Review of Operating Conditions, Diagnostic Methods, and Technical Condition Assessment to Improve Reliability and Develop a Maintenance Strategy for Electrical Equipment

Technical condition (TC) of electrical equipment (EE) is evaluated based on functional units weights. Turbogenerator (TG), which is rather common energy equipment, was chosen as the study subject. To assess TG TC, authors consider TG functional units (FnUnt) and their control parameters. Weights are calculated as well as TG TC index.

Purpose. The aim of this work is to develop a mathematical model of the generalized diagnostic indicator of the technical state of traction substations electrical equipment. Methodology. The main tenets of the experiment planning theory, methods of structural-functional and multi-factor analysis, methods of mathematical and numerical modeling have been used to solve the set tasks. Results. To obtain the mathematical model of the generalized diagnostic indicator, a full factorial experiment for DC circuit breaker have been conducted. The plan of the experiment and factors affecting the change of the unit technical condition have been selected. The regression equation in variables coded values and the polynomial mathematical model of the generalized diagnostic indicator of the circuit breaker technical condition have been obtained. On the basis of regression equation analysis the character of influence of circuit breaker diagnostic indicators values on generalized diagnostic indicator changes has been defined. As a result of repeated performances of the full factorial experiment the mathematical models for other types of traction substations power equipment have been obtained. Originality. An improved theoretical approach to the construction of generalized diagnostic indicators mathematical models for main types of traction substations electric equipment with using the methods of experiments planning theory has been suggested. Practical value. The obtained polynomial mathematical models of the generalized diagnostic indicator D can be used for constructing the automated system of monitoring and forecasting of the traction substations equipment technical condition, which allows improving the performance of processing the diagnostic information and ensuring the accuracy of the diagnosis. Analysing and forecasting the electrical equipment technical condition with the using of mathematical models of generalized diagnostic indicator changes process allows constructing the optimal strategy of maintenance and repair based on the actual technical condition of the electrical equipment. This will reduce material and financial costs of maintenance and repair work as well as the equipment downtime caused by planned inspections and repair improving reliability and uptime of electrical equipment.

The rhythmic and stable operation of trolleybuses and autonomous trolleybuses or urban electric buses, depends to a large extent on the reliability of the equipment installed on the trolleybus. The actual operational reliability of trolleybus electrical equipment (EE) depends on its technical condition. Under the influence of external factors and specific operating modes, the technical condition of the equipment is continuously deteriorating, reliability indicators are decreasing, and the number of failures is increasing. Using the mathematical theory of reliability, probability theory and mathematical statistics, numerical methods of solving nonlinear and transcendental equations, this article defines the conditions of diagnostics depending on the intensity of failures and the given probability of failure-free operation of the equipment. Additionally, the inverse problem of determining the current reliability of electrical engineering systems depends on the terms of diagnostics and the intensity of failures being solved. As a result of the processing of statistical information on failures it is established that for the electrical equipment of a trolleybus, after a number of repair measures, the maximum density of failures occurs at a lower mileage, and the probability of failure-free operation can vary depending on the degree of wear of the equipment, i.e., on the number of previous failures. It is theoretically substantiated and experimentally confirmed that the reliability of trolleybus electrical equipment changes according to the exponential law of distribution of a random variable. It has been established that the real averaged diagnostic terms regulated by instructions are not optimal in most cases and differ several times from those defined in this paper. The dependence of switching equipment run-in on time has been clarified, which served as a prerequisite for specifying the inter-repair period for various types of trolleybus electrical equipment. A method of adjustment of the inter-repair time for the electrical equipment of trolleybuses is proposed.

