Reliability Of Monitoring System Research Articles

ERIRMS Evaluation of the Reliability of IoT-Aided Remote Monitoring Systems of Low-Voltage Overhead Transmission Lines.

Researchers have studied instances of power line technical failures, the significant rise in the energy loss index in the line connecting the distribution transformer and consumer meters, and the inability to control unauthorized line connections. New, innovative, and scientific approaches are required to address these issues while enhancing the reliability and efficiency of electricity supply. This study evaluates the reliability of Internet of Things (IoT)-aided remote monitoring systems specifically designed for a low-voltage overhead transmission line. Many methods of analysis and comparison have been employed to examine the reliability of wireless sensor devices used in real-time remote monitoring. A reliability model was developed to evaluate the reliability of the monitoring system in various situations. Based on the developed models, it was found that the reliability indicators of the proposed monitoring system were 98% in 1 month. In addition, it has been proven that the reliability of the system remains high even when an optional sensor in the network fails. This study investigates various IoT technologies, their integration into monitoring systems, and their effectiveness in enhancing the reliability and efficiency of electrical transmission infrastructure. The analysis includes data from field deployments, case studies, and simulations to assess performance metrics, such as accuracy, latency, and fault detection capabilities.

An asset management modelling framework for wind turbine blades considering monitoring system reliability

By incorporating information about asset condition from a monitoring system, engineers can utilize asset management models to manage maintenance activities on wind turbine blades throughout their lifespan. This can lower operating and maintenance costs and increase the life of the blades. The asset management model relies on the monitoring system as a source of information, however, commonly the reliability of the monitoring system is not considered. This paper presents a wind turbine blade asset management Petri net (PN) model that covers the blade asset management process, including degradation, inspection, condition monitoring (CM), and maintenance processes. The paper proposes two contributions. Firstly, while taking into account detailed industry guidelines, the developed model can forecast the future blade condition for a given asset management strategy. Secondly, it investigates the impact of the reliability of the monitoring system on the asset management modelling results. With the aid of the developed model, the number of repair actions and probability distributions of blade condition discovery time are obtained. In addition, the PN gives an indication of how misreporting (underestimation and overestimation) occurs and the extent of the misreporting. The simulation results illustrate the degree of uncertainty introduced into the monitoring results by the reliability of the monitoring system and, consequently, the extent to which this factor influences the maintenance strategies. The proposed model can be used to support asset management decisions when monitoring system performance degrades.

RETRACTED: Enhancing Data Quality Management in Structural Health Monitoring through Irregular Time-Series Data Anomaly Detection Using IoT Sensors

The importance of monitoring in assessing structural safety and durability continues to grow. With recent technological advancements, Internet of Things (IoT) sensors have garnered attention for their complex scalability and varied detection capabilities, becoming essential devices for monitoring. However, during the data collection process of IoT sensors, anomalies arise due to network instability, sensor noise, and malfunctions, degrading data quality and compromising monitoring system reliability. In this study, Interquartile Range (IQR), Long Short-Term Memory Autoencoder (LSTM-AE), and time-series decomposition were employed for anomaly detection in Structural Health Monitoring (SHM) processes. IQR and LSTM-AE produce irregular patterns; however, time-series decomposition effectively detects such anomalies. In road monitoring influenced by weather and traffic, the time-series decomposition approach is expected to play a crucial role in enhancing monitoring accuracy.

A physics-embedded deep-learning framework for efficient multi-fidelity modeling applied to guided wave based structural health monitoring

Health monitoring of structures using ultrasonic guided waves is an evolving technology with potential applications in monitoring pipelines, civil bridges, and aircraft components. However, the sensitivity of guided waves to external parameters affects the reliability of monitoring systems based on them. These influencing factors and experimental related factors cannot be perfectly modeled, which give rise to the discrepancy between numerical simulations and experimental measurements. Therefore, it is important to address this inevitable discrepancy and generate close-to-experiment simulations. In this work, we present a deep learning-based Digital Twin framework containing multi-fidelity modeling to reduce the discrepancy between measurements and simulations and a deep generative model to generate close-to-experiment guided wave responses by harnessing the vital characteristics of the two sources. These realistic simulations (close to experiment) can then be used in assessing the reliability of health monitoring system by generating probability of detection curves. Furthermore, they can also be used for augmenting the training data for a machine learning algorithm. We use a measurement dataset corresponding to crack propagation and simulations to validate the proposed framework. The results show that the discrepancy is indeed reduced to a great extent, furthermore, we also show that this framework enables the computation of probability of detection from close-to-experiment data as a direct consequence of rapid generation of realistic simulations.

