Fault Detection Using Limit Checking: A Brief Introductory Review

Miljković, Dubravko

doi:10.23919/mipro52101.2021.9596786

Cited by 12 publications

(14 citation statements)

References 12 publications

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“…(f ) Current sensor: Measurement of current is essential for the appropriate working of electronic gadgets like Hitchhiker's guide to successful remote sensing deployments in Mongolia [35] e author builds an economic air quality sensor that gathers real-time data from varied locations. e paper presents the implementation and challenges faced in the deployment of the same Unplugged sensors were not anticipated and considered in their design Fault detection using limit checking: a brief introductory review [36] is In the smart setup presented in this paper, all the devices and sensors deployed inside a smart day care are connected to the microcontroller, which facilitates centralized control over them. e SDRAM inside the microcontroller acts as a data storage for readings of all the devices.…”

Section: (Iii) Trend Checking Y D � Y-y Pmentioning

confidence: 99%

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SWOT: A Hybrid Hardware-Based Approach for Robust Fault-Tolerant Framework in a Smart Day Care

Sharma¹,

Gupta

et al. 2022

Security and Communication Networks

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Internet of Things (IoT) has made its imprint on every part of the globe today. Offices, households, factories, industries, agriculture, and day cares, among other places, have all upgraded to their innovative version. It has propounded great potential in various real-world applications. The topical trends in the adoption of IoT have also highlighted the challenges associated with the performance of IoT devices. IoT devices require continuous monitoring for any performance degradation. A bibliometric analysis of 587 papers is undertaken on the Scopus database to spot the increasing interest of researchers in fault detection in IoT. A smart system’s or an IoT-based space’s usability, efficiency, and performance are all built on a fault-tolerant approach and interruption-free smooth operations. The investigation was carried out based on the literature to determine the need for a pro-fault detection system in an IoT-enabled day care. Kids’ security and safety are highly dependent on the hassle-free working of smart devices. It’s overhead to carry out device tracking manually along with demanding kids. This issue needs to be addressed to uphold the smart day care’s trustworthiness. A pro-fault detection approach can be applied to resolve the aforementioned issue to enhance the smart day care’s performance and efficiency. This paper proposes SWOT, a novel hybrid hardware-based approach in an IoT-based day care to safeguard the proper working of all IoT devices deployed. It screens every single appliance associated with a smart day care to detect the faulty appliance beforehand. The solution will assist the day care staff in providing the best care and security to their kids without any overhead. SWOT evaluation proved that it is an economical and efficient approach in detecting faults, accurately, swiftly, and with a low false alarm ratio.

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Section: (Iii) Trend Checking Y D � Y-y Pmentioning

confidence: 99%

“…Comparison with existing work on hardware-based approaches of fault detection[30][31][32][33][34][35][36].…”

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confidence: 99%

SWOT: A Hybrid Hardware-Based Approach for Robust Fault-Tolerant Framework in a Smart Day Care

Sharma¹,

Gupta

et al. 2022

Security and Communication Networks

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“…20 Fault detection refers to the process of identifying the presence of deviations from normal operating conditions within the system or process of interest. 21 Clearly, early detection of process faults is crucial to ensure safety and reliability of chemical processes. Based on research conducted on FDD post-Industrial Revolution 3.0, fault-detection methods can be classified broadly into: (i) analytical or model-based;…”

Section: Overview Of Fault Diagnosis and Ra Methods In Literaturementioning

confidence: 99%

Multivariate alarm systems to recognize rare unpostulated abnormal events

Sudarshan,

Seider,

Patel

et al. 2023

AIChE Journal

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Most chemical and manufacturing plants have safety/reliability systems in place that are well equipped to handle commonly occurring postulated abnormal events, but often prove to be ineffective in predicting highly unanticipated and randomly occurring unpostulated abnormal events. In this paper, an advisory system is developed to analyze and recognize such events, consisting of novel, multivariate alarm systems and response actions introduced using process modeling and path‐sampling for unpostulated abnormal events. It augments existing safety/reliability systems, suggesting actions when unanticipated abnormal events are approached. Forward‐flux sampling (FFS), developed to discover rare molecular dynamics pathways, is applied. With an approximate process model, for an exothermic continuous‐stirred tank reactor using a perturbed feed concentration, the FFS algorithm is applied to identify rare trajectories between high‐conversion and low‐conversion steady‐states, with key process variables saved at various “crossing points.” Then, committer probabilities, , are computed at each crossing point, yielding a mathematical model that expresses as a function of the key process variables; that is, the reactor temperature (T), cooling‐water flow rate (), and cooling‐water temperature (), selected as choices for the primary alarm variables. For these, alarm thresholds; that is, L—low, LL—low low, and LLL—low low low, are suggested by computing their critical ranges, given the ranges for every alarm threshold.

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“…It is considered a classi cation task that entails classifying a new observation as an inlier (normal observation) or an outlier (abnormal observation) [28]. Outlier detection and Novelty detection are used interchangeably in literature [30]. In this work, we considered them synonymous with each other as long as the de nition of abnormal data can be de ned as a novelty or outlier by comparing it to normal cleaned data.…”

Section: Outlier and Novelty Detectionmentioning

confidence: 99%

Data-Self-Check: A framework for automated Data Quality Assessment of Malaria routine surveillance data designed for DHIS2 using Machine Learning techniques

Kuderha,

Kala,

Mungungu

et al. 2023

Preprint

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Background The extraction of valuable insights from malaria routine surveillance data is highly dependent on the processes and tools used to collect, curate, store, analyse, and disseminate that data and the essential information obtained from it. The main challenge is to ensure good quality of data collected at the local level. In this work, we have proposed a new framework for Data Quality Assessment designed for DHIS2 using Machine Learning techniques. Methodology The data used in this study was extracted from the DHIS2 Platform for 8 districts of Mopti in Mali for 2016 and 2017. We carried out three data preprocessing tasks. We developed four models based on machine learning algorithms for local and global outlier detection, trained and validated on malaria surveillance routine data extracted from DHIS2. We used five main evaluation metrics to assess the performance of the developed models. The proposed framework's design will consider the steps of Report-Accuracy Assessment and Cross-Checks presented in the Malaria Routine Data Quality Assessment Tool (MRDQA Tool). Results For the case of random errors (outliers), we found that all four models did not reach an AUC value of 60%. Despite the low value of the AUC metric, the precision scores reached values more than 90%. As the AUC metric represents the overall performance of the models, we can say that random errors do not leave enough patterns in the malaria routine surveillance data to be detected. In contrast, detecting systematic errors has good value for performance metrics (87% AUC and 98% precision. This is the case for systematic errors with the same structures (same consecutive months and same columns) in two different districts and systematic errors with different structures at the same time period in two differents districts. Conclusion The machine learning models integrated into the proposed framework perform well in detecting random and systematic errors (global or local outliers) in the malaria routine surveillance data. Only consistent and accurate data will be stored in the DHIS system with the proposed framework. This will maximise the potential to extract actionable knowledge from malaria routine surveillance data to make better informed-decision.

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Fault Detection Using Limit Checking: A Brief Introductory Review

Cited by 12 publications

References 12 publications

SWOT: A Hybrid Hardware-Based Approach for Robust Fault-Tolerant Framework in a Smart Day Care

SWOT: A Hybrid Hardware-Based Approach for Robust Fault-Tolerant Framework in a Smart Day Care

Multivariate alarm systems to recognize rare unpostulated abnormal events

Data-Self-Check: A framework for automated Data Quality Assessment of Malaria routine surveillance data designed for DHIS2 using Machine Learning techniques

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