Alarm Management System Research Articles

Crying wolf, alarm safety and management in paediatrics: A scoping review.

To provide a contemporaneous evidentiary overview of neonatal and paediatric studies investigating alarm-related patient safety and alarm system management. Furthermore, to describe how clinical alarm burden is captured and reported, to identify clinical devices that contribute to alarm burden, to explore alarm-related and patient safety measures and terminologies and to review alarm management initiatives. Scoping review. A systematic search of PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, Scopus and EBSCOhost was conducted from 2013 to 2023 using predetermined search terms, index terms, medical subject headings and truncation. Observational and qualitative studies with neonatal and paediatric populations reporting monitoring and alarm practices; and interventional studies reporting the success of alarm safety interventions were included. The quality of the included studies was assessed using the mixed methods appraisal tool. The search yielded 37 studies of acceptable quality. The majority explored alarm burden associated with physiological monitoring (n = 35; 95%). Alarm definitions were reported in 46% (n = 17) of studies, and commonly included what constituted actionable and non-actionable alarms. While 32% (n = 12) of studies considered alarms in relation to clinical outcomes surrounding patient safety, clinician response to alarms was only reported in 19% (n = 7) of studies. Alarm and monitoring interventions were assessed in 51% (n = 19) of included studies, with categorization into six domains: changing alarm parameters, clinician education, communication and planning, technology, alarm ordering and standardization or guidelines. This review has demonstrated the enormity of alarms in clinical settings, heterogeneity of alarm definitions and outlined interventions associated with alarm burden and patient safety. Strategies to ensure appropriate alarm limits are set and clinicians are empowered through education to recognize and respond appropriately to alarms can maximize patient safety. This review adheres to the preferred reporting items for systematic reviews and meta-analysis protocols extension for scoping reviews. No patient or public contribution.

Machine Learning-Supported Designing of Human–Machine Interfaces

The design and functionality of the human–machine interface (HMI) significantly affects operational efficiency and safety related to process control. Alarm management techniques consider the cognitive model of operators, but mainly only from a signal perception point of view. To develop a human-centric alarm management system, the construction of an easy-to-use and supportive HMI is essential. This work suggests a development method that uses machine learning (ML) tools. The key idea is that more supportive higher-level HMI displays can be developed by analysing operator-related events in the process log file. The obtained process model contains relevant data on the relationship of the process events, enabling a network-like visualisation. Attributes of the network allow us to solve the minimisation problem of the ideal workflow–display relation. The suggested approach allows a targeted process pattern exploration to design higher-level HMI displays with respect to content and hierarchy. The method was applied in a real-life hydrofluoric acid alkylation plant, where a proposal was made about the content of an overview display.

Robust alarm design strategy for medical devices: Application to air-in-line detection and occlusion management

Alarm fatigue in the hospital environment is a recurring problem that can be solved through technical, human, or organizational considerations. The use of technical factors to get a robust alarm management system is illustrated here by two case studies related to air-in-line detection in peritoneal dialysis and insulin pump occlusion management. Air-in-line sensors are usually very sensitive to the presence of small bubbles stuck on tubing and may therefore deliver false alarms. To provide a reliable estimate of the cumulated air volume pumped an IR sensor technology that is insensitive to small bubbles was implemented and tested. Another strategy was used to limit false occlusion alarms in an insulin patch pump. The piezoelectric actuator of this micropump was servo-controlled to the pressure sensor signal to allow insulin to flow into the patient despite the presence of a partial occlusion. The false alarm rate is thus reduced by using a self-adaptive occlusion detection threshold that has been evaluated using numerical simulations.

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.

Presenting a soft sensor for monitoring and controlling well health and pump performance using machine learning, statistical analysis, and Petri net modeling.

