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The development of the automotive industry is associated with the rapid advancement of onboard systems. In addition, intensive development in the electronics and control systems industry has resulted in a change in the approach to the issue of assistance systems in vehicles. Classic hydraulic systems have been almost completely replaced by modern electric power steering (EPS) systems, especially in citizen vehicles. This paper focuses on fault detection algorithms for EPS, along with the available tools to aid development and verification. The article discusses in detail the current state of knowledge in this area. The principle of operation of the EPS system and the influence of the structure of the mechanical system on its operation, in particular the characteristics of the ground–tire contact, are presented. Various error identification methods are presented, including those based mainly on a combination of tests of real objects as well as those combined with modern hardware-in-the-loop (HIL) equipment and virtual vehicle environment software, enabling the development of new diagnostic methods, enhancing the security, reliability, and energy control in the vehicle. A review of the literature indicates that although many algorithms which enable fault detection at an early stage are described, their potential for use in a vehicle is highly limited. The reason lies in simplifications, including models and the operating EPS temperature range. The most frequently used simplification of the model is its linearization, which significantly reduces the calculation time; however, this significantly reduces the accuracy of the model, especially in cases with a large range of system operation. The need for methods to detect incipient faults is important for the safety and reliability of the entire car, not only during regular use but also especially during life-saving evasive maneuvers. [ABSTRACT FROM AUTHOR]

Currently, applications of the algorithms based on artificial intelligence (AI) principles can be observed in various fields. This can be also noticed in the wide area of electrical drives. Consideration has been limited to neural networks; however, the tasks for the models can be defined as follows: control, state variable estimation, and diagnostics. In the subsequent sections of this paper, electrical machines, as well as power electronic devices, are assumed as the main objects. This paper describes the basics, issues, and possibilities related to the used tools and explains the growing popularity of neural network applications in automatic systems with electrical drives. The paper begins with the overall considerations; following that, the content proceeds with the details, and two specific examples are shown. The first example deals with a neural network-based speed controller tested in a structure with a synchronous reluctance motor. Then, the implementation of recurrent neural networks as state variable estimators is analyzed. The achieved results present a precise estimation of the load speed and the shaft torque signals from a two-mass system. All descriptions in the article are considered in the context of the trends and perspectives in modern algorithm applications for electrical drives. [ABSTRACT FROM AUTHOR]

This work is an introduction of acoustic emission (AE) signals used in order to detect the malfunction of selected semiconductor elements. The authors proposed the use of internally generated signals (elastic waves) of acoustic emission leading to the detection of the pre-fail state of switching IGBT transistors. The analysis of the AE signals allows the creation of a reference pattern of properly working transistors and at the same time the identification of abnormal signals, which are generated by a defective element. Unlike many papers, this article shows experimental results demonstrating a comparison of undamaged, properly working and defective IGBT transistors which can be used, for example, as a reference for diagnostic tools. Analysis of the signal in the frequency domain obtained from the faulty transistor (overheated or with damaged casing) shows the presence of additional frequencies which can indicate the imminent occurrence of critical damage. [ABSTRACT FROM AUTHOR]

The installed wind power capacity is growing worldwide. Remote condition monitoring of wind turbines is employed to achieve higher up-times and lower maintenance costs. Machine learning approaches can be used for detecting developing faults in wind turbines in their earlier occurrence. However, training fault detection models may require large amounts of past and present data. These data are often not available or not representative of the current operation behaviour. These data can be acquired with supervisory control and data acquisition systems. Note also that newly commissioned wind farms lack data from previous operation, whilst older installations may also lack representative working condition data as a result of control software updates or component replacements. After such events, a turbine's operation behaviour can change significantly so its data are no longer representative of its current behaviour. Therefore, this paper shows that cross–turbine transfer learning can improve the accuracy of fault detection models in turbines with scarce data from supervisory control and data acquisition systems. In particular, it highlights that combining the knowledge from turbines with scarce data and turbines with plentiful data enables earlier detection of faults than prior art methods. In this way, the reuse and the knowledge transfer across wind turbines allows us to overcome this lack of data, thus enabling accurate fault detection in wind turbines. [ABSTRACT FROM AUTHOR]

