Dynamic Alarm Research Articles

Dynamic alarm prediction for critical alarms using a probabilistic model

Alarm systems play important roles for the safe and efficient operation of modern industrial plants. Critical alarms are configured with a higher priority and are safety related among many other alarms. If critical alarms can be predicted in advance, the operator will have more time to prevent them from happening. In this paper, we present a dynamic alarm prediction algorithm, which is a probabilistic model that utilizes alarm data from distributed control system, to calculate the occurrence probability of critical alarms. It accounts for the local interdependences among the alarms using the n-gram model, which occur because of the nonlinear relationships between variables. Finally, the dynamic alarm prediction algorithm is applied to an industrial case study.

Whole-Condition Dynamic Alarm Research of Hydropower Unit Operating Parameters

Set the alarm value in the condition monitoring system of hydropower unit to monitor unit state parameters, if the difference between the two values exceeds the safety threshold, the alarm works, it is an effective method. Now the state parameters alarm values are set up mainly for the stable operation area in the unit condition monitoring system, which were not suit for low load conditions and in the process of load. To realize the full condition dynamic alarm of hydroelectric generating units, they are two ways: (1), build unit state parameter prediction model, compared predict values with the measured values. (2), set the alarm line, when the measured value exceeds the alarm line, the alarm works,. These two methods are considered the working condition head and output .This paper introduces several different alarm line settings, such as line alarm, partition line alarm, dynamic alarm curve and dynamic alarm surface.

Integrating reinforcement learning, equilibrium points, and minimum variance to understand the development of reaching: a computational model.

Despite the huge literature on reaching behavior, a clear idea about the motor control processes underlying its development in infants is still lacking. This article contributes to overcoming this gap by proposing a computational model based on three key hypotheses: (a) trial-and-error learning processes drive the progressive development of reaching; (b) the control of the movements based on equilibrium points allows the model to quickly find the initial approximate solution to the problem of gaining contact with the target objects; (c) the request of precision of the end movement in the presence of muscular noise drives the progressive refinement of the reaching behavior. The tests of the model, based on a two degrees of freedom simulated dynamical arm, show that it is capable of reproducing a large number of empirical findings, most deriving from longitudinal studies with children: the developmental trajectory of several dynamical and kinematic variables of reaching movements, the time evolution of submovements composing reaching, the progressive development of a bell-shaped speed profile, and the evolution of the management of redundant degrees of freedom. The model also produces testable predictions on several of these phenomena. Most of these empirical data have never been investigated by previous computational models and, more important, have never been accounted for by a unique model. In this respect, the analysis of the model functioning reveals that all these results are ultimately explained, sometimes in unexpected ways, by the same developmental trajectory emerging from the interplay of the three mentioned hypotheses: The model first quickly learns to perform coarse movements that assure a contact of the hand with the target (an achievement with great adaptive value) and then slowly refines the detailed control of the dynamical aspects of movement to increase accuracy.

A dynamic alarm management strategy for chemical process transitions

Chemical processes frequently operate upon a multitude of steady states and transitions between these states are inevitable. Unfortunately, transitions are exactly where alarm floods often occur. Alarm floods cause critical alarms overwhelmed and thus increase the probability of larger safety issues. Existing techniques for the design of alarm systems mostly focus on one steady state of operation and yet cannot effectively deal with alarm floods during transitions. In this paper, a dynamic alarm management strategy is proposed for controlling alarm floods during transitions of chemical processes. In this strategy, the artificial immune system-based fault diagnosis (AISFD) method and a Bayesian estimation based dynamic alarm management (BEDAM) method are integrated. During transitions, dynamic alarm limits obtained by the BEDAM method can control alarm floods. However, if a process fault occurs during transitions, a flood of alarms could still be yielded. To generate useful alarms in fault situations, an artificial immune system based on dynamic time warping (DTW) is used for fault detection and diagnosis. Finally, in case studies, the dynamic alarm management strategy is applied to the startup stage and a throughput change transition in a pilot-scale distillation column.

An Automatic Alarm Scheme of Video Security Monitoring System Based on Autocorrelation Function

This study presents an autocorrelation function-based program of dynamic image recognition and automatic alarm. Lighting effect analysis, binarization of the dynamic image and other methods were used to enhance the image; a dynamic image autocorrelation function was established to determine the threshold of automatic alarm system; and using Matlab software a simulation experiment was conducted on the autocorrelation function. The experiment showed that the correlation function value is inversely proportional to the amount of change of the video surveillance image; the autocorrelation function value can be used to determine when to trigger the automatic alarm system. This dynamic image recognition program, characterized by strong stability and little impact from the outside, can be used for automatic alarm of monitoring system.

