Alarm Monitoring Research Articles

Correction to: Carbon nanotube modified cellulose nonwovens: superhydrophobic, breathable, and sensitive for drowning alarm and motion monitoring

Correction to: Carbon nanotube modified cellulose nonwovens: superhydrophobic, breathable, and sensitive for drowning alarm and motion monitoring

An adaptive metaheuristic optimization approach for Tennessee Eastman process for an industrial fault tolerant control system.

The Tennessee Eastman Process (TEP) is widely recognized as a standard reference for assessing the effectiveness of fault detection and false alarm tracking methods in intricate industrial operations. This paper presents a novel methodology that employs the Adaptive Crow Search Algorithm (ACSA) to improve fault identification capabilities and mitigate the occurrence of false alarms in the TEP. The ACSA is an optimization approach that draws inspiration from the observed behavior of crows in their natural environment. This algorithm possesses the capability to adapt its search behavior in response to the changing dynamics of the optimization process. The primary objective of our research is to devise a monitoring strategy that is adaptable in nature, with the aim of efficiently identifying faults within the TEP while simultaneously minimizing the occurrence of false alarms. The ACSA is applied in order to enhance the optimization of monitoring variables, alarm thresholds, and decision criteria selection and configuration. When compared to traditional static approaches, the ACSA-based monitoring strategy is better at finding faults and reducing false alarms because it adapts well to changes in process dynamics and disturbances. In order to assess the efficacy of our suggested methodology, we have conducted comprehensive simulations on the TEP dataset. The findings suggest that the monitoring strategy based on ACSA demonstrates superior fault identification rates while concurrently mitigating the frequency of false alarms. In addition, the flexibility of ACSA allows it to efficiently manage process variations, disturbances, and uncertainties, thereby enhancing its robustness and reliability in practical scenarios. To validate the effectiveness of our proposed approach, extensive simulations were conducted on the TEP dataset. The results indicate that the ACSA-based monitoring strategy achieves higher fault detection rates while simultaneously reducing the occurrence of false alarms. Moreover, the adaptability of ACSA enables it to effectively handle process variations, disturbances, and uncertainties, making it robust and reliable for real-world applications. The contributions of this research extend beyond the TEP, as the adaptive monitoring strategy utilizing ACSA can be applied to other complex industrial processes. The findings of this study provide valuable insights into the development of advanced fault detection and false alarm monitoring techniques, offering significant benefits in terms of process safety, reliability, and operational efficiency.

Enhancing Coal Mining Efficiency

Coal is a prominent energy resource for several countries. Of late, exploring the automatic management and control of coal mining has been a significant task. This article presents a framework for a mine-wide integrated automation management and control platform with the goal of advancing coal mining through unified data, models, platforms, and plans. Utilizing cutting-edge technologies, the platform offers resource management, real-time monitoring, remote control, statistical analysis, and intelligent alarm systems. Data access design ensures standardized data collection and exchange, fostering interoperability. A big data storage center manages heterogeneous data sources. The platform interface design emphasizes flexibility and scalability through containerized applications and microservices frameworks, streamlining deployment. The functional design encompasses subsystem configuration access, real-time monitoring, remote access, etc. A detailed evaluation is presented to demonstrate the significance of the proposed platform in terms of functionality, performance, and scalability.

An anti-swelling chitosan-based hydrogel driven by balance of hydrophilic segment and hydrophobic segment strategy for underwater detection of human motion

Hydrogel-based flexible strain sensors have gained rapid development. However, most of traditional hydrogels were prone to undergo swelling behavior underwater as solvent molecules continuously penetrated, which limited their application scenarios. To make up this deficiency, a chitosan-based anti-swelling hydrogel cross-linked by ionic liquid are presented. The hydrogel is consisted of chitosan (CS) and copolymer of acrylic acid (AA), 2-hydroxyethyl methacrylate (HEMA), butyl acrylate (BA) and dimethylaminoethyl methacrylate maleate (DMAEMA-MA). The P(HEMA-BA-AA)/ILs/CS hydrogel exhibited excellent anti-swelling properties driven by balance of hydrophilic segment and hydrophobic segment strategy (equilibrium swelling rate of −3.79 % in water for 100 days). The ionic liquid was introduced into the hydrogel system as cross-linking agent, which could effectively prevent the loss of conductivity of the hydrogel after soaking in water. The synergy effect chain entanglement, H-bonds and electrostatic interactions endowed the hydrogel with excellent mechanical property. Surprisingly, the modulus and toughness of the hydrogel were significantly boosted after a week of submersion. Thus, this anti-swelling hydrogel was employed as wireless strain sensor, which implemented underwater motion monitoring and safety alarms. It is expected that the anti-swelling hydrogel driven by balance of hydrophilic and hydrophobic effects would inspire the development of underwater sensors.

