Alarm Monitoring Research Articles

Intelligent alarm system for river embankment seepage based on BILSTM

Currently, the alarm functions of existing levee seepage monitoring systems are limited to single-parameter monitoring and lack rate-of-change alarms and correlation alarms. This can lead to false alarms, missed alarms, equipment failures, or unnecessary downtime. To enhance the intelligence of levee safety monitoring and seepage alarms, a levee seepage intelligent alarm system based on a Bidirectional Long Short-Term Memory (BILSTM) network model was designed and implemented. Firstly, data cleaning and preprocessing are carried out on the engineering safety monitoring operation data to reduce the influence of dirty data such as outliers and repetitive values on the accuracy of alarms. Secondly, for the correlation between the piezometric tube water levels of the levee and the Yangtze River water levels, a correlation analysis based on Mutual Information (MI) theory was conducted to minimize the effect of piezometric tube water level change delays on correlation. Finally, the BILSTM model was used to predict trends in these potentially abnormal data intervals. Based on engineering application requirements, alarm thresholds were established, and a multi-level alarm module was developed. Field operation test results show that the proposed method can accurately predict the piezometric tube water levels of levees, achieving intelligent alarms within the engineering safety monitoring system.

Design of Signal Lamp Filament Monitoring Alarm Instrument

To improve the reliability of the light emitting diode (LED) signal lamp filament current monitoring alarm instrument for metro systems, a new type of hot standby online monitoring apparatus was developed which is based on synchronous transmission data (STD) bus technology. In this system, a double hot standby mode can be achieved by adopting bus arbitration. In addition, to detect the effective value of alternating current which is from 0 to 200 mA in the signal lamp lighting circuit, a precision rectifier signal conditioning circuit and an isolated acquisition circuit were designed. This new type of alarm instrument has high detection accuracy and could meet the functional requirements for metro signal systems after comparing it with some industry products that were applied on the spot.

A Credibility Monitoring Approach and Software Monitoring System for VHF Data Exchange System Data Link Based on a Combined Detection Method

Due to VDES’s higher data transmission speed and complex communication protocols, vulnerabilities within its data link infrastructure are more pronounced. To ensure the reliability of VDES data transmission, this manuscript proposes a credibility monitoring approach based on the combined detection method of radio interference detection and spoofing source identification and localization, focusing on key data link vulnerabilities outlined in the IALA G1181 VDES VDL Integrity Guide. Automated monitoring is achieved through VDES data link monitoring software (VDES(AIS 2.0)), which is based on a three-tier architecture and a Client/Server (C/S) model. The software validates monitoring techniques and software against various interference scenarios. Visualization of monitoring results, alarm notifications, and relevant data through the front-end interface enhances understanding of VDES data link credibility. This framework supports effective surveillance and detection of vulnerabilities, such as radio interference and spoofing sources.

Industrial IoT-Based Energy Monitoring System: Using Data Processing at Edge

Edge-assisted IoT technologies combined with conventional industrial processes help evolve diverse applications under the Industrial IoT (IIoT) and Industry 4.0 era by bringing cloud computing technologies near the hardware. The resulting innovations offer intelligent management of the industrial ecosystems, focusing on increasing productivity and reducing running costs by processing massive data locally. In this research, we design, develop, and implement an IIoT and edge-based system to monitor the energy consumption of a factory floor’s stationary and mobile assets using wireless and wired energy meters. Once the edge receives the meter’s data, it stores the information in the database server, followed by the data processing method to find nine additional analytical parameters. The edge also provides a master user interface (UI) for comparative analysis and individual UI for in-depth energy usage insights, followed by activity and inactivity alarms and daily reporting features via email. Moreover, the edge uses a data-filtering technique to send a single wireless meter’s data to the cloud for remote energy and alarm monitoring per project scope. Based on the evaluation, the edge server efficiently processes the data with an average CPU utilization of up to 5.58% while avoiding measurement errors due to random power failures throughout the day.

