Alarm Patterns Research Articles

Enhanced sensitivity and broadband response in porous triple periodic minimal surface piezoresistive sensors for telemedicine applications

Wearable pressure sensors with outstanding performance have garnered significant attention in fields such as telemedicine. However, the development of sensors that achieve both adjustable high sensitivity and a wide response range remains a formidable challenge. To address these issues, we propose the development of a flexible piezoresistive sensor featuring simultaneous macro- and microstructures. This is achieved by constructing a complex macro-topological skeleton using Fused Deposition Molding (FDM) technology and integrating supercritical carbon dioxide (ScCO2) foaming technology for microscopic pore creation. The unique conductive network structure, coupled with the Triple Periodic Minimal Surface (TPMS) metamaterial framework and microscopic pore architecture, endows the piezoresistive sensors with exceptional sensing performance, including high sensitivity (12.13 MPa−1), a broad response range (exceeding 30 MPa), and long-term fatigue resistance (over 26,000 s cycles). In addition, the sensors are shielded from electromagnetic interference, with a maximum EMI shielding effectiveness of 53.2 dB for the TMFM with a thickness of 2.3 mm. The effects of varying porosities and foaming pressures on sensitivity were analyzed using finite element simulations. Owing to these remarkable features, multiple foot movement recognition was successfully demonstrated with volunteer participants by assembling sensing unit array modules. Based on this, an intelligent fall warning recognition system is developed using machine learning. This system accurately distinguishes four types of activity behaviors—standing, walking, spraining foot, and falling—with accuracy rates of 98.9 %, 97.1 %, 98.9 %, and 95.1 %, respectively. The system automatically generates an alarm pattern for detected fall behaviors. These findings suggest that the combination of TPMS skeleton and micropores structure offers a promising strategy for developing sensors with adjustable sensitivity and wide detection range for wearable devices. This technology holds significant potential for applications in telemedicine assistance, particularly for the elderly and other vulnerable populations.

Alarm Management in Provisional COVID-19 Intensive Care Units: Retrospective Analysis and Recommendations for Future Pandemics.

In response to the high patient admission rates during the COVID-19 pandemic, provisional intensive care units (ICUs) were set up, equipped with temporary monitoring and alarm systems. We sought to find out whether the provisional ICU setting led to a higher alarm burden and more staff with alarm fatigue. We aimed to compare alarm situations between provisional COVID-19 ICUs and non-COVID-19 ICUs during the second COVID-19 wave in Berlin, Germany. The study focused on measuring alarms per bed per day, identifying medical devices with higher alarm frequencies in COVID-19 settings, evaluating the median duration of alarms in both types of ICUs, and assessing the level of alarm fatigue experienced by health care staff. Our approach involved a comparative analysis of alarm data from 2 provisional COVID-19 ICUs and 2 standard non-COVID-19 ICUs. Through interviews with medical experts, we formulated hypotheses about potential differences in alarm load, alarm duration, alarm types, and staff alarm fatigue between the 2 ICU types. We analyzed alarm log data from the patient monitoring systems of all 4 ICUs to inferentially assess the differences. In addition, we assessed staff alarm fatigue with a questionnaire, aiming to comprehensively understand the impact of the alarm situation on health care personnel. COVID-19 ICUs had significantly more alarms per bed per day than non-COVID-19 ICUs (P<.001), and the majority of the staff lacked experience with the alarm system. The overall median alarm duration was similar in both ICU types. We found no COVID-19-specific alarm patterns. The alarm fatigue questionnaire results suggest that staff in both types of ICUs experienced alarm fatigue. However, physicians and nurses who were working in COVID-19 ICUs reported a significantly higher level of alarm fatigue (P=.04). Staff in COVID-19 ICUs were exposed to a higher alarm load, and the majority lacked experience with alarm management and the alarm system. We recommend training and educating ICU staff in alarm management, emphasizing the importance of alarm management training as part of the preparations for future pandemics. However, the limitations of our study design and the specific pandemic conditions warrant further studies to confirm these findings and to explore effective alarm management strategies in different ICU settings.