Multivariate Alarm Monitoring for Non-Convex Normal Operating Zones Based on Search Cones

Normal operating zones (NOZs) are the variation spaces of multiple related variables under normal conditions. This paper proposes a search-cone-based method to establish NOZ models and design dynamic alarm thresholds for multivariate alarm monitoring. First, mathematical models are established by exploiting search cones from historical normal data points to describe non-convex NOZs of multiple related process variables. Second, dynamic alarm thresholds of each process variable are designed based on the established NOZ models for new data points being inside or outside the NOZs. The proposed method extends from convex NOZs to non-convex ones, and the main contribution is to establish mathematical models of NOZs for monitoring abnormal conditions and design dynamic alarm thresholds for generating alarms. Continuous stirred tank reactors (CSTR) and industrial examples are provided to illustrate the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Alarm systems play critically important roles for safe and efficient operation of industrial plants. Many process variables are related so that their normal variation ranges formulate high-dimensional geometric spaces referred to as normal operating zones (NOZs). Hence, it is desirable to adopt the NOZs for alarm monitoring. By contrast, if relationships among process variables are ignored, missed and false alarms may occur to deteriorate the performance of alarm systems. This paper proposes a method to establish mathematical models for NOZs and design dynamic alarm thresholds. The established models can describe non-convex or convex NOZs; the dynamic alarm thresholds can quantify distances between NOZ boundaries and operating points of a multivariate process

Bridging the Gap between Technology and Medicine through the Revolutionary Impact of the Healthcare Internet of Things on Remote Patient Monitoring

Healthcare Internet of Things (IoT) initiatives that aim to integrate technology and medicine are shaking the sector to its foundations. The revolutionary potential of the proposed strategy is shown here as we investigate the far-reaching consequences of the Healthcare IoT on remote patient monitoring. The beginning sets the stage by underlining the significance of bridging the gap between technology and medicine. Our multi-pronged approach comprises Internet of Things (IoT) remote monitoring, cloud-based analysis, artificial intelligence (AI) integrated diagnostics, real-time alerts, and predictive analytics. Our study's results demonstrate that the proposed approach is superior to the status quo. The area of remote patient monitoring has profited considerably from the employment of traditional approaches, such as the fusion of data from wearable sensors, analysis in the cloud, diagnostics that utilize artificial intelligence, real-time monitoring, predictive modeling, and smart alarm systems. The suggested strategy, however, performs very well across all of the most important measures of assessment. Comparatively, the accuracy rate of the conventional wearable sensor fusion approach was only 76%, whereas our suggested method reached 89%. Our strategy was also more accurate than the standard approach (88% vs. 73%). When compared to the recall rate of 68% produced by conventional methods, our suggested strategy significantly outperformed the competition. It's a great option for hospitals and clinics since it improves diagnostic precision and speed without breaking the bank.

Horário da inseminação artificial na taxa de prenhez das vacas Holandesas usando monitoramento automatizado de atividades

ABSTRACT: This study evaluated the probability of pregnancy and associated factors for two times artificial inseminations (AI), 8 or 10 hours after automated activity monitoring (AAM) alarm on the first postpartum AI of 1,054 Holstein dairy cows. The estrus was synchronized by prostaglandin or estradiol-progesterone program. Stepwise logistic regression was performed to analyze the probability of pregnancy, and associated factors (activity, estrus intensity, parity, peripartum health, retained placenta, postpartum vaginal discharge, and season). The highest pregnancy rates were obtained with multiparous animals, inseminated ten hours after the AAM alarm, in the fall or winter season, with a high activity peak and estrus intensity (P &lt; 0.05). Peripartum diseases, retained placenta, and postpartum vaginal discharge negatively influenced the pregnancy rate, regardless of parity. Thus, the optimization of AAM models by including on-farm measures like parity, peripartum health history, and environmental conditions may favor the correct identification of estrus and improve the AAM alarm regarding the ideal moment for AI, increasing the reproductive performance in dairy cows.

