Monitoring Systems In Order Research Articles

Hybrid Approach for Detection and Diagnosis of Short-Circuit Faults in Power Transmission Lines

In this article, the main problem under investigation is the detection and diagnosis of short-circuit faults in power transmission lines. The proposed fault detection (FDD) approach is mainly based on principal component analysis (PCA). The proposed fault diagnosis/identification (FAI) approach is mainly based on sliding-window versions of the discrete Fourier transform (DFT) and discrete Hilbert transform (DHT). The main contributions of this article are (a) a fault detection approach based on principal component analysis in the two-dimensional scores space; and (b) a rule-based fault identification approach based on human expert knowledge, combined with a probabilistic decision system, which detects variations in the amplitudes and frequencies of current and voltage signals, using DFT and DHT, respectively. Simulation results of power transmission lines in Portugal are presented in order to show the robust and high performance of the proposed FDD approach for different signal-to-noise ratios. The proposed FDD approach, implemented in Python, that can be executed online or offline, can be used to evaluate the stress to which circuit breakers (CBs) are subjected, providing information to supervision- and condition-based monitoring systems in order to improve predictive and preventive maintenance strategies, and it can be applied to high-/medium-voltage power transmission lines as well as to low-voltage electronic transmission systems.

Neural Networks Applications for Fault Detection on Wind Turbines

Wind energy is the renewable energy source with a higher growth rate in the last decades. The huge proliferation of wind farms across the world has arisen as an alternative to the traditional power generation and also as a result of economic issues which necessitate monitoring systems in order to optimize availability and profits. Tools to detect the onset of mechanical and electrical faults in wind turbines at a sufficiently early stage are very important for maintenance actions to be well planned, because these actions can reduce the outage time and can prevent bigger faults that may lead to machine stoppage. The set of measurements obtained from the wind turbines is enormous and the use of neural networks may be useful in understanding if there is any important information that may help the prevention of serious failures. The training of the Neural Networks however is not easy because the measurement set used for training must represent a period of time with no faults in the equipment of the turbine that is being monitored.

Automated Monitoring and Emergency Response System for Sensitive Areas Along High-Speed Railway Lines

The safety of high-speed railroads is an important indicator of travelers’ trust, and it is also an important factor in determining whether or not the operation volume can reach the expected goal. Therefore, high-speed railroad companies in Europe, the United States, and Japan have invested heavily in establishing safety monitoring systems in order to avoid the occurrence of disasters and to strengthen the mechanism of crisis response and emergency treatment. High-speed railroad operation may occur in a variety of accidents, different disaster warning, emergency response and rescue, need to have different equipment, contingency measures, and well-trained personnel to deal with. Therefore, the safety standards for high-speed rail systems in advanced countries around the world are much higher than those for traditional train systems. The theories and technologies of disaster prevention and relief have matured both at home and abroad. This study combines the theories and technologies of disaster prevention with GIS, such as monitoring instruments, satellite positioning, remote sensing detection, radio transmission, the Internet, database management, and information management, to provide effective support for decision-making and analysis of commanders, to rapidly assist in crisis management, and to strengthen the mechanism of emergency response.

Tool wear segmentation in blanking processes with fully convolutional networks based digital image processing

The extend of tool wear significantly affects blanking processes and has a decisive impact on product quality and productivity. For this reason, numerous scientists have addressed their research to wear monitoring systems in order to identify or even predict critical wear at an early stage. Existing approaches are mainly based on indirect monitoring using time series, which are used to detect critical wear states via thresholds or machine learning models. Nevertheless, differentiation between types of wear phenomena affecting the tool during blanking as well as quantification of worn surfaces is still limited in practice. While time series data provides partial insights into wear occurrence and evolution, direct monitoring techniques utilizing image data offer a more comprehensive perspective and increased robustness when dealing with varying process parameters. However, acquiring and processing this data in real-time is challenging. In particular, high dynamics combined with increasing strokes rates as well as the high dimensionality of image data have so far prevented the development of direct image-based monitoring systems. For this reason, this paper demonstrates how high-resolution images of tools at 600 spm can be captured and subsequently processed using semantic segmentation deep learning algorithms, more precisely Fully Convolutional Networks (FCN). 125,000 images of the tool are taken from successive strokes. Selected images are labeled pixel by pixel according to their wear condition and used to train a FCN (U-Net). The approach presented offers the possibility of spatially monitoring wear in high-speed blanking operations in real-time.

