Alarm Floods Research Articles

Improved similarity analysis of industrial alarm flood sequences by considering alarm correlations

Alarm floods are leading issues that compromise the efficiency of industrial alarm systems and are identified as major causes of many industrial accidents. As an advanced technique to handle alarm floods, sequence alignment based similarity analysis has been developed to match alarm flood sequences, and thus can help with further root cause identification and early warning of alarm floods. However, existing methods based on biological sequence alignment algorithms ignore the relations between alarm occurrences, and thus may cause incorrect matches or mismatches of alarms when comparing two flood sequences. Accordingly, this paper proposes a new alarm flood similarity analysis method based on global vectors and Move–Split–Merge (MSM) distance. The contributions are mainly twofold: (1) An alarm encoding model based on modified global vectors is devised to convert alarm sequences into numerical vectors that reflect the correlations of alarms; (2) a similarity analysis method based on the modified MSM distance is proposed for comparison of encoded alarm flood sequences of unequal lengths. The effectiveness of the proposed method is demonstrated through a case study with a publicly accessible industrial model for Vinyl Acetate Monomer.

Intelligent alarm flood management for power systems

Alarm floods pose a challenge to the successful operation of transmission and distribution systems because the alarm rate is often higher than what operators can effectively manage. Such a high volume of alarms can distract operators from unrelated but relevant alarms that occurred during the flood. To mitigate this situation, we propose a method to group alarm floods into clusters and identify additional alarms that might be lost in the flood and ignored by the operator. To do so, three different clustering approaches are introduced and tested using historical alarm data from a transmission system operator (TSO). Importantly, the results are validated by the end user highlighting the applicability of the proposed alarm flood management tool in real world scenarios.

Root Cause Identification of Industrial Alarm Floods Using Word Embedding and Few-Shot Learning

Alarm systems are commonly deployed in modern industrial facilities to monitor process operations. However, due to the presence of nuisance alarms and alarm floods, their efficiencies are much degraded. Especially, alarm floods are among the most difficult issues in industrial alarm management and recognised as the main causes of many industrial accidents. To address alarm floods, this paper proposes a Root Cause Identification method (RCI) for industrial alarm floods based on word embedding and few-shot learning. The contributions are threefold: 1) A textual encoding method based on word embedding is proposed to convert alarm messages into numerical word vectors that can be used in the modeling of RCI; 2) an alarm priority based adaptive weighting strategy is designed to make the RCI more sensitive to alarms of higher priorities and appearing earlier; 3) a few-shot learning method based on the long short-term memory is adapted to identify root causes of alarm floods based on limited instances of labeled data. The effectiveness and superiority of the proposed method are demonstrated by a case study based on data from the Vinyl Acetate Monomer (VAM) public model.

Structure Feature Extraction for Hierarchical Alarm Flood Classification and Alarm Prediction

Structure Feature Extraction for Hierarchical Alarm Flood Classification and Alarm Prediction

Diagnosing root causes of faults based on alarm flood classification using transfer entropy and multi-sensor fusion approaches

Process abnormalities can result in serious accidents that may lead to unexpected loss of life and property. Early fault detection and diagnosis are essential to prevent these accidents. In the efficient operation of industrial systems, alarm systems play a crucial role. Developing new sensors and alarm adjustment networks has increased the possibility of generating multiple alarms. The alarm flood is the most important type of this problem. In this study, a new algorithm is proposed to diagnose the root cause of the fault through the classification of alarm floods. The main novelty of this algorithm is to use transfer entropy as a criterion to detect the similarity between the alarm flood sequences. The other innovations include calculating transfer entropy between the process variable and the alarm data, multi-sensor information fusion for large-scale plants and simultaneous alarms, and proposing an online version of the algorithm for the early prediction of the type of fault occurring. Finally, the Tennessee Eastman Process system, as a simulator, and the Saveh rotary cement kiln, as a real industrial system, are applied to evaluate the proposed method.

Mining Sequential Alarm Pattern Based on the Incremental Causality PrefixSpan Algorithm

Alarm flood is the main reason for the alarm system failure in the modern industry, so suppressing the subsequent alarms caused by anomalous propagation during the alarm flooding is an important issue. An incremental causality prefixSpan malgorithm is proposed to mine the frequent and sequence alarm patterns from the alarm flood, and dynamically update the old alarm mode from the new database. First, the alarm event is re-defined and the detection of alarm flood is formulated. The frequent alarm pattern with causality is expressed with the orderly and time delay features of sequential alarms. Under the new definition of alarm pattern, causality prefixSpan algorithm is proposed by introducing the time constraints, i.e., the causality. Furthermore, a new incremental mining strategy is co-implemented on the improved algorithm. When new alarm flooding data occurs, the old alarm modes are quickly updated without re-scanning the entire changing database. The incremental strategy makes the proposed method easily implemented in the real operation. The effectiveness of the proposed method is verified with the synthetic data sets and Tennessee-Sterman (TE) process.