Design and Implementation of Intelligent Fire Monitoring System Based on Multi-Sensor Data Fusion

Abstract To reduce the losses caused by fire, this paper proposes an intelligent fire monitoring system based on multi-sensor data fusion. Through the design of the temperature, smoke and flame sensors, based on the weighted least squares WLS and improved fuzzy support degree of similar sensors and dissimilar sensors data fusion, respectively, in the ZigBee wireless network based on the design of the fire remote monitoring and control alarm for monitoring, and add the local storage and emergency module. In addition, whether the router nodes work on the ZigBee wireless network during the fire test depends on multi-sensor data fusion. The test shows that the node communication distance is extended and can reach the acceptable requirements in the range of 70m, the highest probability of fire occurrence is 0.92, the lowest probability is 0.14, the overall probability of occurrence is in the range of 0.4 to 0.6, and the error between the predicted value of the probability of occurrence of the fire and the expected value is within ±0.05.

ADVANCED AUTOMATIC SYSTEM FOR REMOTE CONTROL IN THE OIL AND GAS INDUSTRY

The purpose of this work is to design and implement a cascade control system of the level with the flow rate, with remote supervision, in an industrial type installation, located in the Laboratory of Measurement Techniques, Department of Automation, Computers and Electronics, within the Faculty of Mechanical and Electrical Engineering. The operating parameters of the installation are as follows: suction pressure: 0 – 2 bar; discharge pressure: 0 -10 bar; water flow: 0-30 m3/h; transported liquid: water. The automation system is structured on three hierarchical levels: level 1: the instrumentation mounted on the installation (transducers, signaling devices, etc.); level 2: includes the TGA System; level 3: constitutes the upper hierarchical level and includes the HMI (Human Machine Interface). The TGA system is a computerized system that ensures the monitoring, command and control functions of all the stand's component elements. It contains a programmable logic controller PLC 1214 – Siemens, which communicates via an Ethernet IP network with an operator interface and with the network of the Faculty of Mechanical and Electrical Engineering, which allows remote control of the installation. It also includes the protection system in case of failure, which involves stopping the rotating equipment, isolating the pipes by closing the control valve and alarming the operators. The HMI (Human Machine Interface) interface presents 3 options on the start page, from where the operator can select which application he wants to test: flow control loop application; level adjustment loop application; cascade adjustment loop application. The HMI equipment integrates SCADA functions, database update, evolution curves of analog quantities, sound and monitor alarm system, keyboard command, periodic records, event records, alarm records and technological schemes at the human-machine interface. The entire software is made in an open architecture, recommended by ISO norms. The PLC communicates in the network by adopting the protocol TCP/IP. An executable program used for the real-time application program was developed.

Carbon nanotube modified cellulose nonwovens: superhydrophobic, breathable, and sensitive for drowning alarm and motion monitoring

Carbon nanotube modified cellulose nonwovens: superhydrophobic, breathable, and sensitive for drowning alarm and motion monitoring

The Influence Of Using Media Trainer Instrumentation For Monitoring System Alarms On The Learning Outcomes Maritime Electrical Cadets

Control and instrumentation engineering education plays a crucial role in preparing DIII Maritime Electrical to face the complexity of control systems in the maritime industry. In an effort to improve the quality of learning, the use of educational technologies such as media trainer instrumentation alarm monitoring systems has become the focus of research. The study aims to investigate the impact of the use of media trainer instrumentation alarm system monitoring on the learning results of the control system of the cadets. Merchant Marine Polytechnic Of Surabaya, as the leading maritime higher education institution in Indonesia, has led in integrating this technology into the learning process. By leveraging the media trainer instrumentation alarm monitoring system, they hope the cadets can gain a deep understanding of the key concepts in system control and monitoring. This research uses a quantitative data analysis approach. The control group followed conventional teaching methods, while the experimental group engaged in learning using this technology. Learning data from both groups, including test scores, tasks, and performance evaluations, were collected and analyzed. The results of this research provide valuable insights for maritime higher education institutions and practitioners in this industry. The use of trainer learning media has a significant impact on the learning outcomes of the experimental class, which reaches a higher level compared to the control class that does not use the trainer (protoboard) learning media in the Control System courses at the ETO Faculty in Merchant Marine Polytechnic Of Surabaya