Power Supply Issues in E-health Monitoring Applications

Abstract. Recent years have seen a rapid growth in the development of e-health systems for the continuous remote patient monitoring of physiological data such as temperature, heart rate (HR), Blood pressure (BP), oxygenation (SpO2), respiration and glucose levels. The roll-out of such schemes show promise in delivering improvements in patient care while at the same time reducing both the demand for resources and the financial burden on healthcare systems. These wearable monitoring systems are used to monitor, log and transmit patient data to a central health authority. Depending on the patient, it is often critical that the monitoring system reliability is high to deliver the appropriate patient care and ensure patient safety. However, since wearable systems are solely dependent on battery power, continuous monitoring will rapidly deplete the battery energy making the system prone to failure. In this paper, methods to reduce power consumption will be discussed using the example of prosthetic socket temperature monitoring data

Automatic microseismic signal classification for mining safety monitoring using the WaveNet classifier

AbstractMicroseismic monitoring is a promising method for the safety monitoring of underground mines. However, it is crucial to isolate microseismic signals related to the collapse of a mine from others for successful monitoring because the monitoring system records various signals. Recently, deep learning‐based classification techniques have achieved high performance in such data classification problems. In this context, we develop an automatic signal classification technique using the modified WaveNet classifier. The main characteristic of the WaveNet structure is its ability to extract features at various frequencies from very long time‐series data, and such an advantage makes the WaveNet suitable for seismic data processing. The data imbalance problem coming from the safe condition of the monitoring target is solved by augmenting the training data with those acquired from another mine and employing class weighting. After training, an optimal classifier is chosen considering the loss function, accuracy and Fβ score. The optimal classifier shows very high accuracy and excellent performance for the test data prediction. Compared to the random forest model and another one‐dimensional convolutional neural network–based network, the suggested classifier has higher reliability in predicting microseismic signals. Even though the proposed WaveNet model has a much more complex structure than the random forest model, the actual application examples demonstrate that the proposed model achieves high efficiency without any preprocessing. The automatic signal classifier developed in this study can be directly applied to various safety monitoring problems, not only mines, to improve the efficiency and reliability of monitoring systems.

A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles

Electricity consumption patterns reveal energy demand behaviors and enable strategY implementation to increase efficiency using monitoring systems. However, incorrect patterns can be obtained when the time-series components of electricity demand are not considered. Hence, this research proposes a new method for handling time-series components that significantly improves the ability to obtain patterns and detect anomalies in electrical consumption profiles. Patterns are found using the proposed method and two widespread methods for handling the time-series components, in order to compare the results. Through this study, the conditions that electricity demand data must meet for making the time-series analysis useful are established. Finally, one year of real electricity consumption is analyzed for two different cases to evaluate the effect of time-series treatment in the detection of anomalies. The proposed method differentiates between periods of high or low energy demand, identifying contextual anomalies. The results indicate that it is possible to reduce time and effort involved in data analysis, and improve the reliability of monitoring systems, without adding complex procedures.

