Intelligent Virtual Measurement in a Process Plant Based on Model Based Diagnosis

The principle of Model Based Diagnosis (MBD) provides a basis from which e cient fundamental techniques that are applicable to various problem tasks such as measurement, monitoring and design are derived, and it is not limited to only a stand alone diagnosis system. Consistency checking, generation of conicts and derivation of diagnoses in the MBD principle can be used to validate a sensing system and to provide appropriate interpretations of measurement in process plant instrumentation. This idea plays an important role especially for an indirect sensing system that consists of multiple complex measurement schemes and requires high robustness in its operation. Besides, recently a new technique named as Virtual Measurements (VMs) has become practical in accordance with the progress of on-line computing technology. Operators of any process plant often faces a situation in which they are reqested to obtain estimates of some important physical quantities even in rough accuracy to maintain the plant in a safety state. VMs are used to indirectly obtain rough estimate of objective quantities under an even distorted and incomplete measurement environment. It is expected to support operators in making decisions by providing the credible estimation. Since the estiamted values must be ensured to be appropriate, the validity of the measurement schemes should be always monitored and diagnosed, and an appropriate interpretation of the valid measurement for each faluty candidate should be provided. The work presented here proposes a framework of the application of MBD to a VM system to measure the ow rate of the secondary sodium coolant loop of a fast breeder nuclear reactor. The role of the MBD is the diagnosis of the VM system and the interpretation of valid measurements under the candidate faults. The feasibility of the proposed framework is demonstrated by actual experimental data of the measurement signals that are associated with a secondary sodium coolant pump. Introduction The basic principle of Model Based Diagnosis (MBD)(Reiter 1987; de Kleer &Williams 1987) forms a rm foundation for e cient elementary techniques that are applicable to various problem tasks such as measurement, monitoring and design, and is not limited to only a stand alone diagnosis system(Alberts et al. 1993). The validation of a sensing system in process plant instrumentation is considered to be one of the candidate applications of MBD. In the operation of a process plant, operators often face situations in which they are required to obtain the credible estimates of some important physical quantities even in rough accuracy to maintain the plant safety. To support operators under these situations, a sensing system to directly measure and/or indirectly estimate some safety-related key quantities has been developed(Beltracchi & Lapinsky 1985). This type of sensing systems must be highly robust against the degradation of the quality and the loss of completeness of some measurement signals due to the faults of some components and devices that are involved in the measurement processes. Feasible interpretations of the values of the key quantities must be provided at least, even if their unique values can not be determined. Accordingly, the validation of the measurement processes should be conducted on the basis of monitoring and diagnosis, and the appropriate interpretations of the valid measurements for each candidate fault should be provided in an e ective manner. The principle of the MBD is applicable to the diagnosis of the faulty processes in the sensing system and to the interpretation of the feasible values of the objective quantities. To enhance the credibility and the robustness of the sensing system in a process plant, various techniques have been developed and actually implemented. The credibility of the direct sensing of an objective quantity by using a dedicated sensor, e.g., a ow meter to measure the ow rate of a uid, is sensitive to the reliability of the sensor hardware. When the reliability is not su cient enough, hardware redundancy such as 2 out of 3 logic has been applied. However, as this is costly, smarter approaches have been developed. One of the approaches is analytic redundancy in which the values of the objective quantities are indirectly estimated from other sensing information by a physical model(Gertler 1988). For instance, the ow rate of a uid is estimated from the sensing information of the valve opening and the pressure drop between its inlet and outlet by a physical model of the valve. However, this approach requires a complete physical model and also a complete set of observed signals for the model to calculate the objective quantities. This restriction reduces the applicability of this technology in practical environments of the process plants because a complete model of the plant and the clear condition of its applicability are hardly obtained for the large scale and complex plants. Moreover, the arrangement and the quality of the sensors in the plant are not always sufcient for the purpose of indirect estimation since the speci cation of plant instrumentation is not designed for the technology. This issue of the indirect estimation of physical quantities gave birth to a new technique named as Virtual Measurements (VMs)(Ikonomopoulos, Tsoukalas, & Uhrig 1993). It is based on the recent progress of neural network research, and does not have to use any complete model and complete observed signals to obtain the indirect estimation of the objective quantities. It has high robustness under certain assumptions to the degraded environment so that the feasible values of the objective quantities are estimated even under distorted and incomplete measurement environments without using any physical models. The feature of this technique will provide an e cient remedy to the limited applicability of the current indirect estimation methodology. One of the representative study on VMs is due to L. Tsoukalas et al.(Ikonomopoulos, Tsoukalas, & Uhrig 1993) They applied multiple neural networks to obtain an experimental model of the valve opening in a plant and to estimate the value. Their method demonstrated high applicability of VMs under the lack of some important measurement signals and the anomalous states of the plant. However, an issue remains. When some of the assumptions become no longer valid by the change of operation modes and/or when some faults occurs in the plant, the estimated values of objective quantities may also become invalid. Accordingly, the validity of the assumptions of the VM system must be always monitored. Once a violation of the assumptions are detected, the identi cation of the irrelevant assumptions, i.e., diagnosis, and the feasible interpretation of the objective quantities must be conducted. The work presented here proposes a framework of the application of MBD to a VM system. The role of the MBD is the diagnosis of the assumptions of the VM system, and the interpretation of valid measurements when some of the assumptions are thought to be fault. The feasibility of the proposed framework is demonstrated to measure the ow rate of sodium coolant using actual experimental data of the measurement signals associated with a secondary sodium coolant pump of a fast breeder nuclear reactor. Virtual Measurements The most of the past work to develop VMs utilized the conventional three layers feedforward neural networks(Ikonomopoulos, Tsoukalas, & Uhrig 1993; Sakuma, Kitamura, & Washio 1994). The advantages of this type of neural network are its simple implementation and the fast convergence in training. However, its ability to embed the given data is basically limited to the static relations among the data. As the virtual measurements should provide valid estimation of the objective quantities under a dynamic change in the plant state, this feature of the conventional approach easily mislead the estimation. We, therefore, adopted two layers recurrent neural networks called as Elman networks(Elman 1990). The outline of this network is depicted in Fig. 1. The multiple inputs are provided to each neuron in the rst layer. The output function of each neuron in the rst layer is a tangent sigmoid function, and the output is fed back to the inputs of all neurons in that layer. Also, the outputs are loaded to the unique neuron in the second layer. The neuron in the second layer is completely linear, and its output is the result of this entire neural network. This network has the following features. 1. The recurrent mechanism enables the learning of the highly dynamic features of given data. 2. Any relations among given data can be embedded if the su cient number of neurons is provided in the rst layer. These features are considered to yield high applicability of VMs under dynamic and complex environments of the plant. The inputs to the network are the sensor signals having some relations to the objective quantity which is provided at the output. The valve opening and the pressure drop between the inlet and outlet of the valve can be input signals of the Elman network to give the estimated value of the ow rate of a uid crossing the valve at the output. The neural network needs to be trained by using the empirical data representing the relations among these inputs and the output quantities. The use of e cient learning algorithm is prerequisite to the case of the recurrent network because the convergence speed of the training is generally slow due to the compensation of the output change of each neuron by the feedback e ect. Accordingly we applied an advanced back propagation algorithm using the momentum method in concert with the adaptive learning rate method(Wasserman 1989). Though an well-trained neural network, i.e., a VM, provides an estimation of the objective quantity, the assumptions for the VM must hold to provide the