Relevance. In the context of the digital transformation of the electric power industry, the urgency of developing distributed control systems for the condition of electrical network equipment based on forecasting the operating time for a defect with the determination of the adaptive frequency of preventive action is increasing.The purpose. To justify the expediency of creating and applying a similar electrical installation repair management system based on thermal imaging control (TIC) statistics as an alternative to local on-line monitoring systems based on various temperature sensors. To develop a predictive mathematical model to determine the operating time for a developed defect in the equipment. To form a methodology for calculating the adaptive frequency of equipment withdrawal for repair according to technical condition.Methods. The research uses methods of statistical data processing and statistical hypothesis testing, the formation of homogeneous Markov models with continuous time and numerical modeling in the MathCAD software environment.Results. The article reveals the relevance of the topic, outlines the methodological aspects of a distributed predictive control system for repairs of electrical network equipment, shows its advantages over local control systems based on modern temperature sensors, suggests models for predicting operating time for a developed defect in equipment and the frequency of its preventive repairs according to the actual technical condition. The calculation of the frequency of preventive maintenance of CTS-6/0.4 kV transformers of one of the electric utilities is given based on the forecast of operating time for a developed defect, illustrating the possibilities of the claimed technique.Conclusion. The proposed system of distributed predictive control of the technical condition of electrical network equipment based on operating time for a defect, unlike local temperature control systems based on modern sensors, has great functionality with significant cost savings. Its use is guaranteed to ensure the effectiveness of equipment prevention management due to the high reliability of the forecast of operating time for a defect and the determination of the adaptive frequency of preventive action.

Integrated Vehicle Health Management (IVHM) aims to support Condition-Based Maintenance (CBM) by monitoring, diagnosing, and prognosing the health of the host system. One of the technologies required by IVHM to carry out its objectives is the means to emulate the functioning of the host system, and the concept of a Digital Twin (DT) was introduced in aerospace IVHM to represent the functioning of such a complex system. This paper aims to discuss the role played by DT in the field of IVHM. A DT is the virtual representation of any physical product, that is used to project the functioning of the product at a given instance. The DT is used across the lifecycle of any product, and its output can be customized depending upon the area of application. The DT is currently popular in industry because of the technologies like sensors, cloud computing, Internet of Things, machine learning, and advanced software, which enabled its development. This paper discusses what encompasses a DT, the technologies that support the DT, its applications across industries, and its development in academia. This paper also talks about how a DT can combine with IVHM technology to assess the health of complex systems like an aircraft. Lastly, this paper presents various challenges faced by industry during the implementation of a DT and some of the possible opportunities for future growth.

Due to the fact that the equipment of modern electric trains is functionally and technologically complicated, the relevance of creating airborne systems for predictive monitoring of the technical condition of trains to identify their actual and predicted technical condition is increasing. At present, it has not been possible to build automatic on-board systems for predictive monitoring of the technical condition of trains. One of the possible solutions to this problem can be considered the creation of on-board systems, the identification of the technical condition of equipment in which is carried out using neural network technologies. The article proposes a methodology for identifying the technical condition of electric train equipment using artificial neural network technologies, which allows real-time detection of the occurrence and development of malfunctions of electric train equipment with the display of information on the display in the driver’s cab. Taking into account the specifics of the problem being solved, the choice of a multilayer architecture of a direct distribution neural network is justified. All layers of the neural network are completely interconnected, while the number of neurons of the input and output layers of the network is determined, equal to the number of controlled parameters of the technical condition of the electric train and the number of its possible technical conditions, respectively. As a function of activation of network neurons, a logistic function was selected. A heuristic approach is used to train an artificial neural network.

The deadly coronavirus disease (COVID-19) has highlighted the importance of remote health monitoring (RHM). The digital twins (DTs) paradigm enables RHM by creating a virtual replica that receives data from the physical asset, representing its real-world behavior. However, DTs use passive internet of things (IoT) sensors, which limit their potential to a specific location or entity. This problem can be addressed by using the internet of robotic things (IoRT), which combines robotics and IoT, allowing the robotic things (RTs) to navigate in a particular environment and connect to IoT devices in the vicinity. Implementing DTs in IoRT, creates a virtual replica (virtual twin) that receives real-time data from the physical RT (physical twin) to mirror its status. However, DTs require a user interface for real-time interaction and visualization. Virtual reality (VR) can be used as an interface due to its natural ability to visualize and interact with DTs. This research proposes a real-time system for RHM of COVID-19 patients using the DTs-based IoRT and VR-based user interface. It also presents and evaluates robot navigation performance, which is vital for remote monitoring. The virtual twin (VT) operates the physical twin (PT) in the real environment (RE), which collects data from the patient-mounted sensors and transmits it to the control service to visualize in VR for medical examination. The system prevents direct interaction of medical staff with contaminated patients, protecting them from infection and stress. The experimental results verify the monitoring data quality (accuracy, completeness, timeliness) and high accuracy of PT’s navigation.