Drift Calibration Using Constrained Extreme Learning Machine and Kalman Filter in Clustered Wireless Sensor Networks

Wireless sensor networks (WSNs) have been increasingly applied for environmental monitoring in recent years. However, the sensor data drift is a serious issue affecting the reliability of monitoring system. Based on the assumption that the neighboring nodes have correlated measurements, this paper presents a novel algorithm using constrained extreme learning machine and Kalman filter (CELM-KF) for tracking and calibrating drift of sensor data. CELM-KF has two phases: training phase and calibrating phase. In the training phase, the wavelet denoising method is used for data preprocessing. Then the cluster head trains the model of the constrained extreme learning machine (CELM) using the measurements of its neighbors to obtain the prediction data of the target sensor. In the calibrating phase, we track and correct the data drift of target sensor using Kalman filter. To evaluate the performance of CELM-KF, simulation experiments on different datasets are conducted, and two parameters including decision coefficient and mean square error (MSE) of CELM-KF are compared with those of existing algorithms. The simulation results show that CELM-KF can successfully calibrate the sensor data drifts.

Computer-aided design of ECG telemetry systems for online cardiac monitoring

Telemetry is rapidly becoming standard practice for clinical studies. Remote monitoring may be performed at multiple locations of subjects over WiFi or 3G cell phone networks. Nevertheless, the accuracy and reliability of monitoring systems have still to be improved for clinical acceptability. Main issues are to enhance the battery lifetime of IoT-enabled devices and to allow the transmission of acceptable quality ECG signals. The objective is to propose an in silico-based method to optimize ECG telemetry systems by identifying critical telecommunication parameters and optimizing both battery lifetime and quality of transmitted ECG. 15 simulation parameters have been selected to test their criticality and three quality attributes have been examined: battery lifetime, number of received packets and ECG distortion. The methodology framework relies on the Quality-by-Design guidelines (ICH Q8-Q12). Its partial implementation was based on two consecutive sets of simulations. In a first step, a screening design of numerical experimentations (Plackett-Burman design) was carried out to identify the critical network parameters. In a second step, an optimization campaign, based on a central composite design, was implemented to identify the design space, i.e. the region of interest in which all the telecommunication parameters have to be kept fulfilling the quality specifications. Simulations were carried out in Omnet++ and the statistical analysis in the software environment R. For the number of packets received, two critical parameters were identified: message length and bit rate. For ECG distortion, the first two most critical factors are background noise power and energy detection of the radio receiver. For the battery lifetime, preliminary results tend to show that background noise power and bit rate are critical. This study demonstrates the relevance to use numerical simulations to design but also check the compatibility of a telemetry system with predefined specifications on quality of services in a given and constrained context. Such an approach can be implemented in the early steps of development and can save of lot of time and money by preventing malfunctions, scraps and posteriori restructurations of the telemetry networks.

Assessing the Utility of Early Warning Systems for Detecting Failures in Major Wind Turbine Components

This paper provides enhancements to normal behaviour models for monitoring major wind turbine components and a methodology to assess the monitoring system reliability based on SCADA data and decision analysis. Typically, these monitoring systems are based on fully data-driven regression of damage sensitive-parameters. Firstly, the problem of selecting suitable inputs for building a temperature model of operating main bearings is addressed, based on a sensitivity study. This shows that the dimensionality of the dataset can be greatly reduced while reaching sufficient prediction accuracy. Subsequently, performance quantities are derived from a statistical description of the prediction error and used as input to a decision analysis. Two distinct intervention policies, replacement and repair, are compared in terms of expected utility. The aim of this study is to provide a method to quantify the benefit of implementing the online system from an economic risk perspective. Under the realistic hypotheses made, the numerical example shows for instance that replacement is not convenient compared to repair.

Effect of changing of the parameters of the cable network of monitoring systems of high-rise buildings on the basis of string converters on their operability

The article is devoted to the problem of improving the reliability of monitoring systems for the technical conditions of high-rise buildings. The improvement is based on string sensors with an impulsed excitation method ensuring the maximum signal-to-noise ratio at their output. The influence of the parameters of the monitoring system on the shape of the excitation impulses of the string, and, consequently, on the amplitude of the string vibration of the string converter is also considered in the article. It has been experimentally proved that the parameters of the excitation impulses of the string converters. The article presents the results of the experiments showing the effect of the fronts duration of the excitation impulses on the amplitude of the oscillations of the strings. The influence of the fronts duration of the excitation impulse with the frontal lengths up to 0.5 ms is studied at the excitation impulse duration not exceeding 0.5 times the duration of natural oscillation periods of the string. The experimental data are compared with the theoretical ones and hypotheses explaining their difference are advanced. The article suggests some methods of reducing the influence of the cable-switching equipment system parameters on the amplitude of string oscillations. The possibilities of improving the reliability of the systems developed on the basis of string sensors with an impulsed excitation method and used for monitoring the technical conditions of the high-rise buildings are proposed.

