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Alarm Management

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Tongwen Chen – One of the best experts on this subject based on the ideXlab platform.

  • Design of visualization plots of industrial Alarm and event data for enhanced Alarm Management
    Control Engineering Practice, 2018
    Co-Authors: Ahmad W. Al-dabbagh, Tongwen Chen, Sirish L. Shah


    Abstract The availability of large volumes of Alarm & event data in complex industrial facilities has prompted the development of Alarm Management techniques and also resulted in a great demand to transform such data and derived results into effective visual forms. Even though good visualization applications can be found in many existing studies, systematic studies to the design of visualization plots are still rare in the area of industrial Alarm monitoring. More efforts need to be devoted to enriching the family of visualization techniques, so as to help industrial practitioners in better understanding the behavior of Alarm systems and to facilitate decision making for the enhancement of Alarm Management. This paper presents timely work in the design of visualization plots of Alarm & event data. First, a comprehensive literature survey is carried out to investigate existing visualization techniques, which are categorized into three classes based on the input information. Problems in the existing studies are summarized and design requirements for visual analytics are presented. Then, design studies on the development of visualization plots are presented in three categories, including visualization towards overall performance, visualization towards pattern insights, and visualization towards realtime applications. Examples are provided to demonstrate the effectiveness and utility of these visualization techniques.

  • An Application of Advanced Alarm Management Tools to an Oil Sand Extraction Plant
    IFAC-PapersOnLine, 2015
    Co-Authors: Muhammad Shahzad Afzal, Tongwen Chen, Gustavo Brandt, Eric Lau, Sirish L. Shah


    Abstract For better performance analysis and design improvement of industrial Alarm systems, a variety of advanced Alarm Management tools have been developed recently. These tools are quite comprehensive and handy in various applications, such as the assessment of Alarm systems, detection of nuisance Alarms, Alarm flood analysis, and recommendation of better configurations. To demonstrate the effectiveness of these tools, this paper presents some industrial case studies based on the Alarm data collected from an oil sand extraction plant operated by Suncor Energy in northern Alberta, Canada. Application results show the practicality and utility of the advanced Alarm Management tools for Alarm rationalization and routine Alarm Management.

  • CDC – Effective resource utilization for Alarm Management
    49th IEEE Conference on Decision and Control (CDC), 2010
    Co-Authors: Iman Izadi, Sirish L. Shah, Tongwen Chen


    In industrial plants, operators constantly receive a large number of Alarms that are mostly false or nuisance. A majority of these Alarms are generated by a small number of process variables known as bad actors. These bad actors are either poorly controlled which result in a lot of fluctuations; or their Alarms are poorly configured. There are a number of products on the market and many methods in the literature that can identify and give recommendations to resolve bad actors. Nonetheless, although rectifying bad actors will significantly drop the Alarm count, it is not nearly enough. To bring the performance of an Alarm system within an acceptable range (given by a number of standards), we need to go further than fixing bad actors. This second step, if not more, is as difficult and time consuming as rectifying bad actors. In this paper we discuss and present methods and ideas to improve the performance of Alarm systems beyond the bad actors.

P.k. Scanlan – One of the best experts on this subject based on the ideXlab platform.

  • TMACS test procedure TP001: Alarm Management. Revision 6
    , 1994
    Co-Authors: P.k. Scanlan


    The TMACS Software Project Test Procedures translate the project`s acceptance criteria into test steps. Software releases are certified when the affected Test Procedures are successfully performed and the customers authorize installation of these changes. This Test Procedure addresses the Alarm Management requirements of the TMACS. The features to be tested are: real-time Alarming on high and low level and discrete Alarms, equipment Alarms, dead-band filtering, Alarm display color coding, Alarm acknowledgement, and Alarm logging.

  • TMACS Test Procedure TP001: Alarm Management
    , 1994
    Co-Authors: P.k. Scanlan


    The TMACS Software Project Test Procedures translate the project`s acceptance criteria into test steps. Software releases are certified when the affected Test Procedures are successfully performed and the customers authorize installation of these changes. This Test Procedure tests the TMACS Alarm Management functions.

János Abonyi – One of the best experts on this subject based on the ideXlab platform.

  • Learning operation strategies from Alarm Management systems by temporal pattern mining and deep learning
    Computer Aided Chemical Engineering, 2018
    Co-Authors: Gyula Dorgo, Peter Pigler, Máté Haragovics, János Abonyi


    Abstract We introduce a sequence to sequence deep learning algorithm to learn and predict sequences of process Alarms and warnings. The proposed recurrent neural network model utilizes an encoder layer of Long Short-Term Memory (LSTM) units to map the input sequence of discrete events into a vector of fixed dimensionality, and a decoder LSTM layer to form a prediction of the sequence of future events. We demonstrate that the information extracted by this model from Alarm log databases can be used to suppress Alarms with low information content which reduces the operator workload. To generate easily reproducible results and stimulate the development of Alarm Management algorithms we define an Alarm Management benchmark problem based on the simulator of a vinyl acetate production technology. The results confirm that sequence to sequence learning is a useful tool in Alarm rationalization and, in more general, for process engineers interested in predicting the occurrence of discrete events.

  • SMC – Multi-temporal sequential pattern mining based improvement of Alarm Management systems
    2016 IEEE International Conference on Systems Man and Cybernetics (SMC), 2016
    Co-Authors: Richard Karoly, János Abonyi


    Even in a case of a simple failure, modern process control systems can cause a vast number of Alarms. Due to the overload of the operators these Alarm floods may result in tragedical accidents. Alarm Management systems can suppress correlated and predictable Alarms to reduce the workload of the operators. Since the process units of complex production systems are strongly interconnected, the signals defined on different process variables generate complex multi-temporal patterns. We propose a multi-temporal sequence mining based approach to extract these patterns and form Alarm suppression rules. We demonstrate the applicability of the concept in a vinyl-acetate production technology. The results illustrate the multi-temporal analysis of events defined on process variables can detect causes of Alarm, and prevent Alarm floods by pro-actively suppressing Alarms based on the extracted sequences of events.

  • Detection of Safe Operating Regions: A Novel Dynamic Process Simulator Based Predictive Alarm Management Approach
    Industrial & Engineering Chemistry Research, 2010
    Co-Authors: Tamás Varga, Ferenc Szeifert, János Abonyi


    The operation of complex production processes is one of the most important research and development problems in process engineering. A safety instrumented system (SIS) performs specified functions to achieve or maintain a safe state of the process when unacceptable or dangerous process conditions are detected. A logic solver is required to receive the sensor input signal(s), to make appropriate decisions based on the nature of the signal(s), and to change its outputs according to user-defined logic. The change of the logic solver output(s) results in the final element(s) taking action on the process (e.g., closing a valve) to bring it back to a safe state. Alarm Management is a powerful tool to support the operators’ work to control the process in safe operating regions and to detect process malfunctions. Predictive Alarm Management (PAM) systems should be able not only to detect a dangerous situation early enough, but also to give advice to process operators which safety action (or safety element(s)) mus…