MFM-based alarm root-cause analysis and ranking for nuclear power plants

Alarm flood due to abnormality propagation is the most difficult alarm overloading problem in nuclear power plants (NPPs). Root-cause analysis is suggested to help operators in understand emergency events and plant status. Multilevel Flow Modeling (MFM) has been extensively applied in alarm management by virtue of the capability of explaining causal dependencies among alarms. However, there has never been a technique that can identify the actual root cause for complex alarm situations. This paper presents an automated root-cause analysis system based on MFM. The causal reasoning algorithm is first applied to identify several possible root causes that can lead to massive alarms. A novel root-cause ranking algorithm can subsequently be used to isolate the most likely faults from the other root-cause candidates. The proposed method is validated on a pressurized water reactor (PWR) simulator at HAMMLAB. The results show that the actual root cause is accurately identified for every tested operating scenario. The automation of root-cause identification and ranking affords the opportunity of real-time alarm analysis. It is believed that the study can further improve the situation awareness of operators in the alarm flooding situation.

A priority-aware sequential pattern mining method for detection of compact patterns from alarm floods

Alarm floods are commonly present in modern complex industrial process, and usually degrade the performance of the alarm systems severely. In an alarm flood, the massive alarm messages may overwhelm plant operators and submerge critical alarms, which would further worsen the situation and lead to severe consequences. One effective way to deal with alarm flood situations is to mine meaningful alarm patterns that can be used to configure dynamic alarm suppression modules that suppress irrelevant alarms in alarm floods based on certain conditions or to help with online alarm prediction. Using the existing methods in literature, alarms of higher priorities could be easily overlooked in the pattern mining as they usually appear less frequent; thus, it makes the results become unreliable. Motivated by this issue, this paper proposes a new pattern mining method to extract priority-aware compact alarm patterns from historical alarm flood sequences. The main contributions of this study are twofold: (1) a priority-aware sequential pattern mining algorithm is designed to mine alarm sequential patterns using a closed sequential pattern mining algorithm with alarm priority information incorporated; (2) a pattern compression strategy is proposed to merge similar sequences into compact patterns, making it easier to search for alarm patterns. The effectiveness of the proposed pattern mining method is demonstrated by alarm data generated using a vinyl acetate monomer chemical plant model.

An association rule mining approach to predict alarm events in industrial alarm floods

Industrial operators often experience overwhelming situations during ongoing alarm floods due to high alarm rates. In such situations, real-time assistance in the form of prediction of upcoming alarm events can ease off the decision-making for industrial operators. Accordingly, this work studies alarm prediction in alarm flood situations, and the main contribution lies in a novel association rule mining approach for real-time prediction of alarm events and their corresponding times of annunciation during an ongoing alarm flood. The proposed method is capable of performing predictions at the triggering instant and modifying the predictions with the increasing of the ongoing alarm flood. The proposed method is implemented mainly in the following steps: (1) A Compact Prediction Tree (CPT) model is modified with new features, namely, the time table and co-occurrence matrix, and constructed based on historical alarm sequences; (2) an alarm relevancy detection strategy is designed to detect and eliminate irrelevant alarms in alarm floods; (3) an online alarm prediction algorithm is designed to predict upcoming alarms at the early stage of the ongoing alarm flood; (4) the confidence intervals of the time differences between the annunciations of subsequent predicted alarm events are calculated for time prediction. To demonstrate the effectiveness of the proposed method, an industrial case study based on real alarm & event logs from an oil refinery is provided.

Frequent Alarm Pattern Mining of Industrial Alarm Flood Sequences by an Improved PrefixSpan Algorithm

Alarm systems are essential to the process safety and efficiency of complex industrial facilities. However, with the increasing size of plants and the growing complexity of industrial processes, alarm flooding is becoming a serious problem and posing challenges to alarm systems. Extracting alarm patterns from an alarm flood database can assist with an alarm root cause analysis, decision support, and the configuration of an alarm suppression model. However, due to the large size of the alarm database and the problem of sequence ambiguity in the alarm sequence, existing algorithms suffer from excessive computational overhead, incomplete alarm patterns, and redundant outputs. In order to solve these problems, we propose an alarm pattern extraction method based on the improved PrefixSpan algorithm. Firstly, a priority-based pre-matching strategy is proposed to cluster similar sequences in advance. Secondly, we improved PrefixSpan by considering timestamps to tolerate short-term order ambiguity in alarm flood sequences. Thirdly, an alarm pattern compression method is proposed for the further distillation of pattern information in order to output representative alarm patterns. Finally, we evaluated the effectiveness and applicability of the proposed method by using an alarm flood database from a real diesel hydrogenation unit.