Analysis of ST segment alarms in children admitted to the paediatric and cardiac intensive care units and cardiac progressive care unit: a single-centre retrospective study.

ST segment monitoring in the adult population allows for the early detection of myocardial ischaemia. In children admitted to the paediatric intensive care unit (PICU), cardiac intensive care unit (CICU), and cardiac progressive care unit (CPCU), it is unclear if continuous ST segment alarm monitoring is necessary in all patients. All patients admitted to the PICU, CICU, and CPCU during the study period were included. Children with any ST segment alarms were compared with those without an alarm during their stay. The electrocardiogram confirmed true ST segment alarms were compared with all other ST segment alarms. Demographic and clinical data were extracted from the medical record. Medical interventions and procedures occurring around ST segment alarms were recorded for multivariable analysis assessing for the association of true ST segment. Logistic regression was used to evaluate the associations with ST segment alarms during hospital stays. ST segment alarms occurred in 36% of hospital stays, and only 3.4% were considered true. True alarms were significantly more common among patients with a cardiac-related diagnosis, located in both cardiac units, and having received an intervention with any vasoactive medication. In the multivariable logistic regression, patients 11 years or older, hypotension, supraventricular tachycardia, and initiation/escalation of any vasoactive were independently associated with a true ST segment alarm. True ST segment alarms were infrequent, occurring in 1.2% of stays during the study period. Alarm monitoring may be beneficial in those with an underlying cardiac diagnosis.

Prototype Alarm Integrating Pulse-Driven Nitrogen Dioxide Sensor Based on Holey Graphene Oxide/In2O3.

NO2 seriously threatens human health and the ecological environment. However, the fabrication of highly sensitive NO2 sensors with rapid response/recovery rates, low detection limits, and ease of integration remains a challenge. Herein, benefiting from the fast carrier transfer and rich active sites, holey graphene oxide (HGO) was adopted to functionalize the In2O3 nanosheet to construct NO2 gas sensors. Characterization and theoretical calculations established the merits of HGO decoration in the NO2 sensing. The optimal sample, 0.5 wt % HGO/In2O3-sheet, exhibited superior sensing properties, resulting in a 1.37-fold improvement in response to 1 ppm of NO2 compared to the GO/In2O3 counterpart. Gas-sensing kinetics analysis revealed its lower activation energy and higher kinetic rate constants. Importantly, pulsed-temperature modulation was employed to decouple the gas adsorption from surface activation processes, achieving an ultrahigh response of 2776 to 1 ppm of NO2 for the 0.5 wt % HGO/In2O3-sheet sensor. Compared to the isothermal mode, this strategy enhanced the response value by 1.6 times, reduced the response/recovery time by 33%/70%, and enabled the detection of NO2 concentrations as low as 1 ppb. Finally, an NO2 monitoring alarm system based on the 0.5 wt % HGO/In2O3-sheet sensor with pulsed-temperature modulation was demonstrated for hazard warnings.

Mitigating Alarm Fatigue and Improving the Bedside Experience by Reducing Nonactionable Alarms

To assess whether conditional bedside alarm triggers can reduce the frequency of non-actionable alarms without compromising patient safety and enhance nursing and family satisfaction. Single center, quality improvement initiative in an acute care cardiac unit (ACCU) and pediatric intensive care unit (PICU). Following the 4-week pre-intervention baseline period, bedside monitors were programmed with hierarchical time delay and conditional alarm triggers. Bedside alarms were tallied for 4 weeks each in the immediate post intervention period and 2-year follow-up. The primary outcome was alarms per monitored patient day. Nurses and families were surveyed pre- and post-intervention. A total of 1509 patients contributed to 2034, 1968, and 2043 monitored patient days which were evaluated in the baseline, follow-up, and 2-year follow-up periods, respectively. The median number of alarms per monitored patient day decreased by 75% in the PICU (p<0.001) and 82% in the ACCU (p<0.001) with sustained effect at 2-year follow-up. No increase of rapid response calls, emergent transfers, or code events occurred in either unit. Nursing surveys reported an improved capacity to respond to alarms and fewer perceived non-actionable alarms. Family surveys, however, did not demonstrate improved sleep quality. Implemented changes to bedside monitor alarms decreased total alarm frequency in both the acute care cardiac unit and pediatric intensive care unit, improving the care provider experience without compromising safety.

