermediate Staphylococcus aureus (GISA) caused by inappropriate testing methods

ABSTRACTThe combination of vancomycin and β-lactams is often considered synergistic and has been recommended for the treatment of glycopeptide-intermediate Staphylococcus aureus (GISA) infections. In this study, the combination of vancomycin or teicoplanin with different β-lactams was tested. When using NaCl 4% w/v, for better expression of heterogeneous resistance to β-lactams, with a longer (48-h) incubation period and a higher (107 CFU/mL) inoculum, the association of vancomycin with β-lactams was antagonistic. However, a synergistic effect was observed for teicoplanin under the same conditions

PCA in a Bayesian framework for fault detection

In this paper, we give an original representation of Principal Component Analysis (PCA) for fault detection. PCA with its corresponding quadratic test statistics are integrated under a particular case of Bayesian Networks (BNs) named Conditional Gaussian Network (CGN). The proposed network maps a new observation to an orthogonal space and gives probabilities on the state of the system even when some data in the sample test are missing. An illustrative example is given on a simple process