Development of a new EDRNN procedure in control of human arm trajectories

In this paper the trajectory tracking control of a human arm moving on the sagittal plane is investigated by an interdisciplinary approach with the combination of neural network mapping, evolutionary computation, and dynamic system control. The arm in the study is described by a musculoskeletal model with two degrees of freedom and six muscles, and the control signal is applied directly in the muscle space. A new control system structure is proposed to manipulate the complicated nonlinear dynamical arm motion. To design the intelligent controller, an evolutionary diagonal recurrent neural network (EDRNN) is integrated with proper performance indices, in which genetic algorithm (GA) and evolutionary program (EP) strategy are effectively integrated with the diagonal recurrent neural network (DRNN). The hybrid GA with EP strategy is applied to optimize the DRNN architecture and an adaptive dynamic back-propagation (ADBP) algorithm with momentum for the multi-input multi-output (MIMO) systems is used to obtain the network weights. The effectiveness of the control scheme is demonstrated through a simulated case study.

Approximate Optimal Control as a Model for Motor Learning.

Current models of psychological development rely heavily on connectionist models that use supervised learning. These models adapt network weights when the network output does not match the target outputs computed by some agent. The authors present a model of motor learning in which the child uses exploration to discover appropriate ways of responding. The model is consistent with what is known about how neural systems evaluate behavior. The authors model the development of reaching and investigate N. Bernstein's (1967) hypotheses about early motor learning. Simulations show the course of learning as well as model the kinematics of reaching by a dynamical arm.

An integrated knowledge base development tool for knowledge acquisition and verification for NPP dynamic alarm processing systems

Knowledge acquisition and knowledge base verification are important activities in developing knowledge-based systems such as alarm processing systems in nuclear power plants (NPPs). In this work, we developed an integrated tool, for knowledge acquisition and verification of NPP alarm processing systems, by using a G2 tool. The tool integrates the documents analysis method and extended colored Petri net (ECPN) matrix analysis method with backward simulation for knowledge acquisition and knowledge verification, respectively. This tool enables knowledge engineers to perform their tasks from knowledge acquisition to knowledge verification effectively and consistently.

The real-time functional test facility for advanced instrumentation and control in nuclear power plants

Testing and validation of the functions and performance of the digital instrumentation and control (I&C) system should be done prior to installation in nuclear power plants. The objective of the I&C Functional Test Facility (FTF) is to test and validate the functions of developed digital control and various monitoring systems. The FTF provides the simulated testing environment as an experimental test bed. The FTF software consists of a mathematical modeling program which simulates a three-loop 993 MWe pressurized water reactor and a supervisory program that comprises all the instructions necessary to run the FTF. The hardware equipment provides an interface between a host computer and a simple test panel or the developed target systems to be tested. The graphical user interface supports an easy and friendly interface between the FTF and users. It is implemented through a Picasso-3 graphic tool developed by the Halden Reactor Project. The FTF is applied to an advanced I&C system prototype, such as an automatic start-up intelligent control system, dynamic alarm system, accident identification system, and intelligent logic tracking system, to test its algorithm or performance. The results of the test show good operational performance of the FTF in normal and transient conditions.

Development of an advanced human–machine interface for next generation nuclear power plants

An advanced human–machine interface (HMI) has been developed to enhance the safety and availability of a nuclear power plant (NPP) by improving operational reliability. The key elements of the proposed HMI are the large display panels which present synopsis of plant status and the compact, computer-based work stations for monitoring, control and protection functions. The work station consists of four consoles such as a dynamic alarm console (DAC), a system information console (SIC), a computerized operating-procedure console (COC), and a safety system information console (SSIC). The DAC provides clean alarm pictures, in which information overlapping is excluded and alarm impacts are discriminated, for quick situation awareness. The SIC supports a normal operation by offering all necessary system information and control functions over non-safety systems. In addition, it is closely linked to the other consoles in order to automatically display related system information according to situations of the DAC and the COC. The COC aids operators with proper operating procedures during normal plant startup and shutdown or after a plant trip, and it also reduces their physical/mental burden through soft automation. The SSIC continuously displays safety system status and enables operators to control safety systems. The proposed HMI has been evaluated using the checklists that are extracted from various human factors guidelines. From the evaluation results, it can be concluded that the HMI is so designed as to address the human factors issues reasonably. After sufficient validation, the concept and the design features of the proposed HMI will be reflected in the design of the main control room of the Korean Next Generation Reactor (KNGR).

Knowledge acquisition for alarm processing systems of nuclear power plants using documents analysis

The knowledge acquisition is one of the most difficult and time-consuming activities in developing knowledge-based systems. In this work, we propose a novel knowledge acquisition method through documents analysis. The knowledge base can be built correctly, rapidly, and partially automatically by using this method. This method is especially useful when it is difficult to find domain experts. We apply this method to the knowledge acquisition for a simple dynamic alarm processing system (DAPS) for nuclear power plants and develop the alarm knowledge acquisition system (AKAS) for DAPS by using G2.