On the matter of the reliability of the chemical monitoring system based on the modern control and monitoring devices

The reliability of the main equipment of any power station depends on the correct water chemistry. In order to provide it, it is necessary to monitor the heat carrier quality, which, in its turn, is provided by the chemical monitoring system. Thus, the monitoring system reliability plays an important part in providing reliability of the main equipment. The monitoring system reliability is determined by the reliability and structure of its hardware and software consisting of sensors, controllers, HMI and so on [1,2]. Workers of a power plant dealing with the measuring equipment must be informed promptly about any breakdowns in the monitoring system, in this case they are able to remove the fault quickly. A computer consultant system for personnel maintaining the sensors and other chemical monitoring equipment can help to notice faults quickly and identify their possible causes. Some technical solutions for such a system are considered in the present paper. The experimental results were obtained on the laboratory and experimental workbench representing a physical model of a part of the chemical monitoring system.

New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System.

This research introduces an improvement plan for the reliability of Global Navigation Satellite System (GNSS) positioning solutions. It should be considered the most suitable methodology in terms of the adjustment and positioning of GNSS in order to maximize the utilization of GNSS applications. Though various studies have been conducted with regards to Bridge Health Monitoring System (BHMS) based on GNSS, the outliers which depend on the signal reception environment could not be considered until now. Since these outliers may be connected to GNSS data collected from major bridge members, which can reduce the reliability of a whole monitoring system through the delivery of false information, they should be detected and eliminated in the previous adjustment stage. In this investigation, the Detection, Identification, Adaptation (DIA) technique was applied and implemented through an algorithm. Moreover, it can be directly applied to GNSS data collected from long span cable stayed bridges and most of outliers were efficiently detected and eliminated simultaneously. By these effects, the reliability of GNSS should be enormously improved. Improvement on GNSS positioning accuracy is directly linked to the safety of bridges itself, and at the same time, the reliability of monitoring systems in terms of the system operation can also be increased.

Multi-sensor data fusion framework for CNC machining monitoring

Reliable machining monitoring systems are essential for lowering production time and manufacturing costs. Existing expensive monitoring systems focus on prevention/detection of tool malfunctions and provide information for process optimisation by force measurement. An alternative and cost-effective approach is monitoring acoustic emissions (AEs) from machining operations by acting as a robust proxy. The limitations of AEs include high sensitivity to sensor position and cutting parameters. In this paper, a novel multi-sensor data fusion framework is proposed to enable identification of the best sensor locations for monitoring cutting operations, identifying sensors that provide the best signal, and derivation of signals with an enhanced periodic component. Our experimental results reveal that by utilising the framework, and using only three sensors, signal interpretation improves substantially and the monitoring system reliability is enhanced for a wide range of machining parameters. The framework provides a route to overcoming the major limitations of AE based monitoring.

Power supply issues in e-health monitoring applications

Recent years have seen a rapid growth in the development of e-health systems for the continuous remote patient monitoring of physiological data such as temperature, heart rate (HR), Blood pressure (BP), oxygenation (SpO2), respiration and glucose levels. The roll-out of such schemes show promise in delivering improvements in patient care while at the same time reducing both the demand for resources and the financial burden on healthcare systems. These wearable monitoring systems are used to monitor, log and transmit patient data to a central health authority. Depending on the patient, it is often critical that the monitoring system reliability is high to deliver the appropriate patient care and ensure patient safety. However, since wearable systems are solely dependent on battery power, continuous monitoring will rapidly deplete the battery energy making the system prone to failure. In this paper, methods to reduce power consumption will be discussed using the example of prosthetic socket temperature monitoring data.