NABNet: Deep Learning-Based IoT Alert System for Detection of Abnormal Neck Behavior.

The excessive use of electronic devices for prolonged periods has led to problems such as neck pain and pressure injury in sedentary people. If not detected and corrected early, these issues can cause serious risks to physical health. Detectors for generic objects cannot adequately capture such subtle neck behaviors, resulting in missed detections. In this paper, we explore a deep learning-based solution for detecting abnormal behavior of the neck and propose a model called NABNet that combines object detection based on YOLOv5s with pose estimation based on Lightweight OpenPose. NABNet extracts the detailed behavior characteristics of the neck from global to local and detects abnormal behavior by analyzing the angle of the data. We deployed NABNet on the cloud and edge devices to achieve remote monitoring and abnormal behavior alarms. Finally, we applied the resulting NABNet-based IoT system for abnormal behavior detection in order to evaluate its effectiveness. The experimental results show that our system can effectively detect abnormal neck behavior and raise alarms on the cloud platform, with the highest accuracy reaching 94.13%.

Continuous early warning score calculations can facilitate the evaluation of the condition of patients developing anastomotic leak or infection after esophageal and pancreatic resection; a cohort study

BackgroundEarly diagnosis of anastomotic leak is challenging due to its heterogeneous clinical presentation. Continuous vital sign monitoring compared to periodic monitoring has potential to expedite anastomotic leak diagnosis. The aim is to assess this potential after esophageal and pancreatic cancer resection. MethodsIn this data modelling cohort study vital signs were continuously monitored and periodically recorded according to EWS protocol. We retrospectively calculated EWSs every one minute using continuous vital sign data. The percentage of patients having an EWS-alarm in 24 hours preceding an anastomotic leak, the elapsed time between the first alarm and diagnosis were compared between regular periodic nurse EWSs and the continuously calculated EWSs. These data were also compared to those of patients with other infectious complications and controls without complications. ResultsOne hundred ninety patients were included, twenty-six with an anastomotic leak (14%), thirty-six with an infectious complication (19%) and one hundred twenty-eight controls (67%). The time between the first continuous EWS-alarm and anastomotic leak was 23 hours [IQR 19-24] compared to 3hrs [IQR 2-13] with periodic alarms. Sixty-two percent of leak patients had at least one continuous alarm in the 24 hours before the event, compared to 15% for periodic EWS (p<.001). More patients with infections had a continuous alarm compared to anastomotic leak patients (86% vs. 62%, p=.026). Postoperative alarms in both groups were more frequent compared to controls. ConclusionAfter esophageal and pancreatic resections, continuous monitoring alarms earlier and more frequently than periodic monitoring before anastomotic leak diagnosis. However, combination with several other diagnostic modalities seems required for the definite diagnosis.

Mentoring Professionals to Use Digital Tools in Home Care.

Nurses use electronic information systems daily, and digital devices have been developed to enable patient to live at home as long as possible. This study aimed to test reverse mentoring for professionals working with digital tools in home care. An electronic survey was sent twice to nurses to collect their opinions about the tools they use. Based on the results from the first survey (N=184), the mentoring content focused on the use of information systems and digital tools. Respondents' experiences as information system users were more abundant than their experiences as digital tool users. Tools supporting independent living were seldom used, but safety devices and alarm monitoring were used daily. The mentoring meetings induced changes and encouraged participants to acquire skills related to the use of digital tools and to evaluate their work critically.