Memory representations are flexibly adapted to orthographic systems: A comparison of English and Hebrew

Across languages, speech unfolds in the same temporal order, constrained by the forward flow of time. But the way phonology is spatially mapped onto orthography is language-specific, ranging from left-to-right, right-to-left, and top-to-bottom, among others. While the direction of writing systems influences how known words are visually processed, it is unclear whether it influences learning and memory for novel orthographic regularities. The present study tested English and Hebrew speakers on an orthographic word-referent mapping task in their native orthographies (written left-to-right and right-to-left, respectively), where the onsets and offsets of words were equally informative cues to word identity. While all individuals learned orthographic word-referent mappings significantly above chance, the parts of the word that were most strongly represented varied. English monolinguals false alarmed most to competing foils that began with the same bigram as the target, representing word onsets most strongly. However, Hebrew bilinguals trained on their native orthography showed no difference between false alarm rates to onset and offset competitors, representing the beginning and ends of words equally strongly. Importantly, Hebrew bilinguals tested on English words displayed a more English-like false alarm pattern (although not a full switch), suggesting that memory biases adapt to the opposite directionality of encountered text while retaining traces of native language biases. These findings demonstrate that experience with different writing systems influences how individuals represent novel orthographic words, starting in the earliest stages of learning.

Pre- and Post-debridement Wound Cultures in Gustilo Type III Open Tibial Fractures to Predict Wound Infection at a Tertiary Care Hospital.

Gustilo type III open tibial fractures are difficult injuries that carry a higher risk of infection and other consequences. Open-fracture wound microbiology is dynamic and responsible for change over time. Effective antibiotic treatment plans are required, as detrimental microorganisms are often linked to these types of lesions. The study aimed to determine whether pre- and post-debridement wound cultures could predict wound infection in Gustilo type III open tibial fractures. This prospective study was carried out at the National Institute of Traumatology and Orthopaedic Rehabilitation (NITOR) in Dhaka, Bangladesh, on 344 patients who presented to the emergency department with a Gustilo type III open tibial fracture within 24 hours of injury from June 2018 to October 2019. Three successive cultures were carried out: one in the emergency room (surveillance culture), the second at the emergency theater after debridement, and the third in the ward after one week (seven to 10 days). Statistical analyses of the results were conducted using Microsoft Excel (Microsoft Corp., Redmond, WA) and IBM SPSS Statistics for Windows, version 27 (IBM Corp., Armonk, NY). The study included 344 patients with an average age of 37.15 years, with motor vehicle accidents being the primary cause (78.2%). Gustilo type IIIA fractures made up the majority (48.5%), followed by type IIIB fractures (44.8%). A significant reduction in contamination rates was observed from initial admission (48.8%) to post-debridement (36.6%) (p =.001). There was a significant positive correlation between pre-debridement cultures and wound infections (r =.311), as well as between post-debridement cultures and wound infections. The infection rate increased to 61.6% in ward samples, indicating a high rate of hospital-acquired infections. Pseudomonas and Klebsiella species were the most prevalent multidrug-resistant bacteria that caused these infections. The present study provides information on the relationship between contamination and infection. Gram-negative pathogens were dominant in this study, and the results of the antibiograms showed an alarming pattern of resistance. Nosocomial infection demands further urgent study.