Maintenance is crucial for aircraft engines because of the demanding conditions to which they are exposed during operation. A proper maintenance plan is essential for ensuring safe flights and prolonging the life of the engines. It also plays a major role in managing costs for aeronautical companies. Various forms of degradation can affect different engine components. To optimize cost management, modern maintenance plans utilize diagnostic and prognostic techniques, such as Engine Health Monitoring (EHM), which assesses the health of the engine based on monitored parameters. In recent years, various EHM systems have been developed utilizing computational techniques. These algorithms are often enhanced by utilizing data reduction and noise filtering tools, which help to minimize computational time and efforts, and to improve performance by reducing noise from sensor data. This paper discusses the various mechanisms that lead to the degradation of aircraft engine components and the impact on engine performance. Additionally, it provides an overview of the most commonly used data reduction and diagnostic and prognostic techniques. [ABSTRACT FROM AUTHOR]

Hydraulic power systems are commonly used in heavy industry (usually highly energy-intensive) and are often associated with high power losses. Designing a suitable system to allow an early assessment of the wear conditions of components in a hydraulic system (e.g., an axial piston pump) can effectively contribute to reducing energy losses during use. This paper presents the application of a deep machine learning system to determine the efficiency state of a multi-piston positive displacement pump. Such pumps are significant in high-power hydraulic systems. The correct operation of the entire hydraulic system often depends on its proper functioning. The wear and tear of individual pump components usually leads to a decrease in the pump's operating pressure and volumetric losses, subsequently resulting in a decrease in overall pump efficiency and increases in vibration and pump noise. This in turn leads to an increase in energy losses throughout the hydraulic system, which releases excess heat. Typical failures of the discussed pumps and their causes are described after reviewing current research work using deep machine learning. Next, the test bench on which the diagnostic experiment was conducted and the selected operating signals that were recorded are described. The measured signals were subjected to a time–frequency analysis, and their features, calculated in terms of the time and frequency domains, underwent a significance ranking using the minimum redundancy maximum relevance (MRMR) algorithm. The next step was to design a neural network structure to classify the wear state of the pump and to test and evaluate the effectiveness of the network's recognition of the pump's condition. The whole study was summarized with conclusions. [ABSTRACT FROM AUTHOR]

In this study, the authors explored the issues of the Soderbergh electrolysis cell's increase in economic efficiency. This result was achieved by analyzing the temperature fields of the electrolysis cell in order to determine the overheating points. As a result, it led to the determination of the points of the hearth bottom's subsequent breakdown, causing the failure of the electrolysis cell. In this paper, the mathematical modeling of the temperature fields using a spatially distributed mathematical model and conducted experimental studies were carried out. The mathematical model also provides the opportunity to measure the temperature field in the hearth bottom (at the bottom) of the OA-300M electrolysis cell. The results of the given research can be used to solve the experimental determination of the hearth bottom internal defect problem. [ABSTRACT FROM AUTHOR]

Power transformers play a critical role in electric power networks. Such transformers can suffer failures due to multiple stresses and aging. Thus, assessment of condition and diagnostic techniques are of great importance for improving power network reliability and service continuity. Several techniques are available to diagnose the faults within the power transformer. Frequency response analysis (FRA) method is a powerful technique for diagnosing transformer winding deformation and several other types of problems that are caused during manufacture, transportation, installation and/or service life. This paper provides a comprehensive review on FRA methods and their applications in diagnostics and fault identification for power transformers. The paper discusses theory and applications of FRA methods as well as various issues and challenges faced in the application of this method. [ABSTRACT FROM AUTHOR]

This paper aims to update the review of diagnostic techniques for rotating electrical machines of different type and size. Each of the main sections of the paper is focused on a specific component of the machine (stator and rotor windings, magnets, bearings, airgap, load and auxiliaries, stator and rotor laminated core) and divided into subsections when the characteristics of the component are different according to the type or size of the machine. The review considers both the techniques currently applied on field for the diagnostics of the electrical machines and the novel methodologies recently proposed by the researchers in the literature. [ABSTRACT FROM AUTHOR]

Machine-learning diagnostic systems are widely used to detect abnormal conditions in electrical equipment. Training robust and accurate diagnostic systems is challenging because only small databases of abnormal-condition data are available. However, the performance of the diagnostic systems depends on the quantity and quality of the data. The training database can be augmented utilizing data augmentation techniques that generate synthetic data to improve diagnostic performance. However, existing data augmentation techniques are generic methods that do not include additional information in the synthetic data. In this paper, we develop a model-based data augmentation technique integrating computer-implementable electromechanical models. Synthetic normal- and abnormal-condition data are generated with an electromechanical model and a stochastic parameter value sampling method. The model-based data augmentation is showcased to detect an abnormal condition of a distribution transformer. First, the synthetic data are compared with the measurements to verify the synthetic data. Then, ML-based diagnostic systems are created using model-based data augmentation and are compared with state-of-the-art diagnostic systems. It is shown that using the model-based data augmentation results in an improved accuracy compared to state-of-the-art diagnostic systems. This holds especially true when only a small abnormal-condition database is available. [ABSTRACT FROM AUTHOR]