Accelerometer static state detection (SSD)-assisted GNSS/accelerometer bridge monitoring algorithm

Abstract In the field of structural health monitoring (SHM), a loosely coupled (LC) Kalman filtering algorithm that accounts for baseline drift errors is commonly used to integrate GNSS data with accelerometer data. In the LC algorithm, the baseline drift errors are considered unknown parameters that need to be estimated. In scenario of continuous float solutions, the estimation of baseline drift error is often inaccurate, leading to the divergence of monitoring results. Theoretically, as a type of motion sensor, accelerometers are expected to qualitatively determine the priori state of bridges, whether dynamic or static. Utilizing the inherent characteristics of accelerometers and the principle of zero-velocity detection in integrated navigation, we originally propose a bridge static state detection (SSD) method based on low-cost accelerometer, and introduces this prior SSD information as a constraint in GNSS/accelerometer LC algorithm, called SSD-LC bridge monitoring algorithm. Through a simulation platform and real-world bridge monitored tests, the effectiveness of our proposed SSD method has been verified. Furthermore, our proposed SSD-LC bridge monitoring algorithm can effectively mitigate the divergence problem in baseline drift estimation that occurs with continuous GNSS float solutions in traditional algorithms, which can effectively avoid misjudgments and false alarms in bridge monitoring during GNSS anomalies.&amp;#xD;

Integrated pulmonary index during procedural sedation and analgesia: A cluster-randomized trial.

To evaluate the effectiveness of utilizing the integrated pulmonary index for capnography implementation during sedation administered by nurses. Cluster-randomized trial. Participants were enrolled from the interventional radiology department at an academic hospital in Canada. Nurses were randomized to either enable or disable the Integrated Pulmonary Index feature of the capnography monitor. Procedures were observed by a research assistant to collect information about alarm performance characteristics. The primary outcome was the number of seconds in an alert condition state without an intervention being applied. The number of seconds in an alarm state without intervention was higher in the group that enabled the integrated pulmonary index compared to the group that disabled this feature, but this difference did not reach statistical significance. Likewise, the difference between groups for the total alarm duration, total number of alarms and the total number of appropriate alarms was not statistically significant. The number of inappropriate alarms was higher in the group that enabled the Integrated Pulmonary Index, but this estimate was highly imprecise. There was no difference in the odds of an adverse event (measured by the Tracking and Reporting Outcomes of Procedural Sedation tool) occurring between groups. Desaturation events were uncommon and brief in both groups but the area under the SpO2 90% desaturation curve scores were lower for the group that enabled the integrated pulmonary index. Enabling the integrated pulmonary index during nurse-administered procedural sedation did not reduce nurses' response times to alarms. Therefore, integrating multiple physiological parameters related to respiratory assessment into a single index did not lower the threshold for intervention by nurses. The time it takes to respond to capnography monitor alarms will not be reduced if the integrated pulmonary Iindex feature of capnography monitors is enabled during nurse-administered procedural sedation. Results do not support the routine enabling of the integrated pulmonary index when nurses use capnography to monitor patients during procedural sedation as a strategy to reduce the time it takes to initiate responses to alarms. CONSORT. There was no patient or public contribution. This study was prospectively registered at ClinicalTrials.gov (ID: NCT05068700).

Alarm Management in Intensive Care: Qualitative Triangulation Study.

The high number of unnecessary alarms in intensive care settings leads to alarm fatigue among staff and threatens patient safety. To develop and implement effective and sustainable solutions for alarm management in intensive care units (ICUs), an understanding of staff interactions with the patient monitoring system and alarm management practices is essential. This study investigated the interaction of nurses and physicians with the patient monitoring system, their perceptions of alarm management, and smart alarm management solutions. This explorative qualitative study with an ethnographic, multimethods approach was conducted in an ICU of a German university hospital. Using triangulation in data collection, 102 hours of field observations, 12 semistructured interviews with ICU staff members, and the results of a participatory task were analyzed. The data analysis followed an inductive, grounded theory approach. Nurses and physicians reported interacting with the continuous vital sign monitoring system for most of their work time and tasks. There were no established standards for alarm management; instead, nurses and physicians stated that alarms were addressed through ad hoc reactions, a practice they viewed as problematic. Staff members' perceptions of intelligent alarm management varied, but they highlighted the importance of understandable and traceable suggestions to increase trust and cognitive ease. Staff members' interactions with the omnipresent patient monitoring system and its alarms are essential parts of ICU workflows and clinical decision-making. Alarm management standards and workflows have been shown to be deficient. Our observations, as well as staff feedback, suggest that changes are warranted. Solutions for alarm management should be designed and implemented with users, workflows, and real-world data at the core.