Geospatial mapping of public sentiment and infodemic on human papillomavirus vaccination in India: An indication to formulation of strategies for effective implementation

ABSTRACT The implementation of Human Papillomavirus (HPV) vaccination is crucial for eliminating cervical cancer in India. The infodemic, characterised by misleading information, could hinder the successful implementation of the initiative. Misinformation related to the HPV vaccine, such as rumours, has been reported and circulated, contributing to an alarming pattern of vaccine hesitancy observed on social media. This study aimed to identify the public sentiment towards HPV vaccination based on the ‘Behavioral and Social Drivers (BeSD)’ framework through geospatial, content and sentiment analysis. A total of 1,487 tweets were extracted. After preprocessing, 1010 tweets were identified for sentiment and content analysis. The sentiments expressed towards the HPV vaccine are mixed, with a generally positive outlook on the vaccines. Within the population, there is a pervasive proliferation of misinformation, primarily focusing on vaccine safety and efficacy, contentious subjects, ethical considerations, and a prevalent sense of uncertainty in selecting the appropriate vaccine. These observations are crucial for developing targeted strategies to address public concerns and enhance vaccination rates. The insights gained from these results will guide policymakers, healthcare practitioners, and public health organisations to implement evidence-based interventions, thereby countering vaccine hesitancy and improving public health outcomes.

Impacts of meteorological conditions on the plummeting surface-reaching solar radiation over a sub-tropical station – Pune, India

The unprecedented rise of solar energy as a dominant power source is a remarkable feat, considering our historical struggle to establish its widespread adoption. However, the dynamics of solar radiation reaching the Earth's surface are greatly influenced by meteorological conditions such as clouds, aerosols, and wind speed. This study investigates the detailed changes in surface-reaching solar radiation within the subtropical region Pune from 1993 to 2022. By employing analytical methodologies including linear regression, wavelet coherence, and quantitative analysis, our study reveals a compelling and alarming pattern of plummeting solar radiation over Pune (also known as solar dimming). The total drop is ∼1.04 Wm−2 year−1 (0.21 % year−1), with a more steep decline registered from 2018 to 2022, providing an astonishing rate of ∼6.01 Wm−2 year−1 (1.21 % year−1). Moreover, wavelet coherence transform revealed an antiphase coherence between solar radiation and both clouds and aerosols, suggesting their substantial influence over a longer timeframe. Extensive analysis utilizing observational data is conducted to find the contributing elements to the significant decline during 2018 to June 2022. Analysis of solar radiation statistics showed that clouds led to a substantial reduction of ∼30.18 % compared to conditions with clear-skies. Similarly, the aerosols have a comparable effect, reducing radiation by ∼17.75 % compared to days characterized by low or negligible aerosol loading for the same period. Furthermore, a through statistical analysis investigates a notable anticorrelation (ρ = −0.64) between wind speed and solar radiation. These findings underscore the imperative of incorporating climatic variables while studying solar dimming trends and devising sustainable renewable energy strategies to ensure the long-term viability of solar energy source.

Spatiotemporal Assessment of Surface Solar Dimming in India: Impacts of Multi-Level Clouds and Atmospheric Aerosols

Surface solar radiation (SSR) is a fundamental energy source for an equitable and sustainable future. Meteorology-induced variability increases uncertainty in SSR, thereby limiting its reliability due to its intermittent nature. This variability depends on several meteorological factors, including clouds, atmospheric gases, and aerosol concentrations. This research investigates the detailed impact of different levels of clouds and aerosols on SSR across India. Utilizing satellite data with reanalysis retrievals, the research covers a span of three decades (30 years), from 1993 to 2022. Aerosols contributed to an average attenuation of ~13.33% on SSR, while high, mid, and low cloud conditions showed much stronger impacts, with an attenuation of ~30.80%, ~40.10%, and ~44.30%, respectively. This study reveals an alarming pattern of increasing cloud impact (Cimpact) on SSR in the recent decade, with a significant increasing rate of ~0.22% year−1 for high cloud (HCimpact) and ~0.13% year−1 for mid cloud (MCimpact) impact, while low cloud impact (LCimpact) showed minimal change. The trend of aerosol impact (Aimpact) also showed an average increase of ~0.14% year−1 across all regions. The findings underscore the imperative of considering climatic variables while studying the growing solar dimming. Our findings also will assist policymakers and planners in better evaluating the solar energy resources across India.