Groundwater resources play a key role in supplying urban water demands in numerous societies. In many parts of the world, wells provide a reliable and sufficient source of water for domestic, irrigation, and industrial purposes. In recent decades, artificial intelligence (AI) and machine learning (ML) methods have attracted a considerable attention to develop Smart Control Systems for water management facilities. In this study, an attempt has been made to create a smart framework to monitor, control, and manage groundwater wells and pumps using a combination of ML algorithms and statistical analysis. In this research, 8 different learning methods and regressions namely support vector regression (SVR), extreme learning machine (ELM), classification and regression tree (CART), random forest (RF), artificial neural networks (ANNs), generalized regression neural network (GRNN), linear regression (LR), and K-nearest neighbors (KNN) regression algorithms have been applied to create a forecast model to predict water flow rate in Mashhad City wells. Moreover, several descriptive statistical metrics including mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE), and cross predicted accuracy (CPA) are calculated for these models to evaluate their performance. According to the results of this investigation, CART, RF, and LR algorithms have indicated the highest levels of precision with the lowest error values while SVM and MLP are the worst algorithms. In addition, sensitivity analysis has demonstrated thatthe LR and RF algorithms have produced the most accurate modelsfor deep and shallowwells respectively.Finally, a Petri net model has been presented to illustrate the conceptual model of the smart framework and alarm management system.

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.

Optimisation of clinical workflow and monitor settings safely reduces alarms in the NICU.

AimTo address alarm fatigue, a new alarm management system which ensures a quicker delivery of alarms together with waveform information on nurses' handheld devices was implemented and settings optimised. The effects of this clinical implementation on alarm rates and nurses' responsiveness were measured in an 18‐bed single family rooms neonatal intensive care unit (NICU).MethodsThe technical implementation of the alarm management system was followed by clinical workflow optimisation. Alarms and vital parameters from October 2017 to December 2019 were analysed. Measures included monitoring alarms, nurses' response to alarms and time spent by patients in different saturation ranges. A survey among nurses was performed to evaluate changes in alarm rate and use of protocols.ResultsA significant reduction of monitoring alarms per patient days was detected after the optimisation phase (in particular for SpO2 ≤ 80%, P < .001). More time was spent by infants within the optimal peripheral oxygen saturation range (88% < SpO2 < 95%, P < .001). Results from the surveys showed that false alarms are less likely to cause an inappropriate response after the optimisation phase.ConclusionThe implementation of an alarm management solution and an optimisation programme can safely reduce the alarm burden inside of the NICU environment.

Supporting Telecommunication Alarm Management System With Trouble Ticket Prediction

Fault alarm data emanated from heterogeneous telecommunication network services and infrastructures are exploding with network expansions. Managing and tracking the alarms with trouble tickets using manual or expert rule-based methods have become challenging due to increase in the complexity of alarm management systems and demand for deployment of highly trained experts. As the size and complexity of networks hike immensely, identifying semantically identical alarms, generated from heterogeneous network elements from diverse vendors, with data-driven methodologies, has become imperative to enhance efficiency. In this article, data-driven trouble ticket prediction models are proposed to leverage alarm management systems. To improve performance, feature extraction, using a sliding time window and feature engineering, from related history alarm streams, is also introduced. The models were trained and validated with a data set provided by the largest telecommunication provider in Italy. The experimental results showed the promising efficacy of the proposed approach in suppressing false positive alarms with trouble ticket prediction.

Biological Nanosensors On Network for Diabetes Control With Alert Emission for Users

Introduction: This paper is the product of the research "Biological network nanosensors for diabetes control with the issuance of alerts to users", developed in the technological faculty of the Francisco José de Caldas District University carried out during 2018 and 2019. Problem: Diabetes is a disease that affects most of the adult population and not having proper control can lead to health complications that lead to early deaths. Objective: To propose a theoretical prototype for communication between a nano network and the applications that work to monitor networks, which issues an alarm to users who have diabetes and thus have better control of their disease. Methodology: Research articles, books and forums published from 2010 onwards (some exceptions are found in the references) were used, along with official documentation of tools such as Netcool, Nagios and Pandora. Results: 66 sources were found including papers, monographs, forums and others that complied with the research guidelines. In addition, numerous applications were found that can be implemented in the proposed theoretical prototype. Conclusion: A theoretical prototype is proposed for communication between a nano network and an alarm management system for the end user. This prototype use current technologies and specialized tools. Originality: The article presents an innovative theoretical prototype, designed to solve a problem in the health sector, that makes use of emerging technologies. Limitations: The internet of nano-things, oriented to biological processes, is an emerging technology. At the time of writing this paper, there are no real prototypes or tools to simulate nano networks in conjunction with biotechnology.

