Fault template extraction to assist operators during industrial alarm floods

Charbonnier, Sylvie; Bouchair, Nabil; Gayet, Philippe

doi:10.1016/j.engappai.2015.12.007

Cited by 52 publications

(10 citation statements)

References 30 publications

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“…Data-driven methods directly analyse, manage and reduce the alarm annunciation and therefore flooding [5], without a semantic representation of the system. Multiple approaches exist to this end, drawing from the data mining fields such as sequence identification and pattern recognition [6], [7], correlation analysis [8] or visualisation [9]. Many of these approaches utilise flood similarity measure of some kind, e.g., [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Defining and validating similarity measures for industrial alarm flood analysis

Fullen¹,

Schüller

Niggemann³

2017

2017 IEEE 15th International Conference on Industrial Informatics (INDIN)

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Industrial plant operators regularly observe a high number of alarms generated in a short period of time, a phenomenon which is referred to as alarm flooding. This causes plant downtime, not only because of the repair time but also by the time needed to identify the root cause of machine failurewhich is difficult during an alarm flood. Therefore, diagnosis tools that perform root cause analysis to advise plant operators can help reduce the downtime, which is a crucial issue in industry. We analyse the reproducibility and applicability of an existing approach by Ahmed et al. (2013) which is based on agglomerative hierarchical clustering where raw data in the form of alarm logs is preprocessed, floods are detected, and then clustered. The aim is, that resulting clusters represent floods that originate from the same common root cause. We extend the approach with alternative similarity measures and perform experiments regarding their effectiveness in structuring industrial alarm flood data. In our evaluation we use a real industrial use case which contains more diverse data and a larger amount of data points compared with the original study.

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Section: Introductionmentioning

confidence: 99%

Defining and validating similarity measures for industrial alarm flood analysis

Fullen¹,

Schüller

Niggemann³

2017

2017 IEEE 15th International Conference on Industrial Informatics (INDIN)

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“…In a related study, patterns are extracted based on the relations between alarms, used for online fault detection. This approach is implemented in [5] as follows. At first, sequences and vector patterns of faults are extracted; then, these patterns are combined to form a sequential weighted fault pattern and are used as a detection criterion.…”

Section: Introductionmentioning

confidence: 99%

Alarm-Based Root Cause Analysis in Industrial Processes Using Deep Learning

Javanbakht¹,

Neshastegaran²,

Izadi³

2022

Preprint

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Alarm management systems have become indispensable in modern industry. Alarms inform the operator of abnormal situations, particularly in the case of equipment failures. Due to the interconnections between various parts of the system, each fault can affect other sections of the system operating normally. As a result, the fault propagates through faultless devices, increasing the number of alarms. Hence, the timely detection of the major fault that triggered the alarm by the operator can prevent the following consequences. However, due to the complexity of the system, it is often impossible to find precise relations between the underlying fault and the alarms. As a result, the operator needs support to make an appropriate decision immediately. Modeling alarms based on the historical alarm data can assist the operator in determining the root cause of the alarm. This research aims to model the relations between industrial alarms using historical alarm data in the database. Firstly, alarm data is collected, and alarm tags are sequenced. Then, these sequences are converted to numerical vectors using word embedding. Next, a self-attention-based BiLSTM-CNN classifier is used to learn the structure and relevance between historical alarm data. After training the model, this model is used for online fault detection. Finally, as a case study, the proposed model is implemented in the well-known Tennessee Eastman process, and the results are presented.

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“…The online alarm sequence was matched with these patterns. A method to extract a fault template from a set of alarm lists, raised on the occurrence of several faults, was proposed in [19,20]. The fault template could be used to extract relevant information on the alarm system and by operators as a guideline for fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Fault Diagnosis by the Approach of SCADA Alarms Analysis

et al. 2021

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Wind farm operators are overwhelmed by a large amount of supervisory control and data acquisition (SCADA) alarms when faults occur. This paper presents an online root fault identification method for SCADA alarms to assist operators in wind turbine fault diagnosis. The proposed method is based on the similarity analysis between an unknown alarm vector and the feature vectors of known faults. The alarm vector is obtained from segmented alarm lists, which are filtered and simplified. The feature vector, which is a unique signature representing the occurrence of a fault, is extracted from the alarm lists belonging to the same fault. To mine the coupling correspondence between alarms and faults, we define the weights of the alarms in each fault. The similarities is measured by the weighted Euclidean distance and the weighted Hamming distance, respectively. One year of SCADA alarms and maintenance records are used to verify the proposed method. The results show that the performance of the weighted Hamming distance is better than that of the weighted Euclidean distance; 84.1% of alarm lists are labeled with the right root fault.

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Fault template extraction to assist operators during industrial alarm floods

Cited by 52 publications

References 30 publications

Defining and validating similarity measures for industrial alarm flood analysis

Defining and validating similarity measures for industrial alarm flood analysis

Alarm-Based Root Cause Analysis in Industrial Processes Using Deep Learning

Wind Turbine Fault Diagnosis by the Approach of SCADA Alarms Analysis

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