An innovative methodology for managing repairs in distribution networks and power supply systems for oilfield facilities is proposed in this paper. The methodology makes it possible to select optimal solutions in accordance with the priority management goal by formalizing a risk-based model for planning maintenance and repair (M&R) of electrical equipment with a "condition-based maintenance" strategy. One of the main options in the complex of M&R management tasks is an adaptive model of the technological map for the repairs of power facilities, formed in compliance with a number of key principles. The developed model allows you to optimize the volume and complexity of repairs of electrical equipment of a certain type based on the minimum composition of technological operations necessary to eliminate the identified defects. Detection of defects in electrical equipment is achieved by using methods of technical diagnostics and non-destructive testing. In this case, each of the defects is recorded in the defect statement for the object, with the determination of the degree of criticality and fixing a specific type of corrective action. Optimal prioritization of electrical equipment repairs is performed in accordance with the minimum risk function under current restrictions on reliability (an equipment technical condition index) and available resources (total maintenance costs). The originality of the implementation of a key risk event – a power supply failure of an oilfield facility provides calculating the probability and severity of the consequences of an electrical equipment failure taking into account its technical condition and features of the power supply scheme. The given example illustrates the effectiveness of the proposed approach and emphasizes the validity of optimal decisions. The key components of the developed methodology have found practical application in the conditions of real operation of the equipment of oilfield facilities.

The rapid development of digitalization, the Internet of Things (IoT), and Industry 4.0 has led to the emergence of the digital twin concept. IoT is an important pillar of the digital twin. The digital twin serves as a crucial link, merging the physical and digital territories of Industry 4.0. Digital twins are beneficial to numerous industries, providing the capability to perform advanced analytics, create detailed simulations, and facilitate informed decision-making that IoT supports. This paper presents a review of the literature on digital twins, discussing its concepts, definitions, frameworks, application methods, and challenges. The review spans various domains, including manufacturing, energy, agriculture, maintenance, construction, transportation, and smart cities in Industry 4.0. The present study suggests that the terminology “3 dimensional (3D) digital twin” is a more fitting descriptor for digital twin technology assisted by IoT. The aforementioned statement serves as the central argument of the study. This article advocates for a shift in terminology, replacing “digital twin” with “3D digital twin” to more accurately depict the technology’s innate potential and capabilities in Industry 4.0. We aim to establish that “3D digital twin” offers a more precise and holistic representation of the technology. By doing so, we underline the digital twin’s analytical ability and capacity to offer an intuitive understanding of systems, which can significantly streamline decision-making processes using the digital twin.