A dynamic new alarm system for use in biological early warning systems

AbstractThe Musselmonitor® (Delta Consult, Kapelle, The Netherlands) is a biomonitor that uses the valve position and the activity of eight Dreissena polymorpha to monitor the water quality. The normal behavior was studied to evaluate whether eight mussels are sufficient for reliable and reproducible data. The Musselmonitor was placed on a location with strong fluctuations of physical and chemical parameters. The results clearly indicate significant correlations between the valve position and the activity of the mussels and several parameters, such as temperature and concentration of suspended material (represented as turbidity and chlorophyll concentration). The valve position also indicates diurnal and seasonal rhythms in the natural behavior. A new data analyzing system was developed for the Musselmonitor, which takes into account the natural fluctuations in the behavior. The sensitivity of the dynamic data analyzing system was evaluated with copper (20, 40, 50, and 80 μg/L) in dosage experiments and the results were compared with the existing static alarm system. The study revealed that static threshold levels are insensitive and require a lot of experience. The dynamic alarm system works satisfactorily with the Musselmonitor and is very useful for the detection of discontinuities in the normal behavior.

Development strategies of an intelligent human-machine interface for next generation nuclear power plants

An intelligent human-machine interface (HMI) has been developed to enhance the safety and availability of a nuclear power plant (NPP) by improving operational reliability. The key elements of the proposed HMI are the large display panels that present synopsis of the plant status and the compact, digital workstations for monitoring, control, and protection functions: The workstation consists of four consoles such as a dynamic alarm console (DAC), a system information console (SIC), a computerized operating-procedure console (COC), and a safety-related information console (SRIC). The DAC provides clean alarm pictures in which information overlapping is excluded and alarm impacts are discriminated for quick situation awareness. The SIC supports a normal operation by offering all necessary system information and control functions. In addition, it is closely linked to automatically display the related system information according to situations of the DAC and the COC. The COC aids the operator with proper emergency operation guidelines so as to shutdown the plant safely, and it also reduces his physical/mental burden by tracing the operating procedures automatically. The SRIC continuously displays safety-related information so that the operator can assess the plant status focusing on plant safety. The proposed HMI has been validated and demonstrated using the full-scope simulator for Yonggwang Units 1, 2. From the demonstration results, it can be concluded that the HMI enables the operator to terminate or mitigate plant disturbances early. After sufficient validation, the characteristic design features of the proposed HMI will be reflected in the main control room for the Korean next generation NPP's.

Advanced Alarm-Management Post-Processor Prototype for a Fossil Power Station

During a power station disturbance, the data processing and monitoring system (DPMS) may present an overwhelming number of alarms (200-400) to the power station operators within a short period of time. This may lead to operator confusion and makes problem analysis and decisions difficult. An advanced post-processor Connected to the existing DPMS is being developed at IEC. In the present stage of this project our aim is to develop a model for dynamic alarm management and adaptive operator guidance that will permit the verification of the implementation methodology. The hierarchical structure of the power station is mapping the different equipment features and technological processes, to the structure used by our alarm-management system. On the basis of this double hierarchy, all the DPMS points were grouped and each DPMS point labeled with its alarm priority level (i.e., urgent alarm, alarm, warning, not an alarm). Cause-consequence graphs were constructed. The nodes of the graphs were the selected points of the DPMS. According to our methodological proposal the cause-consequence connections between the selected points of the DPMS may be constructed on the basis of the ready made ’flow diagram - process and instrument diagrams’, ’control logic diagrams’ and ’control and instrument diagrams’ of the power station documentation. Our prototype includes some limited parts of the power station such as deareator level control, one of the turbine driven boiler feed-water pumps and cooling system of the unit main transformer. About 100 rules were generated from the cause-consequence graphs. Some 200 inputs were simulated off-line. The user interface and the inference machine were built by using the KAPPA PC (IntelliCorp, Inc.) expert system shell. This prototype is being tested now in the Training Center of the Operation Division.

The Safety Design of Process Automation, Experiences and Methods

A control project for a complex batch process producing organic fine chemicals is discussed. Because of process characteristics special attention was devoted to the safety of the control system. In the design of the control room general principles of man-manchine interaction were utilized. New systematic review methods were developed as Finnish contribution to the European Co-operation in the Field of Scientific and Technical Research (COST-A1 project). In alarm handling a new concept with dynamic alarm limits was introduced. Experiences are discussed. Based on these, some ideas for the development of new safety oriented design and review methods are presented.