Highly Sensitive Wireless NO2 Gas Sensing System

There is a need for reliable and accurate air quality monitoring systems in order to secure human health from the air pollutants such as nitrogen dioxide (NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ). The development of low-cost and modern technology-compatible sensing systems is essential. In this work, a highly sensitive and selective Indium Gallium Zinc oxide (IGZO) thin film transistor has been used to design an efficient and wireless NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> sensing system. The sensing system uses the IGZO TFT as a current source for the current-starved ring oscillator where exposed NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> concentration determines the oscillation frequency. The presented NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> gas sensing system exhibited excellent sensitivity of 5.39 MHz/ppm with the lowest detection limit (LOD) of 50 ppb at room temperature. To the best of the authors’ knowledge, the achieved sensitivity is the best-reported performance for frequency-based NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> sensors. The sinusoidal output of the sensor obviates the need for costly peripheral signal conditioning circuits and allows direct integration with wireless systems. The 13.56 MHz RFID antenna connected to the output of the sensor is used to show wireless sensing compatibility and highlights the excellent candidacy of the proposed work for the growing Internet of Things (IoT) and untethered sensor applications.

Validation and limitations of large-scale forest condition indicators – An example from Hungary

As the multi-scale study of biodiversity is extremely resource-intensive, proxy indicators taken from various databases are often used to answer questions on a larger spatial scale. Considering differences in the scale and methods, the application of such indicators (especially from different monitoring systems) requires careful consideration and standard methods of validation.In order to demonstrate this, we validated the results of the MAES-HU (National Ecosystem Assessment of Hungary) forest condition assessment (based on the Hungarian NFD – National Forestry Database) with thematically richer, finer-scale field data. We also examined the relationships of some MAES-HU indicators with other variables significant for nature conservation, not currently included in the NFD. We found the MAES-HU scoring was similar to the fine-scale score results in the case of tree species composition indicators, however, less so for structural indicators. The MAES-HU assessment uses the values of the NFD, averaged for forest management units, and thus tends to underestimate structural variety. This highlights a potential loss of important conservation-related information. During the examination of relationships with other indicators that are not included in the large-scale MAES-HU assessment, we found that the presence of large-diameter and old trees correlates with tree-related microhabitats and large standing deadwood, but no relationship was found for other investigated indicators (game pressure, further deadwood indicators). This highlights the need for integrating some key conservation indicators (presence of old and large trees, quantity and quality of standing and lying deadwood) into existing forest monitoring systems in order to optimise the resources dedicated to multipurpose data collection. Our study also highlights that applying indicators as proxies requires the full knowledge of monitoring methods and validated indicator-indicanda relationships.

Public monitoring as a tool for assessing the state of the air basin in the Ukrainian Regions

Over the recent years automated observation systems that work continuously and allow obtaining information at any time have been widely implemented. Such systems include public monitoring systems in order to receive more complete information about the quality characteristics of the air basin over Ukraine. Examples of public monitoring organizations include SaveEcoBot, EcoCity, SaveDnipro.&#x0D; The information presented on the websites of the public monitoring system contains large amount of data that are difficult to process and analyze. In order to optimize data processing, a program called PublicMonitoringParser was developed. It transforms large volumes of data presented on the Internet resource SaveEcoBot.com into compact, convenient, small files containing average monthly concentrations of pollutants and indicators of meteorological parameters. The program is written using the C# programming language and the .NET platform. The structure of the program and the principles of its operation are presented in this research.&#x0D; According to the data of the website SavEecoBot.com the developed program was applied for assessment of the air basin state of Lviv Region in 2021. In general, for most substances, monitoring data were available only at the station in the city of Lviv at the following address: 13 Plastova Str. Monitoring of PM2.5 and PM10 content was carried out across the whole region.&#x0D; It was detected that the content of NO2, SO2, NH3 exceeded the limit value. Significant excesses were also noted for the NH3 content (up to 9 – 16 MPC). It was established that the location of the public monitoring station was surrounded by industrial buildings, numerous transport industry enterprises, an oil depot for OKKO gas stations and city sewage treatment facilities. A comparison of the data obtained at stationary and automated points that monitor individual pollutants showed that the results are comparable only for the NO2 content.&#x0D; Data from automated observation points complement the network of stationary points. Currently, the existing stationary network of atmospheric pollution monitoring across most regions of Ukraine is outdated and needs to be re-equipped. In general, it is necessary to further implement automated systems that are able to characterize the state of the air basin across individual populated places in a more complete and detailed manner.