Evaluation of the sensory environment in a large tertiary ICU

BackgroundICU survival is improving. However, many patients leave ICU with ongoing cognitive, physical, and/or psychological impairments and reduced quality of life. Many of the reasons for these ongoing problems are unmodifiable; however, some are linked with the ICU environment. Suboptimal lighting and excessive noise contribute to a loss of circadian rhythms and sleep disruptions, leading to increased mortality and morbidity. Despite long-standing awareness of these problems, meaningful ICU redesign is yet to be realised, and the ‘ideal’ ICU design is likely to be unique to local context and patient cohorts. To inform the co-design of an improved ICU environment, this study completed a detailed evaluation of the ICU environment, focussing on acoustics, sound, and light.MethodsThis was an observational study of the lighting and acoustic environment using sensors and formal evaluations. Selected bedspaces, chosen to represent different types of bedspaces in the ICU, were monitored during prolonged study periods. Data were analysed descriptively using Microsoft Excel.ResultsTwo of the three monitored bedspaces showed a limited difference in lighting levels across the day, with average daytime light intensity not exceeding 300 Lux. In bedspaces with a window, the spectral power distribution (but not intensity) of the light was similar to natural light when all ceiling lights were off. However, when the ceiling lights were on, the spectral power distribution was similar between bedspaces with and without windows. Average sound levels in the study bedspaces were 63.75, 56.80, and 59.71 dBA, with the single room being noisier than the two open-plan bedspaces. There were multiple occasions of peak sound levels > 80 dBA recorded, with the maximum sound level recorded being > 105 dBA. We recorded one new monitor or ventilator alarm commencing every 69 s in each bedspace, with only 5% of alarms actioned. Acoustic testing showed poor sound absorption and blocking.ConclusionsThis study corroborates other studies confirming that the lighting and acoustic environments in the study ICU were suboptimal, potentially contributing to adverse patient outcomes. This manuscript discusses potential solutions to identified problems. Future studies are required to evaluate whether an optimised ICU environment positively impacts patient outcomes.

Correlation warping radius tracking for condition monitoring of rolling bearings under varying operating conditions

Health indicators (HI) are crucial in early fault alarm and degradation monitoring of mechanical failures. In recent years, many HIs are developed and reported, however, a constructed HI which is more sensitive to incipient defect, robust to varying operating conditions and is interpretable to characterize the degradation process is still remain to be studied. To address this issue, a more powerful and effective HI applicable for nonstationary operating condition is studied in this paper. Inspired by chaos fractal theory (CFT) and slowly varying damage dynamics theory (SVDDT), this paper calculates the range of correlation warping radius (CWR) of each segment of signal through phase space reconstruction, and then finds that the boundary of correlation warping radius range (CWR-B) in normal conditions follows a fixed linear relationship. Through the application of this linear relationship, a new health monitoring indicator for vibration signals of rotating machinery has been proposed, through correlation warping radius tracking measurement (CWRTM). The proposed indicator not only has outstanding monitoring advantages for rotating machinery in a cyclo-stationary condition, but also applicable for machinery condition monitoring under variable operating conditions, achieving the unity of methods for condition monitoring in stationary and variable operating conditions. Compared with some typical health indicators, the experimental results on bearing run-to-failure dataset and wind turbine generator bearing field dataset show that the proposed indicator in this article is not only sensitive to early-stage fault, but also validated robust to varying operating conditions.

Application of ship’s information system to control and monitor the technical condition of ship’s equipment

Failures and breakdowns of marine equipment that occur during the daily operation of ships can lead to major accidents, endangering the lives of the crew and passengers of ships, and also pose a threat to the environment. A project for using a ship information system to monitor the technical condition of ship power equipment in real time through integration with ship automated systems is presented in the paper. The implementation of the information system was carried out on the Handy Size dry-cargo vessel with a deadweight of 36,987 tons, during which an analysis of ship systems was carried out and sources for obtaining initial information were identified. These were AMS system (alarm monitoring system), technical control system of MAN 5S50ME-B “CoCoS” main engine condition, equipment on the navigation bridge (AIS, GPS, ECDIS, VDR). During the implementation of the project, a secure connection to the above-mentioned ship systems for collecting and processing information using the installed Dataloger computer with software was made. Channels for transmitting information to the shore through the satellite Internet and mobile communication systems using data coding and compression to reduce traffic volume were determined. The use of the implemented information system makes it possible to remove some of the routine tasks for the crew and the onshore technical service, to obtain more accurate information on the formation and control of maintenance tasks in real time.

