On-line Sensor Calibration Monitoring and Fault Detection for Chemical Processes

In most process industries, periodic sensor calibrations are required to assure sensors are operating properly. Out-of-calibration sensors can result in decreased product quality, possibly causing a loss in revenue. Real-time, continuous sensor calibration monitoring is desirable to assure product quality and reduce maintenance costs associated with performing unnecessary manual sensor calibrations. An artificial neural network-based sensor calibration monitoring system can provide continuous sensor status information. This paper describes the design of a neural network-based instrument surveillance and calibration verification system (ISCV) for a chemical processing system. INTRODUCTION When monitoring complex processes, it is difficult, or impossible, to detect small drifts in sensor instrumentation. These drifts can cause incorrect control actions, poor product quality, and decreased process efficiency. The current method used to guard against calibration drifts is periodic sensor calibration. These calibrations usually require the instrument be taken out of service and be falsely-loaded to simulate actual in-service stimuli. This can lead to equipment damage and incorrect calibration due to adjustments made under non-service conditions. A less invasive technique is desired. As increased economic competitiveness necessitates streamlining plant operations, condition based maintenance strategies rather than periodic or corrective maintenance strategies are desired. Changing calibration strategies to be condition-based requires that instruments be manually recalibrated only when their performance is degraded beyond a specific tolerance. Continuous verification of the instrument’s calibration will reduce unnecessary sensor calibrations and give operators more confidence in sensor measurements. Elimination of unnecessary maintenance results in cost savings and reduced down time while a better knowledge of the actual state of the process, due to more reliable sensor values, could result in increased product quality, reduced equipment damage, and increased plant efficiency. Specifically, this system continuously monitors the condition of process sensors and allows for the automatic replacement of faulty sensor values with the system’s best estimate. This system aids in scheduling maintenance and increases plant reliability. SYSTEM OF INTEREST Figure 1 is a simple block diagram of the chemical process of interest. The process has instrumentation which measure flows, temperatures, pressures and levels which need to be operating properly to ensure a high quality product. A neural network based sensor calibration and monitoring system can fulfill this need. Siloxanes w/ acid pump 1st w ash phase separator pump 2nd wash phase separator pump distillation feed tank Acid-free siloxanes Figure 1. Siloxanes Wash/Separation Process ISCV SYSTEM ARCHITECTURE The artificial neural network (ANN) based instrument surveillance and calibration verification system (ISCV) has the following major components: a signal estimation module using autoassociative neural network (AANN) architecture, a statistical decision logic module based on the sequential probability ratio test (SPRT), a faulty sensor correction module, and a network tuning module. A block diagram of the ISCV system is shown in Figure 2. Signal Estimation Module (AANN) Sensor Signals + Statistical Decision Module Correction Module Retuning Module Sensor Status Figure 2. ISCV System Block Diagram Signal Estimation Module The use of AANNs for plant wide monitoring has been widely reported in the nuclear industry [B. R. Upadhyaya and E. Eryurek, 1992, R. E. Uhrig, et al, 1996, Nabeshima, et al, 1995]. Similar work using ANNs applied to chemical process systems have also been reported [Dong and McAvoy, 1994, Kramer, 1992]. The work presented in this paper advances the AANN methodology by introducing a faulty sensor replacement algorithm and a model tuning procedure. This research is also significant because it uses data from a chemical process which is not instrumented as fully as nuclear power plants studied previously [Hines 1998]. In an autoassociative neural network, the outputs are trained to emulate the inputs over an appropriate dynamic range. Many plant variables that have some degree of coherence with each other constitute the inputs. During training, the interrelationships among the variables are embedded in the neural network connection weights. A robust training procedure is used to force the network to rely on redundant information from correlated sensors to estimate that specific sensor’s value. As a result, any specific network output shows virtually no change when the corresponding input has been distorted by noise, faulty data, or missing data. This characteristic allows the AANN to detect sensor drifts or failures by comparing sensor measurements (network inputs) with the corresponding network estimates of the sensor values (network outputs). Figure 3 shows a sensor monitoring module for a group of four sensors whose measurements are correlated to some degree (actual networks have 15-30 correlated sensors as inputs). When a sensor's signal to the autoassociative network is faulty due to a drift or gross failure, the network still gives a valid estimate of the sensor value due to its use of information from other correlated sensors. The difference or residual (rn) between the sensor estimate (sn') and the actual measurement (sn) normally has a zero mean and a variance related to the amount of noise in the sensor's signal. When a sensor is faulty, its associated residual's mean or variance changes. This can be detected with the statistical decision logic. Fig. 3. Sensor Monitoring Module Model AANN s1 s2 s3 s4 s1` r1 Σ +