The moderating effect of anxiety diagnosis on the relationship between error-related negativity and anxiety sensitivity cognitive concerns

Anxiety sensitivity (AS) refers to fear of anxiety symptoms that are believed to result in physical (Physical Concerns), cognitive (Cognitive Concerns), or social (Social Concerns) harm. AS is implicated in a range of anxiety disorders and may propel maladaptive behaviors by increasing action monitoring systems in order to prevent errors. Indeed, anxious individuals are characterized by elevated neural responses to errors, as indexed by the error-related negativity (ERN). In the current study we examined the moderating effect of clinical diagnosis on the relationship between scores on the Anxiety Sensitivity Index (ASI-3) and the ERN in an unselected sample (N = 124) of women. Based on semi-structured clinical interviews, participants were classified as belonging to an anxiety group (AD), a clinical control group (CC), and a healthy non-clinical group (HC). Participants completed an arrowhead version of the Flanker task while we collected electroencephalogram (EEG) data. Analyses revealed that diagnostic group moderated the association between residualized ERN (ERNResid) and Cognitive Concerns, such that the AD group demonstrated a significantly stronger and more negative association compared to the HC group. Our results indicate that the relationship between ERNResid and Cognitive Concerns is strongest in individuals characterized by elevated anxiety.

Magnetic Field Energy Harvesting in Railway

Magnetic field energy harvesting (MFEH) is a method by which a system can harness an ambient, alternating magnetic field in order to scavenge energy. Presented in this article is a novel application of the concept aimed at the magnetic fields surrounding the rail current in electrified railway. Due to its noninvasive nature, the approach has the potential to be widely deployed as a part of low-cost trackside condition monitoring systems in order to increase lifetime and reduce maintenance requirements. In this article, the viability of MFEH in railway is substantiated experimentally—two different configurations are assessed both in a controlled laboratory environment as well as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> along Norwegian railway. When placed near an emulated section of railway carrying 200 A in the laboratory, the power output of the system is up to 40.5 mW at 50 Hz and 4.15 mW at 16 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\frac{2}{3}\; \mathrm{Hz}$</tex-math></inline-formula> . In the field, the prototype system harvests 109 mJ from a single freight train passing by, rendering an estimated daily energy output of 1.14 J in a moderately trafficked location. It is argued that the approach could indeed eliminate the need for battery replacements and potentially increase the lifetime of an energy-efficient, battery-powered condition monitoring system indefinitely.

Prediction of Blood Glucose Level Using Nonlinear System Identification Approach

Predicting the blood glucose level of type 1 diabetes mellitus of patients could prevent hypo/hyperglycemia incidents that are threats for the patients with this disease. A nonlinear system identification approach is proposed in this work to develop a mathematical model, which can be used to predict the blood glucose level over a given period with high accuracy. More specifically, the Hammerstein Box-Jenkins model is used to approximate the system, where two infinite impulse response filters represent the linear and noise processes, and a polynomial basis function represents the nonlinearity. The proposed identification method is based on the Steiglitz-McBride approach to predict the model parameters. Moreover, a simulation software licensed by the USA Food and Drug Administration that simulates the dynamics of the glucose-insulin system metabolism inside the body, called Type I Diabetes Metabolic Simulator (T1DMS), was used to generate the data. Thirty subjects of different age groups were considered, and the data was generated for a week with a sample per minute, i.e. 302430 data points. This data was then processed using a developed MATLAB code to predict the blood glucose level. Various scenarios were established to validate the proposed approach. The simulations showed very promising results with a very low average root mean square error of 1 mg/dl, which is seven times less compared to other prediction techniques. Other cost functions have also been used and they showed very good results. In the future, this approach can be embedded in closed-loop continuous blood glucose monitoring systems in order to give alerts to the patients and help in calculating the needed insulin dose.