Low-Voltage IGZO Field-Effect Ultraviolet Photodiode

Abstract In the era of Internet of Things (IoTs), an energy-efficient ultraviolet (UV) photodetector (PD) is highly desirable considering the massive usage scenarios such as environmental sterilization, fire alarm and corona discharge monitoring. So far, common self-powered UV PDs are mainly based on metal-semiconductor hetero-structures or p–n heterojunctions, where the limited intrinsic built-in electric field restricts further enhancement of the photoresponsivity. In this work, an extremely low-voltage field-effect UV PD is proposed using a gate-drain shorted amorphous IGZO (a-IGZO) thin film transistor (TFT) architecture. A combined investigation of the experimental measurements and technology computer-aided design (TCAD) simulations suggests that the reverse current (I R) of field-effect diode (FED) is highly related with the threshold voltage (V th) of the parental TFT, implying an enhancement-mode TFT is preferable to fabricate the field-effect UV PD with low dark current. Driven by a low bias of −0.1 V, decent UV response has been realized including large UV/visible (R 300/R 550) rejection ratio (1.9 × 103), low dark current (1.15 × 10−12 A) as well as high photo-to-dark current ratio (PDCR, ∼ 103) and responsivity (1.89 A/W). This field-effect photodiode provides a new platform to construct UV PDs with well-balanced photoresponse performance at a low bias, which is attractive for designs of large-scale smart sensor networks with high energy efficiency.

An Online Digital Imaging Excitation Sensor for Wind Turbine Gearbox Wear Condition Monitoring Based on Adaptive Deep Learning Method.

This paper designed and developed an online digital imaging excitation sensor for wind power gearbox wear condition monitoring based on an adaptive deep learning method. A digital imaging excitation sensing image information collection architecture for magnetic particles in lubricating oil was established to characterize the wear condition of mechanical equipment, achieving the real-time online collection of wear particles in lubricating oil. On this basis, a mechanical equipment wear condition diagnosis method based on online wear particle images is proposed, obtaining data from an engineering test platform based on a wind power gearbox. Firstly, a foreground segmentation preprocessing method based on the U-Net network can effectively eliminate the interference of bubbles and dark fields in online wear particle images, providing high-quality segmentation results for subsequent image processing, A total of 1960 wear particle images were collected in the experiment, the average intersection union ratio of the validation set is 0.9299, and the accuracy of the validation set is 0.9799. Secondly, based on the foreground segmentation preprocessing of wear particle images, by using the watered algorithm to obtain the number of particles in each size segment, we obtained the number of magnetic particle grades in three different ranges: 4-38 µm, 39-70 µm, and >70 µm. Thirdly, we proposed a method named multidimensional transformer (MTF) network. Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) are used to obtain the error, and the maintenance strategy is formulated according to the predicted trend. The experimental results show that the predictive performance of our proposed model is better than that of LSTM and TCN. Finally, the online real-time monitoring system triggered three alarms, and at the same time, our offline sampling data analysis was conducted, the accuracy of online real-time monitoring alarms was verified, and the gearbox of the wind turbine was shut down for maintenance and repair.