Alarm Webs: A Framework for Decoding RAN Alarm Dynamics

This paper introduces “Alarm Webs”, a pioneering framework designed to decode the dynamics of Radio Access Network (RAN) alarms, utilizing a novel graph-theoretic approach. Leveraging directed graphs (digraphs), this work uniquely encodes RAN alarm data, with each alarm represented as a node and its severity indicated by distinct colors. The edges of these graphs signify the sequence of alarms, creating a directional flow from one alarm to the next. Utilizing a comprehensive dataset from a major telecommunications service provider in the UK, encompassing alarms from 4G and 5G networks across over 7,000 base stations, the framework ofers an unprecedented analysis of alarm patterns and interactions. The proposed methodology involves the calculation of in-degrees and out-degrees for each node, identifying those with in-degrees higher than four as significant indicators of commonly resulting alarms. This threshold-based selection is instrumental in isolating the most impactful alarms. Additionally, the application of Sankey diagrams brings an intuitive visualization of alarm causality, based on the in-degree centrality of the nodes. This approach not only clarifies the complex interrelationships among alarms but also aids in identifying potential cascading issues within the network. By effectively mapping alarm sequences and identifying key alarms, this framework paves the way for enhanced network reliability and proactive maintenance strategies in the ever-evolving telecommunications landscape.

Pattern of cephalosporin and carbapenem-resistant Pseudomonas aeruginosa: a retrospective analysis

ObjectivesDespite its financial cost on the world's health care system, Pseudomonas aeruginosa antibiotic resistance has been increasing. Therefore, the goal of this study was to assess the level of antimicrobial resistance to anti-pseudomonas medicines, specifically β-lactam medications such as cephalosporin and carbapenems. In addition, we evaluate the prevalence of multi-drug resistance to P. aeruginosa, particularly during the years of the COVID-19 pandemic. MethodsThis retrospective analysis covered the period from January 2019 to December 2022 and included cephalosporin- and carbapenem-resistant P. aeruginosa isolates. The real-time polymerase chain reaction Genexpert test (CARBA-R kit) was used for the detection of genes responsible for carbapenemase resistance. ResultsDuring the time of the study, 1815 clinical isolates of P. aeruginosa were identified and 160 (9%) were resistant to carbapenems and cephalosporins. The resistance rates were 32.5% (13/597) in 2019, 11.2% (44/393) in 2020, 7% (26/369) in 2021, and 11% (50/456) in 2022. Of those isolates, multidrug-resistant rates were 6.7%, 86.3%, 57.7%, and 56%, per year over the study period. Using Genexpert test, 88 (93.6%) of multidrug-resistant P. aeruginosa were negative for carbapenemase genes. ConclusionThis study emphasizes the alarming patterns of carbapenem and cephalosporin resistance among P. aeruginosa clinical isolates. Furhter surviellance from different centers and different regions is required.

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.

IoT-based data-driven predictive maintenance relying on fuzzy system and artificial neural networks

Industry 4.0 technologies need to plan reactive and Preventive Maintenance (PM) strategies for their production lines. This applied research study aims to employ the Predictive Maintenance (PdM) technology with advanced automation technologies to counter all expected maintenance problems. Moreover, the deep learning based AI is employed to interpret the alarming patterns into real faults by which the system minimizes the human based fault recognition errors. The Sensors Information Modeling (SIM) and the Internet of Things (IoT) have the potential to improve the efficiency of industrial production machines maintenance management. This research work provides a better maintenance strategy by utilizing a data-driven predictive maintenance planning framework based on our proposed SIM and IoT technologies. To verify the feasibility of our approach, the proposed framework is applied practically on a corrugated cardboard production factory in real industrial environment. The Fuzzy Logic System (FLS) is utilized to achieve the AI based PM while the Deep Learning (DL) is applied for the alarming and fault diagnosis in case the fault already occured.

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.

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.