Life-cycle management cost analysis of transportation bridges equipped with seismic structural health monitoring systems

Life-cycle cost analysis is an approach that has gained popularity for assisting the design of civil infrastructures. The life-cycle cost analysis approach can be leveraged for structures equipped with structural health monitoring systems in order to quantify the benefits of the technology and de facto support its long-term implementation. However, for new structures, the long-term assessment of the expected value of the total investment cost, in terms of the current worth at the design time, is still the focus of ongoing research due to unknowns and uncertainties on the impact of the structural health monitoring system on long-term structural performance. This article proposes a new combined model of life-cycle cost formulation and simulation methodology for the long-term financial assessment of transportation bridges equipped with seismic structural health monitoring systems, in order to evaluate the total costs and benefits offered by such monitoring systems for post-seismic assessments. The formulation characterizes the time evolution of bridge management cost terms, highlighting the most sensitive parameters. The simulation methodology allows to quantitatively weigh each maintenance action on the total cost based on when the action is performed. The model is used to compare structures managed by the traditional approach of post-earthquake inspection versus those managed by a condition-based approach enabled by structural health monitoring systems. The originality of the model empowers the comparison by payback time, defined as the break-even point between costs and benefits of a structural health monitoring system, as well as by economic gain, defined as the difference between the total costs of an unmonitored versus a monitored structure through the end of service life. The proposed model is demonstrated through parametric analyses on a case study consisting of a continuous steel-concrete composite bridge, where the structural health monitoring system is used to monitor the elastic limit state condition of bending forces in piers during the earthquake.

Method of forming a rational set of test trajectory options to increase the reliability of performance evaluation during testing of radar information tools

To get an objective picture of the state of near-earth space, it is necessary to update the fleet of information tools in a timely manner, which is characterized by the introduction of new tools and the modernization of old tools, while changing the system in stages as the funds are ready. The continuous build-up of the system's assets as they are ready allows us to improve its performance without waiting for all newly introduced and upgraded assets to be ready. To test monitoring systems in conditions of a large number of heterogeneous elements combined in a system to achieve a single goal, an experimental and theoretical method of evaluating effectiveness based on mathematical modeling is used. The essence of this method is to simulate information at the input of programs of the system tools using mathematical models implemented directly on the system tools themselves. In this case, the quality of test trajectories that would allow evaluating the characteristics of radar information tools and the capabilities of monitoring systems is of particular importance. The purpose of the study is to increase the reliability of the results of tests of radar information tools for monitoring space debris by selecting a rational set of trajectory options that allow for the worst possible implementation of the capabilities of radar information tools, taking into account the functioning of each specific radar information tool participating in the tests. Based on the features of the factor space for finding solutions to the problem of forming test trajectories, the algorithm for forming the initial population and the algorithm for generating a new generation should be supplemented with blocks for checking the observability and feasibility of trajectories. The developed method of forming a rational set of variants of test trajectories using genetic algorithms differs from the known ones in that it generates a set of variants of test trajectories optimized from the point of view of obtaining the worst implementations of the capabilities of radar systems during its testing, under conditions of restrictions on the feasibility and observability of trajectories, the application of the methodology allows automating the process of preparing initial data for testing space debris monitoring systems in order to evaluate their characteristics. The application of the developed method allows to obtain the maximum errors in determining the parameters of the trajectory of space debris elements.

Key indicators for harmonized European population health surveillance: an iterative approach

Abstract Introduction A pan-European approach to evaluate policy impact on health behaviours requires the employment of a consensus set of established, relevant indicators. This set includes indicators for policy and action, behavioural aspects, determinants and health outcomes. Methods Following an iterative selection process, we prioritised an agreed set of health indicators of high priority for policy makers. During this process, over 30 European experts were invited to attend a workshop and consult on inclusion and prioritisation of indicators. These experts were representatives of surveillance systems and informants of similar initiatives, e.g. STOP, Co-Create, HBSC, WHO COSI as well as representatives of organisations providing monitoring data such as OECD, EC DG SANTE, WHO and Eurostat. The consultation process was initiated prior to the workshop via email and continued with facilitated group discussions during the workshop. Results Lists with indicators relevant for policy evaluation and monitoring of 1) dietary behaviour (N = 76), 2) physical activity/sedentary behaviour (N = 86) as well as 3) socio-demographic indicators (N = 9) have been composed; examples will be presented. This suite of key indicators was agreed and then mapped against data in existing pan-European monitoring systems in order to provide an open-access catalogue suitable for the design and evaluation of new and existing policies. Conclusions The presentation will offer an opportunity for researchers and stakeholders to learn more about this catalogue, the indicators and how they could be applied and integrated into their activities. Key messages We provide a set of health indicators relevant for pan-European policy impact evaluation. These indicators - ideally measured using harmonized instruments - may facilitate collection of comparable data across EU surveillance and monitoring systems.