High Performance and Stackable Trampoline Like‐Triboelectric Vibration Energy Harvester for In‐Situ Powering Sensor Node with Data Wirelessly Transmitted Over 1000‐m

AbstractAddressing the power supply challenges of wireless sensor nodes is pivotal for advancing the development of the Internet of Things (IoT). This work proposes a high performance and stackable trampoline like‐triboelectric vibration energy harvester (T‐TVEH) that can efficiently harvest ultra‐wideband vibrational energy for in‐situ powering of sensor nodes. The unique structural design and material selection enables T‐TVEH to represent a breakthrough in terms of both working bandwidth and power density compared to recent research efforts of vibration energy harvesting. Specifically, the working bandwidth and the peak power density of T‐TVEH is 192 Hz and 5.9 W m−2, which are higher than previous related studies by 156% and 59.2%, respectively. Based on the excellent performance of the T‐TVEH, a wireless sensor node for monitoring machinery condition is constructed. Temperature, humidity, and frequency information are successfully acquired and transmitted to 1000‐m through the wireless sensor node, which is nine times improved compared to related studies. Meanwhile, it achieves fully self‐powered wireless operation monitoring and abnormal alarm on a real ship's marine diesel engine. Overall, this study proposed an innovative solution for in‐situ power supply of wireless sensor nodes, which has broad application prospects in the field of the IOT.

Critical Care Alarm Fatigue and Monitor Customization: Alarm Frequencies and Context Factors.

Alarm fatigue among nurses working in the intensive care unit has garnered considerable attention as a national patient safety priority. A viable solution for reducing the frequency of alarms and unnecessary noise is intensive care unit alarm monitor customization. A 24-bed cardiovascular and thoracic surgery intensive care unit in a large academic medical center identified a high rate of alarms and associated noise as a problem contributing to nurse alarm fatigue. An alarm monitor quality improvement project used both alarm frequency and nurse surveys before and after implementation to determine the effectiveness of interventions. Multimodal interventions included nurse training sessions, informational flyers, organizational policies, and an alarm monitor training video. Unexpected results inspired an extensive investigation and secondary analysis, which included examining the data-capturing capabilities of the alarm monitors and the impact of context factors. Alarm frequencies unexpectedly increased after the intervention. The software data-capturing features of the alarm monitors for determining frequency did not accurately measure nurse interactions with monitors. Measured increases in patient census, nurse staffing, and data input from medical devices from before to after the intervention substantially affected project results. Alarm frequencies proved an unreliable measure of nurse skills and practices in alarm customization. Documented changes in context factors provided strong anecdotal evidence of changed circumstances that clarified project results and underscored the critical importance of contemporaneous collection of context data. Designs and methods used in quality improvement projects must include reliable outcome measures to achieve meaningful results.

A temperature monitoring and fever alarm system composed of printing flexible electronics

A temperature monitoring and fever alarm system composed of printing flexible electronics

Design of high-precision displacement safety monitoring and three-dimensional spatial alarm system for Beidou based on intelligent algorithms

The purpose of this study is to design a system that can monitor the displacement changes of engineering structures in real time and provide timely alarms when abnormal displacement is detected, in order to provide efficient safety monitoring and early warning functions and reduce the safety risks of engineering structures. The study adopted a combination of intelligent algorithms and the Global Satellite Navigation System (Beidou). By installing high-precision displacement sensors, monitor the displacement changes of the engineering structure. Then, intelligent algorithms are used to analyze and process the data collected by the sensors in real time, in order to extract the characteristics of displacement changes. At the same time, combined with the positioning data of the BeiDou navigation system, high-precision displacement monitoring is achieved. When the monitoring results exceed the set threshold, the system will automatically trigger an alarm mechanism to promptly notify relevant personnel for processing. Through experiments and comparative analysis, the Beidou high-precision displacement safety monitoring and 3D spatial alarm system based on intelligent algorithms designed in this study can effectively monitor and alarm displacement changes. The system has the characteristics of high accuracy, real-time performance, and reliability, which can timely detect displacement anomalies and provide reliable safety monitoring and warning functions.

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