The utilization of hydrogen fuel in gas turbines brings significant changes to the thermophysical properties of flue gas, including higher specific heat capacities and an enhanced steam content. Therefore, hydrogen-fueled gas turbines are susceptible to health degradation in the form of steam-induced corrosion and erosion in the hot gas path. In this context, the fault diagnosis of hydrogen-fueled gas turbines becomes indispensable. To the authors' knowledge, there is a scarcity of fault diagnosis studies for retrofitted gas turbines considering hydrogen as a potential fuel. The present study, however, develops an artificial neural network (ANN)-based fault diagnosis model using the MATLAB environment. Prior to the fault detection, isolation, and identification modules, physics-based performance data of a 100 kW micro gas turbine (MGT) were synthesized using the GasTurb tool. An ANN-based classification algorithm showed a 96.2% classification accuracy for the fault detection and isolation. Moreover, the feedforward neural network-based regression algorithm showed quite good training, testing, and validation accuracies in terms of the root mean square error (RMSE). The study revealed that the presence of hydrogen-induced corrosion faults (both as a single corrosion fault or as simultaneous fouling and corrosion) led to false alarms, thereby prompting other incorrect faults during the fault detection and isolation modules. Additionally, the performance of the fault identification module for the hydrogen fuel scenario was found to be marginally lower than that of the natural gas case due to assumption of small magnitudes of faults arising from hydrogen-induced corrosion. [ABSTRACT FROM AUTHOR]

Understanding a partial discharge mechanism at direct current (DC) is an actual research topic that requires both modeling, simulations and measurements. This paper describes an influence of insulating material properties on partial discharges at DC voltage. Modifications of the traditional model reflecting the conditions of partial discharges (PD) inception and post discharge processes at DC voltage have been proposed. The aim was to show the partial discharge mechanisms and draw attention to the role of parameters of insulation materials adjacent to the cavity at DC voltage. The investigations were performed on two kinds of dielectric material used in power cables. Various combinations of specimens were designed to reveal the effect of the material resistivity on the PD activity. Key observations referred to the impact of the void adjacent material resistance on the partial discharge inception voltage threshold at DC voltage. The modified PD model was applied to analyze both inception and post discharge recovery stage. The role of dielectric properties of material adjacent to the void was investigated, highlighting its impact during static inception stage and in charging stage. Despite many simplifications introduced in the model, measurement results have confirmed the role of the dielectric material surrounding the void on partial discharge dynamics. The average time interval between PD pulses revealed a systematic relationship with respect to the applied voltage and specimen resistivity. This value can be considered in the future research for diagnostic indicator at DC voltage. [ABSTRACT FROM AUTHOR]

This paper presents current flow in cable screens and a cable screen earthing system. Moreover, the paper presents the methodology for the detection of problems in cable screens, such as open phase in a cable screen and high contact resistance of a cable screen in a cable joint. The is based on cable screen current measurements and allows for localization of the erroneous connection–high contact resistance or open phase. The phenomenon is simulated in PowerFactory Software. Moreover, exemplary cable screen current measurements are presented. [ABSTRACT FROM AUTHOR]

The use of low-emission combustion technologies in power boilers has contributed to a significant increase in the rate of high-temperature corrosion in boilers and increased risk of failure. The use of low quality biomass and waste, caused by the current policies pressing on the decarbonization of the energy generation sector, might exacerbate this problem. Additionally, all of the effects of the valorization techniques on the inorganic fraction of the solid fuel have become an additional uncertainty. As a result, fast and reliable corrosion diagnostic techniques are slowly becoming a necessity to maintain the security of the energy supply for the power grid. Non-destructive testing methods (NDT) are helpful in detecting these threats. The most important NDT methods, which can be used to assess the degree of corrosion of boiler tubes, detection of the tubes' surface roughness and the internal structural defects, have been presented in the paper. The idea of the use of optical techniques in the initial diagnosis of boiler evaporators' surface conditions has also been presented. [ABSTRACT FROM AUTHOR]