Efficacy of Medical Device Alarm Integration into a Simulated H-60 Integrated Communication System.

This study sought to examine the efficacy of integrating medical device alarms into the intercommunication set of a simulated HH-60, allowing medics to hear the alarms over the ambient noise of the aeromedical environment. U.S. Army critical care flight paramedics were recruited as subjects for this study. Subjects participated in two testing scenarios: One with patient monitor alarms integrated into their communication lines and one without integrated alarms (the control condition). Testing took place in a simulated HH-60 interior with two priority-level patients per testing scenario, one on either side of the interior. Subjects provided care to these two patients for 30 minutes per scenario. After both scenarios were complete, the subjects were given a questionnaire to obtain their feedback on alarm integration. Six subjects took part in this study, so the results do not have sufficient power to represent the population. No statistically significant results were found. Looking at the trends in the data, implementing alarm integration showed the indications of reducing reaction time to alarms, decreasing or matching the amount of time spent with the patient monitor, and equivalent amounts of time dedicated to patient treatment when compared to the nonintegrated scenario.The feedback obtained from the subjects provided a list of perceived benefits, drawbacks, and improvements related to the integration of medical device alarms into the intercommunication set. Although the study was underpowered, the trends in the data indicate a benefit to the medics when integrating medical device alarms. When coupled with strongly favorable end-user feedback, the results provide justification for pursuing the effort of integrating alarms and performing future studies with improved integration systems to optimize the potential of the system.

Application of Machine Learning Models to Biomedical and Information System Signals From Critically Ill Adults

Machine learning (ML)-derived notifications for impending episodes of hemodynamic instability and respiratory failure events are interesting because they can alert physicians in time to intervene before these complications occur. Do ML alerts, telemedicine system (TS)-generated alerts, or biomedical monitors (BMs) have superior performance for predicting episodes of intubation or administration of vasopressors? An ML algorithm was trained to predict intubation and vasopressor initiation events among critically ill adults. Its performance was compared with BM alarms and TS alerts. ML notifications were substantially more accurate and precise, with 50-fold lower alarm burden than TS alerts for predicting vasopressor initiation and intubation events. ML notifications of internal validation cohorts demonstrated similar performance for independent academic medical center external validation and COVID-19 cohorts. Characteristics were also measured for a control group of recent patients that validated event detection methods and compared TS alert and BM alarm performance. The TS test characteristics were substantially better, with 10-fold less alarm burden than BM alarms. The accuracy of ML alerts (0.87-0.94) was in the range of other clinically actionable tests; the accuracy of TS (0.28-0.53) and BM (0.019-0.028) alerts were not. Overall test performance (F scores) for ML notifications were more than fivefold higher than for TS alerts, which were higher than those of BM alarms. ML-derived notifications for clinically actioned hemodynamic instability and respiratory failure events represent an advance because the magnitude of the differences of accuracy, precision, misclassification rate, and pre-event lead time is large enough to allow more proactive care and has markedly lower frequency and interruption of bedside physician work flows.

Anomalistic Symptom Judgment Algorithm for Predictive Maintenance of Ship Propulsion Engine Using Machine Learning

Ships serve as crucial transporters of cargo and passengers in substantial volumes and operate for a long time; therefore, an efficient maintenance system is essential for economical and stable vessel operation. In this study, a machine learning based approach was developed that considers the rapidly changing load fluctuations on ships and large variability in normal operation data to apply predictive maintenance to the propulsion engines of ships. After acquiring propulsion engine data from the alarm monitoring system of a ship, data and maintenance items were analyzed to select the data that could determine the anomalistic symptoms of the propulsion engine. Further, the main engine condition criterion value was defined as the factor for anomalistic symptom prediction. An engine anomalistic symptom judgment algorithm that can be practically used for ship maintenance prediction was developed and verified using machine learning.

Pediatric Characteristics Associated With Higher Rates of Monitor Alarms.