Pattern Matching of Industrial Alarm Floods Using Word Embedding and Dynamic Time Warping

Dear Editor, This letter proposes a new pattern matching method based on word embedding and dynamic time warping (DTW) to identify groups of similar alarm floods. First, alarm messages are transformed into numeric values that represent alarms and also reflect the relationships between alarm occurrences. Then, similarities between numerically encoded alarm flood sequences are calculated by DTW and groups of similar floods are identified via clustering. The effectiveness of the proposed method is demonstrated by a case study with alarm & event data obtained from a public industrial simulation model.

Leveraging semantic-based Root Cause Analysis with Alarm Flood Reduction

Today's demands on manufacturing companies, which include high production volume and quality, imply that machine downtime must be reduced as much as possible. However, since mechanical failures in complex industrial plants can never be completely avoided, it is important to support operators in finding the root cause of the failure as quickly as possible in order to restore the machine to a productive state. This can be done using root cause analysis systems. However, current approaches need to process large amounts of data to deliver a result and are difficult to adapt to different machines. This paper solves these problems by combining root cause analysis with alarm flood reduction to reduce the amount of data to be processed. It also presents a semantic model to formulate the relationships between alarm floods and root causes. In addition, a concept that ensures adaptability to different machines is presented.

Alarm fatigue: Can Indian intensive care unit nurses keep up with the noise and maintain their edge?

ABSTRACT Alarm fatigue has become a significant concern for intensive care unit (ICU) nurses. The incessant flood of alarms can result in desensitization, negatively affecting both nurses and consequently, patient care. This article delves into the struggles ICU nurses encounter due to alarm fatigue, its repercussions, and potential strategies to tackle the problem. By comprehending and addressing alarm fatigue, healthcare professionals can foster a safer and more efficient work environment for ICU nurses.

Pulse Bag Filter Fault Classifier Research and Analysis

Aiming at the problem of fault alarm flooding, a fault classifier is established to automatically classify fault types in combination with unsupervised learning methods in machine learning. The k-means method is used to classify the alarm types in dimensionality reduction, and the optimal K value is selected as 11 through various evaluation indexes, and the faults are added to the new category labels according to the center of each cluster after clustering. The random forest and support vector classification algorithm is used to establish a fault classifier model on the fault alarm dataset with new labels, and the unified analysis of the fault type prediction results of the test set unlabeled fault data set is compared with the random forest algorithm, support vector classification, and k-means method. The results show that the unity rate of the prediction results of the three types of classifiers on the test set when taking different K values can reach up to 67%, and the uniformity rate of the prediction results of the three models exceeds 0.998, which has a high degree of uniformity, indicating that the establishment of the fault alarm classifier for the pulse bag dust collector is feasible and has good classification accuracy, which can help maintenance personnel quickly locate and troubleshoot faults, and improve the intelligence level of equipment.

Performance analysis of up/down counters in alarm design

Alarm systems, as a layer of protection, play a critical role in process safety. Modern alarm systems, however, pose a challenge in the process industry due to alarm floods, missed alarms, and a large number of false, nuisance, and chattering alarms. In this paper, we propose up/down counter configurations as a technique to improve the quality of univariate alarm systems. Mathematical models are developed based on the Markov chain to study the performance of up/down counters and analytical expressions for performance indices (the false alarm rate, missed alarm rate, and expected detection delay) are derived. The results are confirmed using Monte Carlo simulation and the utility of the proposed technique is demonstrated through examples. This proposed up/down counter is specifically compared to delay-timers as a common and well-studied alarm configuration technique.

PER4Mance Prototyping environment for research on human-machine interactions for alarm floods management: the case study of a chemical plant process control

Alarm floods are dangerous because the quantity of alarms triggered is too numerous for operators to reliably implement the right corrective action. Process operators of complex systems, such as chemical plants or nuclear power production, are faced with alarm management systems that can be better built in consideration of human capabilities and limitations. Developing human-machine interfaces (HMIs) that better support operators is critical for ensuring the safe and reliable operation of critical systems and processes. The research team has developed an accessible and adaptable prototyping environment dedicated for research on alarm management and human-machine interactions in the process industry. The method used was to build on the Tennessee Eastman Process (TEP) simulator and incorporate Human-Machine design guidelines. The results are an open-sourced prototyping environment that incorporates data from a real chemical plant and integrates true alarm data and thresholds. At the end of this article, we share the Github link to the entire MATLAB, Simulink and App Designer files of PER4Mance: a prototyping environment for research on human-machine interactions for alarm flood management.