Fatigue Stress Estimation of an Offshore Jacket Structure Based on Operational Modal Analysis

Fatigue life assessment currently recommended by offshore standards is associated with a large number of uncertainties mainly related to the environmental loads and the numerical model. Recently, for economic reasons, the need for extending the lifetime of existing offshore structures led to the necessity of developing more accurate and realistic predicting models so that damage detection and maintenance can be optimized. This paper proposes the implementation of Structural Health Monitoring Systems in order to extract modal properties—such as mode shapes, natural frequencies, and damping ratios—throughout Operational Modal Analysis (OMA), which is the engineering field that studies the modal properties of systems under ambient vibrations or normal operating conditions. The identified modal properties of the structural system are the fundamental information to update a finite element model by means of an expansion technique. Then, the virtual sensing technique—modal expansion—is used to estimate the stress in the entire structure. Though existing models depend on the load estimation, the model based on OMA-assisted virtual sensing depends on the measured responses and assumes that the loads act as random vibrations. A case study using data from a real offshore structure is presented based on measurements recorded during normal operation conditions of an offshore tripod jacket. From strains estimated using OMA and virtual sensing, fatigue stresses are predicted and verified by applying the concept of equivalent stress range. Both estimated and measured strains are given as input data to evaluate the equivalent stress range and compared with each other. Based on this study, structural health monitoring estimates the fatigue stresses with high precision. As conclusion, this study describes how the fatigue can be assessed based on a more accurate value of stress and less uncertainties, which may allow extending the fatigue life of offshore platforms.

Damage tolerance evaluation of a unit cell plane based on electron beam powder bed fusion (E-PBF) manufactured Ti6Al4V alloy

Electron beam powder bed fusion (E-PBF) enables the fabrication of new and complex light-weight structures within short process times. However, increasing complexity of the producible components leads to complex damage mechanisms and interactions which can not sufficiently be represented by typical material properties such as ultimate tensile strength or fatigue strength. In the current research, the damage tolerance was investigated within a single unit cell plane for a stretch-dominated lattice type under uniaxial cyclic loading based on the E-PBF manufactured Ti6Al4V alloy. During the experiments, a combination of application-specific optical measurement techniques such as digital image correlation and thermography were used to capture occurring material responses. In particular, a clear relationship between local deformation and temperature increase is visible. In comparison to conventionally manufactured material, similar mechanical properties can be achieved in low cycle fatigue range. Furthermore, captured material responses could be used as a basis for future monitoring systems in order to enable a reliable application in safety-relevant components.

A novel oscillating buoy wave energy harvester based on a spatial double X-shaped mechanism for self-powered sensors in sea-crossing bridges

With the sharp increase in the number of sea-crossing bridges, various sensors have been widely used in bridge monitoring systems in order to ensure safety and prevent accidents during the use of sea-crossing bridges. In this paper, an oscillating buoy wave energy harvester (WEH) based on a spatial double X-shaped mechanism is proposed for self-powered sensors in sea-crossing bridges. Fixed to the side of pillars in sea-crossing bridges, the proposed WEH can convert wave energy with high energy density into electricity. The system consists of four modules: wave energy capture module, transmission module, generator module, and energy storage module. The wave energy capture module collects wave energy through a cylindrical buoy. The transmission module converts the up and down motion of the buoy into a unidirectional rotational motion of the generator input shaft in the generator module to produce electricity. The super-capacitors in the energy storage module store this electrical energy, which is applied to the self-powered sensors. A max efficiency of 57% and an average efficiency of 46.17% can be obtained through MTS bench tests. In field tests, the system’s voltage can reach approximately 3 V, given wave excitation of 1.2 Hz frequency and 15 mm amplitude, which indicates that the proposed WEH is feasible and practical for generating energy used for self-powered sensors in sea-crossing bridges.

The Concept of Geodetic Analyses of the Measurement Results Obtained by Hydrostatic Leveling

The article discusses the issue of hydrostatic leveling. Its application is presented in structural health monitoring systems in order to determine vertical displacements of controlled points. Moreover, the article includes a complete computation scheme that utilizes the estimation from observation differences, allowing the elimination of the influence of individual sensors’ systematic errors. The authors suggest two concepts of processing the measurement results depending on the sensors’ connection method. Additionally, the second concept is extended by the elements allowing the prediction of the displacements by means of Kalman filtering.