Background: Continuous physiologic monitoring commonly is used in pediatric medical-surgical (med-surg) units and is associated with high alarm burden for clinicians. Characteristics of pediatric patients generating high rates of alarms on med-surg units are not known. Objective: To describe the demographic and clinical characteristics of pediatric med-surg patients associated with high rates of clinical alarms. Methods: We conducted a cross-sectional, single-site, retrospective study using existing clinical and alarm data from a children's hospital. Continuously monitored patients from med-surg units who had available alarm data were included. Negative binomial regression models were used to test the association between patient characteristics and the rate of clinical alarms per continuously monitored hour. Results: Our final sample consisted of 1,569 patients with a total of 38,501 continuously monitored hours generating 265,432 clinical alarms. Peripheral oxygen saturation (SpO2) low alarms accounted for 57.5% of alarms. Patients with medical complexity averaged 11% fewer alarms per hour than those without medical complexity (P < 0.01). Patients older than 5 years had up to 30% fewer alarms per hour than those who were younger than 5 years (P < 0.01). Patients using supplemental oxygen averaged 39% more alarms per hour compared with patients who had no supplemental oxygen use (P < 0.01). Patients at high risk for deterioration averaged 19% more alarms per hour than patients who were not high risk (P = 0.01). Conclusion: SpO2 alarms were the most common type of alarm in this study. The results highlight patient populations in pediatric medical-surgical units that may be high yield for interventions to reduce alarms. Most physiologic monitor alarms in pediatric medical-surgical (med-surg) units are not informative and likely could be safely eliminated to reduce noise and alarm fatigue.1-3 However, identifying and sustaining successful alarm-reduction strategies is a challenge. Research shows that 25% of patients in pediatric med-surg units produce almost three-quarters of all alarms.4 These patients are a potential high-yield target for alarm-reduction strategies; however, we are not aware of studies describing characteristics of pediatric patients generating high rates of alarms. The patient populations seen on pediatric med-surg units are diverse. Children of all ages are cared for on these units, with diagnoses ranging from acute respiratory infections, to management of chronic conditions, and to psychiatric conditions. Not all patients on pediatric med-surg units have physiologic parameters continuously monitored,4 but among those who do, understanding patient characteristics associated with high rates of alarms may help clinicians, healthcare technology management (HTM) professionals, and others working on alarm management strategies to develop targeted interventions. We conducted an exploratory retrospective study to describe patient characteristics associated with high rates of alarms in pediatric med-surg units.

Research and deployment of a Python-based software framework for large-scale physical experiment control

This paper presents a python-based, open-source framework for slow control systems used in particle physics experiments. The framework includes several functions such as display, data query, alarm, log and remote monitoring, etc. The data communication utilizes the MQTT (Message Queuing Telemetry Transport) protocol, ensuring real-time and reliable data transmission. The object-oriented programming approach ensures each functional module's independence and reusability, making them easily interchangeable and extendable through configuration files in various applications. Finally, two case deployment applications of the framework are provided, where both of them are tested with Docker.

PERCEIVED STRESS AND ALARM FATIGUE AMONG NURSES DEALING WITH CRITICALLY ILL PATIENTS: A CROSS SECTIONAL STUDY

Nurses are exposed to many clinical monitoring alarms in managing critically ill patients, gradually leading to emotional exhaustion and burnout. This situation is called alarm fatigue, where nurses tend to do inappropriate actions to silence the alarms or adjust them outside the appropriate limits. Thus, this study aims to explore stress and alarm fatigue among staff nurses dealing with critically ill patients. A cross-sectional study was conducted among 114 nurses dealing with critically ill patients (ICU/CCU/HDU/NICU, and A&amp;E) in a private hospital in Kuala Lumpur. Using purposive sampling, participants who met inclusion and exclusion criteria were asked to fill up a questionnaire consisting of sociodemographic data, a nursing stress scale, and an alarm fatigue questionnaire. After 30 minutes, the questionnaire was collected. Most nurses dealing with critically ill patients reported a low level of stress (65.8%) and alarm fatigue (69.3%). Based on study findings, subscale workload was identified as the primary source of stress (Mean = 2.516, SD = 0.484). Meanwhile, education level and working shift are the only sociodemographic characteristics associated with stress and alarm fatigue (p &lt; 0.05). However, no significant correlation was seen between stress and alarm fatigue, and this revealed a weak correlation between these two variables (r = 0.078, p = 0.411). Stress and alarm fatigue was found statistically not significant towards each other. Future studies should explore different risk factors associated with alarm fatigue among nurses dealing with critically ill patients.