Chemical Process Alarm Root Cause Diagnosis Method Based on the Combination of Data-Knowledge-Driven Method and Time Retrospective Reasoning.

Due to the abrupt nature of the chemical process, a large number of alarms are often generated at the same time. As a result of the flood of alarms, it largely hinders the operator from making accurate judgments and correct actions for the root cause of the alarm. The existing diagnosis methods for the root cause of alarms are relatively single, and their ability to accurately find out complex accident chains and assist decision making is weak. This paper introduces a method that integrates the knowledge-driven method and the data-driven method to establish an alarm causal network model and then traces the source to realize the alarm root cause diagnosis, and develops the related system modules. The knowledge-driven method uses the hidden causality in the optimized hazard and operability analysis (HAZOP) report, while the data-driven method combines the autoregressive integrated moving average model (ARIMA) and Granger causality test, and the traceability mechanism uses the time-based retrospective reasoning method. In the case study, the practical application of the method is compared with the experimental application in a real petrochemical plant. The results show that this method helps to improve the accuracy of correct diagnosis of the root cause of the alarm and can assist the operators in decision making. Using this method, the root cause diagnosis of alarm can be realized quickly and scientifically, and the probability of misjudgment by operators can be reduced, which has a certain degree of scientificity.

The development of the alarm system for HLS-II

Hefei Light Source II (HLS-II) is a vacuum ultraviolet synchrotron light source. The control system of HLS-II is a distributed system based on the experimental physics and industrial control system (EPICS). The alarm system of HLS-II is responsible for monitoring the alarm state of the facility and distributing the alarm message in time. It facilitates the operator to troubleshoot problem efficiently, so as to improve the availability of HLS-II. Phoebus/Alarms (Alarms) is the latest alarm software released in EPICS community. Based on Alarms, the alarm system of HLS-II is designed, developed and deployed. The alarm distribution strategy is designed from the perspective of the HLS-II operation mode, alarm severity and alarm representative to overcome alarm floods. The 3 ways to distribute the alarm message are added to the HLS-II alarm system, which include WeChat, SMS and web-based GUI. In order to configure alarm options efficiently and conveniently, an alarm configuration management tool is developed based on the MySQL database and Browser/Server architecture. The alarm system of HLS-II has been deployed since November 2021. The result indicates that the alarm system is effective and convenient to operate.

Early Classification of Industrial Alarm Floods Based on Semisupervised Learning

Early classification of ongoing alarm floods in industrial monitoring systems is crucial to provide a safe and efficient operation. It can provide online decision support for plant operators to take timely action, without waiting for the end of an alarm flood. In this article, a data-driven approach is proposed to address the early classification problem with unlabeled historical data. To prioritize earlier activated alarms and take advantage of the triggering time information of alarms, a vector representation called exponentially attenuated component (EAC) is used to represent alarm floods. This makes alarm sequences fit for different powerful machine learning algorithms, which can be easily implemented online with acceptable computational complexities. A method based on the time information of unlabeled historical alarm floods is formulated to determine the attenuation coefficient for EAC representation. With the Gaussian mixture model, an efficient semisupervised approach is proposed to provide an early classification of alarm floods using unlabeled historical data. It includes two phases: offline clustering and online classification, where the clustering step is automated in terms of choosing the optimal number of clusters by applying an efficient cluster validity index. The efficiency of the proposed method is validated by the Tennessee Eastman process benchmark and a real industrial dataset.

Attention Limitations in the Detection and Identification of Alarms in Close Temporal Proximity.

The aim of this study was to establish the effects of simultaneous and asynchronous masking on the detection and identification of visual and auditory alarms in close temporal proximity. In complex and highly coupled systems, malfunctions can trigger numerous alarms within a short period of time. During such alarm floods, operators may fail to detect and identify alarms due to asynchronous and simultaneous masking. To date, the effects of masking on detection and identification have been studied almost exclusively for two alarms during single-task performance. This research examines 1) how masking affects alarm detection and identification in multitask environments and 2) whether those effects increase as a function of the number of alarms. Two experiments were conducted using a simulation of a drone-based package delivery service. Participants were required to ensure package delivery and respond to visual and auditory alarms associated with eight drones. The alarms were presented at various stimulus onset asynchronies (SOAs). The dependent measures included alarm detection rate, identification accuracy, and response time. Masking was observed intramodally and cross-modally for visual and auditory alarms. The SOAs at which asynchronous masking occurred were longer than reported in basic research on masking. The effects of asynchronous and, even more so, simultaneous masking became stronger as the number of alarms increased. Masking can lead to breakdowns in the detection and identification of alarms in close temporal proximity in complex data-rich domains. The findings from this research provide guidance for the design of alarm systems.