De-implementing intrapartum maternal vitals monitoring to reduce alarm fatigue: an L&D quality and safety initiative

Many L&D units likely overuse continuous and automated maternal physiologic monitors. Given national attention to reducing alarm fatigue in healthcare, which adversely impacts response times and patient outcomes, this study evaluates vital sign monitoring and alarm patterns before and after implementing a vital sign protocol to reduce overmonitoring in low-risk obstetric patients. This is a quality improvement study conducted at an academic, tertiary hospital (4200 deliveries/year). The lack of guidance for maternal vital sign assessment in low-risk patients was identified as a safety challenge. A vital sign protocol was developed with multidisciplinary input and implemented. Total vital signs and alarm rates for all patients delivered during designated calendar days were assessed and compared across baseline, peri-intervention, and follow-up periods. Data were examined in p-type statistical process control charts as well as with time-series analysis. Patient characteristics and severe maternal morbidity (SMM, defined according to the CDC), used as a balancing metric, were compared peri-intervention. 35 individual 24-hour periods of vital sign and alarm volume were evaluated surrounding implementation of the protocol (10 baseline, 20 peri-intervention, 5 delayed follow-up). There was a decrease from a mean of 208.3 to 135.5 vitals/patient after implementation (incidence rate ratio [IRR] 0.65, p < 0.01) and in alarms/patient from a mean of 14.3 to 10.5 (IRR 0.73, p = 0.045) [Figures]. There were no significant differences in patient characteristics aggregated over the 2 calendar months of the immediate pre/post implementation periods (6-7/2021, 8-9/2021), or in SMM with and without transfusion (p = 0.34, 0.45). Introducing a maternal vital sign protocol for low-risk patients on L&D decreased vital signs measured as well as alarms, which may ultimately reduce alarm fatigue. This strategy should be considered on L&D units widely to improve patient safety and optimize outcomes.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

APGNN : Alarm Propagation Graph Neural Network for fault detection and alarm root cause analysis

Telecommunication network plays an important role in our daily life. Fault detection and alarm root cause analysis are the keys to ensure the normal operation of the network. To reduce the burden on operators, numerous methods are employed to analyse root cause of faults. However, there still remain a large amount of non-essential or transient alarms after root cause analysis. A simple Rule-based method may help ease the problems. But it needs prior expert knowledge and the diversity of alarm pattern makes the rules redundant and complicated. Moreover, it cannot accurately cover all true faults and need manual methods as complement. In this work, we propose Alarm Propagation Graph Neural Network(APGNN), a novel data-driven propagation-based root cause analysis and fault detection approach.It first associates alarms and extracts root-derived graph based on Bayesian Network. Then it constructs alarm propagation graphs(APG). We refine the repair orders to obtain actual fault information. At last, Graph Neural Network is used to extract features and learn the mapping from APG to the true fault. Our method not only detects the true fault from large volume of original alarms, but also analyses the root cause alarms. We evaluate our approach both on the offline and online environment of the real-world IP Radio Access Network. Experiments show that our model outperforms the state-of-art approach by 4.6% in F1-score on average.

Alarm Pattern Recognition in Continuous Process Control Systems using Data Mining

An alarm management system with the Human Machine Interface in a process control system is used to alert an operator of any abnormal situation, so that corrective action can be taken to ensure safety and productivity of the plant and quality of the product. An alarm system reporting many alarms even during the normal state of the plant is due to chattering alarms, duplicated alarms, intermittent equipment problems and certain alarms configured in the system which may not have any importance. In such a situation the operator may miss certain critical alarms leading to undesirable outcomes. So, to have an optimum alarm system, the unwanted alarms have to be identified and eliminated. In this paper, we propose an offline method to identify repetitive, frequent sequences or patterns using PrefixSpan and Bi-Directional Extension algorithms. With the identified sequences or patterns, plant operation experts can improve the effectiveness of the alarm system through alarm rationalization so that this will help the operator in making the plant more safe, reliable and productive. The main objectives of this work are the following: (i) to use a definitive method to represent alarm data in an alarm log which is Temporal data as Itemsets without a need for complex mathematical, statistical or visual methods; (ii) to use data mining algorithms for identifying Frequent sequences which can be implemented on a normal computing resource such as Personal computer; (iii) to apply the method to the complete alarm data available no matter how big they are; (iv) to study and establish that the chosen method is possible to be applied to larger sized datasets.