Secure smart contract-based digital twins for the Internet of Things

Combination of information and operational technologies has led to a new way of production, to a new technological revolution, known as Industry 4.0. The Digital Twin plays a central role in this technology. The Digital Twin is a predictive maintenance tool, and allows you to simulate various options for device failures taking into account their operation modes, environmental influences and various degrees of wear. The concept of creating a digital twin of a real physical object of research is proposed - an AJAX DPS-180 internal combustion engine with a gas piston compressor, which is designed to pump gas from gas wells. A feature of its work is autonomous long-term operation in the field with the remoteness of the service personnel, direct environmental impact and ensuring the reliability and stability of work. Therefore, monitoring the parameters of the engine with the subsequent prediction of its failures is especially important. The work on creating a digital twin for AJAX DPS-180 is being carried out in cooperation and with the support of Armco-Engineering, the operator of this equipment.
 Six stages of the process of creating a digital twin of a given object are shown: collection and preliminary processing of data on the technical state of a real object; early detection of malfunctions, predicting the time of failure; service planning; optimization of financial and time resources for service. Equipping a real object with various sensors made it possible to continuously collect data on its technical condition, and technologies of the industrial Internet of things, such as Big Data and the predictive statistical model, predict failure times with high accuracy.
 The developed and implemented schemes for equipping an object with data collection equipment and a diagram of the flow of this data in the Internet of Things are presented. The basis of the data collection system is a microcontroller, a set of a crankshaft speed sensor and thermocouples, a multiplexer and 16-bit analog-to-digital converters that convert thermo-EMF of thermocouples. At the moment, channels for measuring the speed, coolant and exhaust gas temperatures have been implemented. It is proposed to use the ThingSpeak server as a remote resource as a cloud aggregator and carrier of this data. The MATLAB mathematical package integrated into the resource is used as a data analyzer.

Standards for the airworthiness and reliability of electrical systems and equipment are discussed in relation to the overall standards for the aircraft and its engines, and the effect of duplication of systems and equipment is considered.The control and protection of generating systems is examined in relation to experience, particularly of total power failure, and it is concluded that systems have not proved sufficiently reliable in the past to justify entire reliance on them in aircraft which depend on electrical supply for their continued flight. Various methods of providing duplicate and emergency supplies are described.The electrical controls of engines and propellers is discussed in relation to engine reliability, and recommendations are made for improvement.Inadvertent operation of some services (e.g. control-surface trimmers, propeller reversing) may constitute a major hazard to the aircraft; a detailed assessment of the causes of such operation is made and of methods of avoiding it.In an aircraft which carries large quantities of fuel and uses other inflammable fluids at high pressures, the extensive use of electricity may involve a considerable risk of fire or explosion unless the most stringent precautions are taken. Some of the risks and the measures taken to combat them are described.

The authors highlight that stationary condition monitoring systems for high-voltage equipment is an essential prerequisite of the Smart-Grid implementation in the industrial electric networks. Diagnostic systems commissioning is relevant for network and electric furnace transformers of iron and steel works. The system operation performance is deteriorated because of lack of the methods intended for general estimation of cumulative diagnostic attributes. The authors formulate requirements to a general technical condition criterion and develop dependences for its calculation. They consider the provisions for electric equipment condition estimation based on integral diagnostic attributes. The process system condition is displayed as a line diagram based on non-equal informative parameters. The paper provides an example of these methods application for a general parameter reflecting condition of the electric furnace transformer based on monitoring key gases dissolved in oil. It offers significance factors and thresholds of the deteriorated and pre-emergency conditions for each information parameter. It provides normalized diagrams of humidity and concentration of gases dissolved in oil. The authors draw conclusions on the effect gas concentration has on the facility general technical conditions. Thus, the efficiency of the developed methods for the estimate of furnace transformer technical conditions based on integral diagnostic features is confirmed. These methods are recommended for condition analysis based on partial discharge monitoring. They may also be adopted for the diagnostics of electrical equipment in on-line monitoring systems.

Currently, in order to ensure the required fault-proof level of technological processes in agricultural production, the following strategies among the main ones: afterfailure, planned systematic and a mixed electrical equipment maintenance and repair strategy. The pervasion of information technology in this segment was not widespread. Application of information technologies in conjunction with implementation of risk-oriented strategy is one of the promising directions of electrical equipment maintenance optimization, yet requires adaptation to sectoral characteristics of agricultural production. The article describes the problem of determining the optimal conditions for adjusting the frequency of maintenance and repair of electrical equipment on the basis of risk-oriented approach. To solve this problem, the equation of annual costs for maintenance and repair of electrical equipment was drawn up taking into account the parameter of its failure risk. The relationship between the change of relative risk and the service frequency was established; based on it, the ranges of frequency adjustment of maintenance and ongoing repair were determined.

Chapter 10 - Impact of internet of things and digital twin on manufacturing era