Pattern mining in alarm flood sequences using a modified PrefixSpan algorithm

Proper monitoring of performance of an alarm system throughout its life cycle is an important factor in safety and reliability of industrial plants. Complexity and extent of modern industrial plants and poor design and management of alarm systems, have increased the importance of monitoring of alarm systems. Alarm floods, defined as a large number of alarms triggered in a short interval, is one of the problems that modern complexes are facing regularly. Many researchers have been focusing on this issue both in academia and industry. One approach to deal with alarm flood is analyzing alarms triggered in different floods and finding similar patterns. The identified patterns could help in locating the root cause of an alarm flood. In this paper a modified PrefixSpan sequential pattern recognition algorithm is used to find alarm patterns in different floods. The effectiveness of the algorithm is demonstrated with real alarm floods from a natural gas processing plant.

Validation of an Adaptive Alarm Interface Design for Digital Control Panels

ABSTRACTThis study proposes an adaptive alarm interface, which is supposed to have the good features of both alarm–tile interfaces that are typical in nuclear power plants (NPPs), and alarm–bar interfaces that use changing bars to indicate the current condition of an NPP and its abnormalities. Depending on the situation, the alarm management system will decide whether the alarm–tile or the alarm–bar interface should be presented to operators. To validate the usability of the adaptive alarm interface, it was compared against both the alarm–tile and alarm–bar interfaces through laboratory experiments with 45 participants. The results demonstrated that the adaptive alarm interface not only allowed the understanding of parameter trending, which was a distinguishing feature of the alarm–bar interface, but also improved the alarm detection, which was one advantage of the alarm–tile interface. However, the situation awareness level of the adaptive alarm interface was insufficient compared to the alarm–tile and bar interfaces possibly due to distraction from interface swapping.

Research on the Clinical Alarm Management Mechanism Based on Closed-loop Control Theory

This paper proposes a clinical alarm management system based on the theory of the closed loop control. The alarm management mechanism can be divided into the expected standard, improving execution rule, rule execution, medical devices with alarm functions, results analysis strategy and the output link. And, we make relevant application and discussion. Results showed that the mechanism can be operable and effective.

Sequence Mining Based Alarm Suppression

Despite the high-pace improvement of industrial process automation, the management of abnormal events still requires human actions. Alarm systems are becoming crucial in providing situation-specific information to the decreasing number of operators. The key role of an alarm management system is to ensure that only the currently significant alarms are annunciated. The design of alarm suppression rules requires the systematic analysis of the process and its control system. We give an overview of the recently developed data-driven techniques and show that the widely applied correlation-based methods utilize a static view of the system. To provide more insight into the process dynamics and represent the temporal relationships among faults, control actions, and process variables, we propose of a multi-temporal sequence mining-based algorithm. The methodology starts with the generation of frequent temporal patterns of the alarm signals. We transform the multi-temporal sequences into Bayes classifiers. The obtained association rules can be used to define the alarm suppression rules. We analyze the data set of a laboratory-scale water treatment testbed to illustrate that multi-temporal sequences are applicable for the description of operation patterns. We extended the benchmark simulator of a vinyl acetate production technology to generate easily reproducible results and stimulate the development of alarm management algorithms. The results of detailed sensitivity analyses confirm the benefits of the application of temporal alarm suppression rules, which are reflecting the dynamical behavior of the process.

Context-Aware Recommendation for Industrial Alarm System

The selection of alerts within an industrial plant is not an easy task, due to the huge amount of variables involved. By this fact, it is necessary to have the means to easiest the acquirement of necessary information throughout the extraction of the knowledge in order to assist operator in the decision-making process. In this sense, a context-sensitive recommendation system can identify a situation from patterns of events. From the recognition of a situation in a particular ontological context, a proactive approach based on knowledge seems to be more effective for this task. The industrial alarm management system, generally used in supervision systems and process controls, has a problem related to the cognitive overload of their operators. Unfortunately, this occurs mainly during perturbations in the process. In this context, this work aims to investigate and develop methods to adopt proper actions sensitive to the present situation in an industrial alarm management system through semantic web technology and machine learning techniques. This problem involves questions related to the way of obtaining the context data, the context model that describes the situation and the way in which the context data of the plant are related, which allows to infer a situation. In this sense, it is proposed the appliance of data mining techniques to obtain knowledge of the process, taken into consideration an adaptive interface that makes non-intrusive recommendations and help the operator in an industrial plant. To combine deterministic knowledge with probabilistic knowledge, a Bayesian Networks is used. For the concept evaluation, a case study was conducted based on a real historical database, which shows sound and promising results.