A novelty-based multi-objective evolutionary algorithm for identifying functional dependencies in complex technical infrastructures from alarm data

In this work, a Multi-Objective Evolutionary Algorithm (MOEA) is developed to identify Functional Dependencies (FDEPs) in Complex Technical Infrastructures (CTIs) from alarm data. The objectives of the search are the maximization of a measure of novelty, which drives the exploration of the solution space avoiding to get trapped in local optima, and of a measure of dependency among alarms, which drives the uncovering of functional dependencies. The main contribution of the work is the direct identification of patterns of dependent alarms; this avoids going through the preliminary step of mining association rules, as typically done by state-of-the-art methods which, however, fail to identify rare functional dependencies due to the need of setting a balanced minimum occurrence threshold. The proposed framework for FDEPs identification is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected at the CTI of CERN (European Organization for Nuclear Research). The obtained results show that the framework enables the thorough exploration of the solution space and captures also rare functional dependencies.

(Mis)information, information literacy, and democracy

The current political climate is characterized by an alarming pattern of global democratic regression driven by authoritarian populist leaders who deploy vast misinformation campaigns. These offensives are successful when the majority of the population lack skills that would allow them to think critically about information in the political sphere, to identify misinformation, and therefore to fully exercise democratic citizenship. Political science has theorized the link between information and power and information professionals understand the cognitive decision-making process involved in processing information, but these two literatures rarely intersect. This paper interrogates the links between information literacy (IL) and the rise of authoritarian populism in order to advance the development of a new transtheoretical model that links political science (which studies power), information science, and critical pedagogy to suggest new paths for teaching and research. We call for a collaborative research and teaching agenda, grounded in a holistic understanding of information as power, that will contribute to achieving a more informed citizenship and promoting a more inclusive democracy.

Improving Operational Efficiency Through Alarm Management in Water Treatment Processes Using Artificial Intelligence

Water Treatment Plants are controlled by modern industrial process control systems like SCADA or DCS. This facilitates to monitor, control, and troubleshoot water treatment processes and helps in maintaining continuous supply of water with adequate quality. At times and in contrary, these systems hamper process control by generating far too many alarms than needed. Many of the alarms are nuisance in nature and do not indicate any real abnormality. The true alarms which require prompt operator actions to normalize the process are often buried in the pool of nuisance alarms causing significant challenge for operator to take appropriate corrective actions in a timely manner. Many of the past major incidents occurring in the major process industries were attributed to operators’ inability to identify true alarms and take necessary actions. In this paper, we propose an Artificial Intelligence (AI) based pattern mining and advisory system to improve operational efficiency in alarm management. The identified alarm patterns bring out actionable insights in data by (i) identifying nuisance, chattering, redundant alarms, and (ii) Alarm response Pattern. A novel technique for sequential pattern mining in industrial Alarm & Event log data was developed based on State-of-the-art AI based association rule and pattern mining. The efficacy of the proposed method for systematically improving alarm management system in an actual plant environment is currently being studied in a water treatment plant in Singapore.

Research of Network Behavior Pattern Recognition Based on Power Monitoring System

Based on the network security protection of the power monitoring system, this paper proposes a pattern recognition model for the operation behavior of the operation and maintenance personnel of power monitoring system. Through the technical research of the alarm pattern recognition of the network security management platform, and carries out the operation type data of the network security management platform Clustering analysis uses historical data to intelligently train the clustering model. Through training, the risk level classification of the operation behavior is obtained, and then real-time data is introduced into the model for detection, which can judge the degree of risk of the operation event in real time. With the improvement of the K-means clustering algorithm and the application of big data analysis, it further improves the he intelligence level of the platform and provides technical support for the intelligent protection of network.