Multi-stage SVM approach for cardiac arrhythmias detection in short single-lead ECG recorded by a wearable device

Objective: Use of wearable ECG devices for arrhythmia screening is limited due to poor signal quality, small number of leads and short records, leading to incorrect recognition of pathological events. This paper introduces a novel approach to classification (normal/‘N’, atrial fibrillation/‘A’, other/‘O’, and noisy/‘P’) of short single-lead ECGs recorded by wearable devices. Approach: Various rhythm and morphology features are derived from the separate beats (‘local’ features) as well as the entire ECGs (‘global’ features) to represent short-term events and general trends respectively. Various types of atrial and ventricular activity, heart beats and, finally, ECG records are then recognised by a multi-level approach combining a support vector machine (SVM), decision tree and threshold-based rules. Main results: The proposed features are suitable for the recognition of ‘A’. The method is robust due to the noise estimation involved. A combination of radial and linear SVMs ensures both high predictive performance and effective generalisation. Cost-sensitive learning, genetic algorithm feature selection and thresholding improve overall performance. The generalisation ability and reliability of this approach are high, as verified by cross-validation on a training set and by blind testing, with only a slight decrease of overall F1-measure, from 0.84 on training to 0.81 on the tested dataset. ‘O’ recognition seems to be the most difficult (test F1-measures: 0.90/‘N’, 0.81/‘A’ and 0.72/‘O’) due to high inter-patient variability and similarity with ‘N’. Significance: These study results contribute to multidisciplinary areas, focusing on creation of robust and reliable cardiac monitoring systems in order to improve diagnosis, reduce unnecessary time-consuming expert ECG scoring and, consequently, ensure timely and effective treatment.

Post-earthquake controls and damage detection through structural health monitoring: applications in l’Aquila

The paper presents structural health monitoring (SHM) activities performed on some representative cultural heritage (CH) buildings in the city of l’Aquila after the strong earthquake (Mw = 6.3) that struck the Abruzzo region (central Italy) on April 6, 2009. The severity and the extent of damages caused by the earthquake to historical buildings and monument were never reached before in the recent Italian earthquake history. Emergency activities started immediately after the earthquake to protect CH structures, including damage survey and design/implementation of temporary safety measures. Some historic buildings were soon equipped with monitoring systems in order to assess the level of damage and verify the effectiveness of the executed provisional interventions. The paper focuses in particular on two case studies, i.e. the Spanish Fortress and the Civic Tower. The results of preliminary investigations are reported, including damage survey and operational modal analysis for modal parameter identification using ambient vibration tests. 3-year static and dynamic monitoring features, automatically extracted from raw data acquired by continuous monitoring systems, were then processed using a data-driven approach based on regression analysis to filter out the environmental effects. Following this approach data are decomposed into their reversible and irreversible components, the latter being associated with active damaging processes and the residual structural performance of the two buildings assessed.

Cycle chemistry monitoring system as means of improving the reliability of the equipment at the power plants

There are many problems in domestic energy at the present that require urgent solutions in the near future. One of these problems - the aging of the main and auxiliary equipment. Wear of equipment is the cause of decrease reliability and efficiency of power plants. Reliability of the equipment are associated with the introduction of cycle chemistry monitoring system. The most damageable equipment’s are boilers (52.2 %), turbines (12.6 %) and heating systems (12.3 %) according to the review of failure rate on the power plants. The most part of the damageability of the boiler is heated surfaces (73.2 %). According to the Russian technical requirements, the monitoring systems are responsible to reduce damageability the boiler heating surfaces and to increase the reliability of the equipment. All power units capacity of over 50 MW are equipped with cycle chemistry monitoring systems in order to maintain water chemistry within operating limits. The main idea of cycle chemistry monitoring systems is to improve water chemistry at power plants. According to the guidelines, cycle chemistry monitoring systems of a single unit depends on its type (drum or once-through boiler) and consists of: 20…50 parameters of on-line chemical analyzers; 20…30 «grab» sample analyses (daily) and about 15…20 on-line monitored operating parameters. The operator of modern power plant uses with many data at different points of steam/water cycle. Operators do not can estimate quality of the cycle chemistry due to the large volume of daily and every shift information and dispersion of data, lack of systematization. In this paper, an algorithm for calculating the quality index developed for improving control the water chemistry of the condensate, feed water and prevent scaling and corrosion in the steam/water cycle.