ALACA: A platform for dynamic alarm collection and alert notification in network management systems

SummaryMobile network operators run Operations Support Systems that produce vast amounts of alarm events. These events can have different significance levels and domains and also can trigger other ones. Network operators face the challenge to identify the significance and root causes of these system problems in real time and to keep the number of remedial actions at an optimal level, so that customer satisfaction rates can be guaranteed at a reasonable cost. In this paper, we propose a scalable streaming alarm management system, referred to as Alarm Collector and Analyzer, that includes complex event processing and root cause analysis. We describe a rule mining and root cause analysis solution for alarm event correlation and analyses. The solution includes a dynamic index for matching active alarms, an algorithm for generating candidate alarm rules, a sliding window–based approach to save system resources, and a graph‐based solution to identify root causes. Alarm Collector and Analyzer is used in the network operation center of a major mobile telecom provider. It helps operators to enhance the design of their alarm management systems by allowing continuous analysis of data and event streams and predict network behavior with respect to potential failures by using the results of root cause analysis. We present experimental results that provide insights on performance of real‐time alarm data analytics systems.

Big data analytics for network and service management

Big data analytics for network and service management

Deploying telemonitoring services in an integrated care model

Introduction: The prevalence of congestive heart failure is considerably high affecting 10% of the population older than 70 years old. Due to the population ageing and the increased survival to acute cardiac diseases, CHF is becoming more common, representing a public health problem. In consequence, it will become increasingly difficult to maintain the quality of care of patient suffering from CHF.In this context, home telemonitoring appears to be a promising alternative to allow healthcare professionals to follow up patient´s health status more closely and then facilitating early detection of worsening symptoms. However, the effectiveness of these interventions in real conditions has not yet been sufficiently tested, since the implementation of telemonitoring services is considerably complex. Frequently telemonitoring studies do not overcome the pilot phase and do not reach the expected success when it comes to scale and place the intervention in routine practice.The aim of the project is to define and up-scale cost-effective and sustainable telemonitoring services for patients with CHF.Definition of the organizational model and the ICT infrastructure of the telemonitoring intervention have been deeply discussed in an inter-organizational and multidisciplinary work group, reaching consensus on how to implement the services within an integrated organizational model.Both the care pathway and the technological solution have been defined in order to design a sustainable service. On one hand, the organizational model is adaptable and flexible enough to be tailored to new contexts allowing new organizations to incorporate the service into their routine practice. On the other hand, a huge effort has been done to integrate distinct technological platforms which permit that the telemonitoring data flow from patient´s home to the EHR.Short description of practice change implemented: The patient who meets the inclusion criteria and signs the informed consent is enrolled in the intervention. The technical provider is responsible for installing devices for the measurement of the heart rate, blood pressure, pulse-oxymetry and weight at patient's home within a week after recruitment. In addition, a personal alarm device for 24/7 real-time emergency detection is installed. The patient is trained in using the telemonitoring and telecare devices.Following the clinician's prescription, the patient will routinely transmit his/her parameters at least once per week. The telemonitoring devices collect and send the data wirelessly to the gateway located at his/her home. The gateway device transmits the data collected by the patient to the alarm management system of the Telecare Centre.The operator of the Telecare Centre checks the data sent by the patient and activates the predefined protocol agreed by a group of professionals, including clinicians, general practitioners, nurses, general directors and representatives of the social area. When clinical parameters are out of range, the operator first verifies the alarm situation by a phone call to the patient. If the alarm is validated, the operator triggers the protocol depending on the severity of the situation. The operator contacts the eHealth Centre composed of nurses; they then solve the alarm on their own, notify the general practitioner or the specialist, or activate Emergency Department.If the patient uses the 24/7 real-time alarm device provided, the Telecare Centre's operator can contact the Emergency Department and/or social services. In addition, the Telecare Centre also solves any technical problems arising in the use of devices.Key findings: 150 patients have been recruited from May 2014 to June 2015 and each patient has been followed up to 12 months.Preliminary results:42.25% alarms are technicalThe remaining 57.75%:- 79.7% do not activate alarm- 20.3% out of range (54.8% false positive, 30.3% true alarms, 14.7% not confirmed)Healthcare activities upon alarms:- 84,1%: solved by eHealth Centre- 5,1%: solved by primary care- 10,7%: solved in secondary careHigh patient and caregiver satisfactionHighlights:- The organizational model using telemonitoring services model has to be well adapted to the routine practice.- Complete telemonitoring service required (device installation, maintenance and user training)- Need of both administrative management and guarantee of good quality of telemonitored data (avoiding false alarms).- Primary Care is responsible for proactive control of the patients- Distributed leadership is crucial from the implementation process perspective (clinical, managerial and methodological)- Need to share midterm results and collect feedback from front-line professionals to detect improvement areasConclusions: We have seen that project objectives have to be completely aligned with the central organization´s strategic plan to ensure the sustainability of the services. Using corporative technological platforms and existing resource re-organization also contribute to the maintenance of the service in long term.

