Quantification of alarm chatter based on run length distributions

Kondaveeti, Sandeep R.; Izadi, Iman; Shah, Sirish L.; Shook, David S.; Kadali, Ramesh; Chen, Tongwen

doi:10.1016/j.cherd.2013.02.028

Cited by 54 publications

(22 citation statements)

References 7 publications

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“…Alarm floods are composed of a series of chronologically sorted alarms, each of which generally includes a variety of attributes, such as the tag name, alarm identifier, time stamp, alarm priority, and process description (Kondaveeti et al, 2013). The tag name is the label of a process variable (including both analog and digital variables) associated with an alarm; the alarm identifier describes the alarm type, e.g.…”

Section: Representations Of Alarm Floodsmentioning

confidence: 99%

“…Hence, a prerequisite step for alarm flood analysis is to remove chattering alarms. The detection and removal of chattering alarms have received increasing attentions recently (Kondaveeti et al, 2013;Naghoosi et al, 2011;Wang & Chen, 2013; in particular, the m-sample delay timer with m equal to 20, as recommended by Rule 3a in Wang and Chen (2014), is applied to remove chattering alarms in the sequel.…”

Section: Representations Of Alarm Floodsmentioning

confidence: 99%

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A local alignment approach to similarity analysis of industrial alarm flood sequences

Wang

Chen

2016

Control Engineering Practice

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Section: Representations Of Alarm Floodsmentioning

confidence: 99%

Section: Representations Of Alarm Floodsmentioning

confidence: 99%

A local alignment approach to similarity analysis of industrial alarm flood sequences

Wang

Chen

2016

Control Engineering Practice

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“…On the other hand, electrical signals in neuroscience are usually clean, because they just transmit the instructions between neurons; while there are often some false alarms and missed alarms in industrial processes when using the thresholds to generate the alarms due to the existence of noise [26], just as shown in Figure 1. As a result, false alarms and missed alarms will bring some wrong information into the alarm data, which in other words will reduce the amount of information that can be obtained from the data.…”

Section: Alarm Series and Data Collectionmentioning

confidence: 99%

Detection of Causality between Process Variables Based on Industrial Alarm Data Using Transfer Entropy

Yang

2015

Entropy

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In modern industrial processes, it is easier and less expensive to configure alarms by software settings rather than by wiring, which causes the rapid growth of the number of alarms. Moreover, because there exist complex interactions, in particular the causal relationship among different parts in the process, a fault may propagate along propagation pathways once an abnormal situation occurs, which brings great difficulty to operators to identify its root cause immediately and to take proper actions correctly. Therefore, causality detection becomes a very important problem in the context of multivariate alarm analysis and design. Transfer entropy has become an effective and widely-used method to detect causality between different continuous process variables in both linear and nonlinear situations in recent years. However, such conventional methods to detect causality based on transfer entropy are computationally costly. Alternatively, using binary alarm series can be more computational-friendly and more direct because alarm data analysis is straightforward for alarm management in practice. The methodology and implementation issues are discussed in this paper. Illustrated by several case studies, including both numerical cases and simulated industrial cases, the proposed method is demonstrated to be suitable for industrial situations contaminated by noise.

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“…Usually a large proportion of an alarm flood are nuisance alarms, of which chattering alarms form an important part. Methods such as high density alarm plots, calculation of a chattering index, delay-timers, and dead-bands can be used to visualize, quantify, and reduce such chattering alarms (Kondaveeti et al, 2010(Kondaveeti et al, , 2013Izadi et al, 2009). However, by applying delay-timers and dead-bands, often one cannot totally suppresses alarms during alarm floods; the remaining alarms are mainly consequence alarms, which can be caused by three reasons: (1) process state changes such as start-up and shutdown, (2) bad alarm configurations such as redundant measurements on a single process and (3) causal relationships among measured variables.…”

Section: Introductionmentioning

confidence: 99%