Sandeep R. Kondaveeti

Bio: Sandeep R. Kondaveeti is an academic researcher from University of Alberta. The author has contributed to research in topics: ALARM & Manual fire alarm activation. The author has an hindex of 8, co-authored 10 publications receiving 329 citations. Previous affiliations of Sandeep R. Kondaveeti include CNOOC Limited.

Quantification of alarm chatter based on run length distributions

Abstract: In the process industry, alarms are configured on the control system to provide indication of abnormal events to the control room operators. In the presence of improper design of alarm generating algorithm or lack of appropriate tuning, alarms are announced more frequently than what is typically sufficient to alert the operator, a condition commonly known as ‘alarm chatter’. Chattering alarms are the most common form of nuisance alarms. The concept of run length is introduced in the alarm management context to study alarm chatter and an index is proposed to quantify the degree of alarm chatter based on run length distributions obtained exclusively from readily available historical alarm data. Chatter index hence plays a crucial role in routine assessment of industrial alarm systems. Prominent features of the proposed chatter index and its variant are demonstrated using industrial data.

Graphical Representation of Industrial Alarm Data

Abstract: Alarms are important for safe and reliable operation of a process Ideally, every alarm that is presented to the operator requires an action Owing to the ease in implementing alarms, many modern day process plants have a large number of alarms configured in their alarm system Many of these alarms are set without proper rationalization resulting in the generation of nuisance alarms During process upsets, the volume of alarms presented to the operator is often too large to facilitate appropriate and timely actions This work demonstrates some novel visualization tools that can be used for assessing the performance of alarm systems in terms of effectively identifying nuisance alarms The utility of the developed tools is illustrated using real industrial alarm data

Runs and Scans With Applications

Abstract: of the conditional distribution speciŽ cations. Chapters 8 and 10 extend these methods from two to more dimensions. Chapter 9 investigates estimation in conditionally speciŽ ed models. Chapter 11 considers models speciŽ ed by conditioning on events speciŽ ed by one variable exceeding a value rather than equaling a value, and Chapter 12 considers models for extreme-value data. Chapter 13 extends conditional speciŽ cation to Bayesian analysis. Chapter 14 describes the related simultaneous-equation models, and Chapter 15 ties in some additional topics. An appendix describes methods of simulation from conditionally speciŽ ed models. Chapters 1–4, plus Chapters 9 and 13, comprise a lively overview of conditionally speciŽ ed models. The remainder of the text constitutes a detailed catalog of results speciŽ c to different conditional distributions. Although this catalog is certainly of value, the reader desiring a briefer and less detailed introduction to the subject might skip the remainder at Ž rst reading. This text is a revision of the book by Arnold, Costillo, and Sarabia (1992). The current version is of similar breadth, but with much more depth than the original. The text is clearly written and accessible with relatively few mathematical prerequisites. I found surprisingly few typographical errors; the authors are to be congratulated for this. In a few cases, regularity conditions for results are not given in full. Generally, this causes little confusion, although something does appear to be missing in the statement of Aczél’s key theorem (Theorem 1.3). Fortunately, most of the results in the sequel are derived from corollaries to this theorem, and the corollaries are stated more precisely. I noted few gaps in the material covered. The only area that I thought was insufŽ ciently represented was application to Markov chain Monte Carlo. Conditional speciŽ cation is particularly important in Gibbs sampling. I believe that many practitioners would beneŽ t from a discussion of the issues involved in these sampling schemes. Each chapter contains numerous exercises. These exercises appear to be at an appropriate level for a graduate course in statistics, and appear to provide appropriate reinforcement for the material in the preceding chapters.

An Overview of Industrial Alarm Systems: Main Causes for Alarm Overloading, Research Status, and Open Problems

Abstract: Alarm systems play critically important roles for the safe and efficient operation of modern industrial plants. However, most existing industrial alarm systems suffer from poor performance, noticeably having too many alarms to be handled by operators in control rooms. Such alarm overloading is extremely detrimental to the important role played by alarm systems. This paper provides an overview of industrial alarm systems. Four main causes are identified as the culprits for alarm overloading, namely, chattering alarms due to noise and disturbance, alarm variables incorrectly configured, alarm design isolated from related variables, and abnormality propagation owing to physical connections. Industrial examples from a large-scale thermal power plant are provided as supportive evidences. The current research status for industrial alarm systems is summarized by focusing on existing studies related to these main causes. Eight fundamental research problems to be solved are formulated for the complete lifecycle of alarm variables including alarm configuration, alarm design, and alarm removal.

Improved correlation analysis and visualization of industrial alarm data.

Abstract: The problem of multivariate alarm analysis and rationalization is complex and important in the area of smart alarm management due to the interrelationships between variables. The technique of capturing and visualizing the correlation information, especially from historical alarm data directly, is beneficial for further analysis. In this paper, the Gaussian kernel method is applied to generate pseudo continuous time series from the original binary alarm data. This can reduce the influence of missed, false, and chattering alarms. By taking into account time lags between alarm variables, a correlation color map of the transformed or pseudo data is used to show clusters of correlated variables with the alarm tags reordered to better group the correlated alarms. Thereafter correlation and redundancy information can be easily found and used to improve the alarm settings; and statistical methods such as singular value decomposition techniques can be applied within each cluster to help design multivariate alarm strategies. Industrial case studies are given to illustrate the practicality and efficacy of the proposed method. This improved method is shown to be better than the alarm similarity color map when applied in the analysis of industrial alarm data.

Similarity Analysis of Industrial Alarm Flood Data

Abstract: Flooding of alarms is a very crucial problem in process industries. An alarm flood makes an operator ineffective of taking necessary actions, and often risking an emergency shutdown or a major upset. In this work, the flooding of alarms is discussed based on the standards presented in ISA 18.2. A new analysis method is proposed to investigate similar alarm floods from the historic alarm data and group them on the basis of the patterns of alarm occurrences. A case study on real industrial alarm data is also presented to demonstrate the utility of the proposed analysis.