Reliability Analysis of Substation Monitoring Systems Based on Branch PMUs

This paper presents the application of fault tree analysis to calculate the probability of failure of a substation monitoring system based on branch phasor measurement units. Results of the fault tree analysis are used in a reliability-based model for the contingency-constrained phasor measurement units (PMUs) placement to find the minimum number of substations where the branch PMUs must be located for enhancing the wide area monitoring system reliability. The algorithm selects the minimal number of substations to monitor the power system under normal operation and the most credible contingencies. Finally, four indexes will be computed to evaluate the robustness of any monitoring system placement solution. The methodology is tested on the IEEE 9-bus and IEEE 57-bus test systems.

Analysis of Monitoring System Reliability for Wind Turbine Based on Wireless Sensor Network

In this paper, based on wireless sensor network technology, we propose the monitoring system for wind tur- bine. In order to ensure that the monitoring system can completely and accurately obtain the condition monitoring infor- mation, using exponential distribution function, we establish the reliability model. Because the topology control tech- niques can improve the monitoring system reliability, we present the hierarchical network topology for monitoring sys- tem. Then, combined with the presented hierarchical network topology, we study the reliability model. Finally, simulation shows that the network parameters affect the reliability and the working time of monitoring system.

Evaluation of MERIS products from Baltic Sea coastal waters rich in CDOM

Abstract. In this study, retrievals of the medium resolution imaging spectrometer (MERIS) reflectances and water quality products using four different coastal processing algorithms freely available are assessed by comparison against sea-truthing data. The study is based on a pair-wise comparison using processor-dependent quality flags for the retrieval of valid common macro-pixels. This assessment is required in order to ensure the reliability of monitoring systems based on MERIS data, such as the Swedish coastal and lake monitoring system (http://vattenkvalitet.se). The results show that the pre-processing with the Improved Contrast between Ocean and Land (ICOL) processor, correcting for adjacency effects, improves the retrieval of spectral reflectance for all processors. Therefore, it is recommended that the ICOL processor should be applied when Baltic coastal waters are investigated. Chlorophyll was retrieved best using the FUB (Free University of Berlin) processing algorithm, although overestimations in the range 18–26.5%, dependent on the compared pairs, were obtained. At low chlorophyll concentrations (< 2.5 mg m−3), data dispersion dominated in the retrievals with the MEGS (MERIS ground segment processor) processor. The lowest bias and data dispersion were obtained with MEGS for suspended particulate matter, for which overestimations in the range of 8–16% were found. Only the FUB retrieved CDOM (coloured dissolved organic matter) correlate with in situ values. However, a large systematic underestimation appears in the estimates that nevertheless may be corrected for by using a local correction factor. The MEGS has the potential to be used as an operational processing algorithm for the Himmerfjärden bay and adjacent areas, but it requires further improvement of the atmospheric correction for the blue bands and better definition at relatively low chlorophyll concentrations in the presence of high CDOM attenuation.

Ecological and economical efficiency of monitoring systems for oil and gas production on the shelf

Requirements for signals’ reliability of monitoring systems (with respect to the errors of the 1st and 2nd kinds, i.e., false alarms and skipping of danger) are deduced from the ratio of expenditures of different kinds (of exploitation expenses and losses due to accidents). The expressions obtained in the research may be used for economic foundations (and optimization) of specifications for monitoring systems. In cases when optimal parameters are not available, the sufficient condition of monitoring systems economical efficiency is presented.

Online Monitoring System of the Mine Main Fan

Monitoring system uses upper and lower machine model. The machine adopts the simple operation, reliable performance of PLC acquisition of main fan for each parameter and alarm control. In order to ensure that the fan monitoring system reliability and real time nature, based on the VC++ and SQL Server to develop a set of computer monitoring system. Monitoring software system and data acquisition and processing hardware system connected by a RS-485 bus system. Computer to other computers in a network or through the monitoring host forwarding see fan monitoring interface.