Spatial Interpretive Structural Model Identification and AHP-Based Multimodule Fusion for Alarm Root-Cause Diagnosis in Chemical Processes

An alarm system plays a fundamental role in safety, quality, and economic profits of chemical processes. Alarm root-cause diagnosis is an essential part in alarm system management to monitor and locate the abnormalities. Because of the high integration and complexity in modern large scale industrial processes, a simplex structure model and monolithic monitoring methods cannot always meet the requirement of alarm root-cause diagnosis. This work introduces a framework to identify the alarm root cause and visualize the abnormality propagation path. A novel spatial interpretive structural model (SISM) is proposed to represent the hierarchical organization of space and show the causal relationships on different levels of granularity. In SISM, multiple spatial unit blocks are obtained by process decomposition based on the process flow diagram. Each block has one or more different variables. The hierarchical internal causal relationships in each individual block and external causal relationships between any two ...

Comprehensive alarm information processing technology with application in petrochemical plant

During the abnormal plant conditions, too much information is produced due to momentary plant excursions above alarm limits. This flood of information impedes correct interpretation and correction of plant conditions by the operator. Existing techniques for the design of alarm systems mostly have weak ability to handle complex hazard scenarios and increase the probability of larger safety issues. In this paper, a comprehensive alarm information processing (AIP) technology is introduced, called multi-round alarm management system (MRAMS), including several processing strategies: AIP based on single sensor, AIP based on sensor group, root cause diagnosis based on Bayesian network, sensor fault judgment method and false alarm inhibition method. In case studies, both simulation experiment and pilot application on a real petrochemical plant are presented. Results indicate the MRAMS is helpful in improving the accuracy of correctly diagnosing the root causes and hence avoiding false and redundant alarms. By adopting this new technology, the safe and reliable operation of the plant can be achieved, and the economic loss brought by improper alarms can be reduced.

Alarm fatigue and its influence on staff performance

An alarm is a warning of an approaching situation which requires a response. The Emergency Care Research Institute considered alarm hazard as the number one health technology hazard for the years 2012 through 2014. In response, The Joint Commission set a standard for all hospitals in the United States to assess alarm fatigue in their monitoring process and to develop a systematic, coordinated approach to clinical alarm system management. In order to comply with this requirement, a working definition of alarm fatigue is necessary. This observational study undertook the objective of defining alarm fatigue, measuring it and exploring its role in performance deterioration. A conceptual model was developed considering the significance of working conditions and staff individuality on alarm fatigue and, consequently, alarm fatigue on staff performance. The results show that in general, performance deterioration is actually influenced by a combination of alarm fatigue, working conditions and staff individuality. In fact, in the case of nurses and response time, alarm fatigue plays no role, only working conditions and staff individuality. These findings suggest that the role of alarm fatigue as a health hazard in the clinical environment should be reevaluated.