In the resilient and reliable electrical power system, the condition of high voltage insulation plays a crucial role. In the field of high voltage insulation integrity, the partial discharge (PD) inception and development trends are essential for assessment criteria in diagnostics systems. The observed trend to employ more and more sophisticated algorithms with machine learning features and artificial intelligence (AI) elements is observed everywhere. The classification and identification of features in PD images is perceived as a critical requirement for an effective high voltage insulation diagnosis. In this context, techniques allowing for anomaly detection, trends observation, and feature extraction in partial discharge patterns are important. In this paper, the application of few algorithms belonging to image processing, machine learning and optical flow is presented. The feature extraction refers to image segmentation and detection of coherent forms in the images. The anomaly detection algorithms can trigger early detection of the trend changes or the appearance of a new discharge form, and hence are suitable for PD monitoring applications. Anomaly detection can also handle transients and disturbances that appear in the PD image as an indication of an abnormal state. The future monitoring systems should be equipped with trend evolution algorithms. In this context, two examples of insulation aging and application of PD-based monitoring are shown. The first one refers to deep convolutional neural networks used for classification of deterioration stages in high voltage insulation. The latter one demonstrates application of optical flow approach for motion detection in partial discharge images. The motivation for the research was the strive to machine-controlled pattern analysis, leading towards intelligent PD-based diagnostics. [ABSTRACT FROM AUTHOR]

The paper discusses the results of a study carried out to determine the thermal condition of a conveyor power unit using a thermal imaging camera. The tests covered conveyors in the main haulage system carrying coal from a longwall. The measurements were taken with a thermal imaging diagnostic method which measures infrared radiation emitted by an object. This technology provides a means of assessing the imminence and severity of a possible failure or damage. The method is a non-contact measuring technique and offers great advantages in an underground mine. The thermograms were analysed by comparing the temperature distribution. An analysis of the operating time of the conveyors was also carried out and the causes of the thermal condition were determined. The main purpose of the research was to detect changes in thermal state during the operation of a belt conveyor that could indicate failure and permit early maintenance and eliminate the chance of a fire. The article also discusses the construction and principle of operation of a thermal imaging camera. The findings obtained from the research analysis on determining the thermal condition of the conveyor drive unit are a valuable source of information for the mine's maintenance service. [ABSTRACT FROM AUTHOR]

This paper presents the identification and classification of steel cord failures in the conveyor belt core based on an analysis of a two-dimensional image of magnetic field changes recorded using the Diagbelt system around scanned failures in the test belt. The obtained set of identified changes in images, obtained for numerous parameters settings of the device, were the base for statistical analysis. This analysis makes it possible to determine the Pearson's linear correlation coefficient between the parameters being changed and the image of the failures. In the second stage of the research, artificial intelligence methods were applied to construct a multilayer neural network (MLP) and to teach it appropriate identification of damage. In both methods, the same data sets were used, which made it possible to compare methods. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motors (PMSMs) are becoming more popular, both in industrial applications and in electric and hybrid vehicle drives. Unfortunately, like the others, these are not reliable drives. As in the drive systems with induction motors, the rolling bearings can often fail. This paper focuses on the possibility of detecting this type of mechanical damage by analysing mechanical vibrations supported by shallow neural networks (NNs). For the extraction of diagnostic symptoms, the Fast Fourier Transform (FFT) and the Hilbert transform (HT) were used to obtain the envelope signal, which was subjected to the FFT analysis. Three types of neural networks were tested to automate the detection process: multilayer perceptron (MLP), neural network with radial base function (RBF), and Kohonen map (self-organizing map, SOM). The input signals of these networks were the amplitudes of harmonic components characteristic of damage to bearing elements, obtained as a result of FFT or HT analysis of the vibration acceleration signal. The effectiveness of the analysed NN structures was compared from the point of view of the influence of the network architecture and various parameters of the learning process on the detection effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative approach to classify the losses related to photovoltaic (PV) systems, through the use of thermographic non-destructive tests (TNDTs) supported by artificial intelligence techniques. Low electricity production in PV systems can be caused by an efficiency decrease in PV modules due to abnormal operating conditions such as failures or malfunctions. The most common performance decreases are due to the presence of dirt on the surface of the module, the impact of which depends on many parameters and conditions, and can be identified through the use of the TNDTs. The proposed approach allows one to automatically classify the thermographic images from the convolutional neural network (CNN) of the system, achieving an accuracy of 98% in tests that last a couple of minutes. This approach, compared to approaches in literature, offers numerous advantages, including speed of execution, speed of diagnosis, reduced costs, reduction in electricity production losses. [ABSTRACT FROM AUTHOR]

Artificial intelligence-based solutions and applications have great potential in various fields of electrical power engineering. The problem of the electrical reliability of power equipment directly refers to the immunity of high-voltage (HV) insulation systems to operating stresses, overvoltages and other stresses—in particular, those involving strong electric fields. Therefore, tracing material degradation processes in insulation systems requires dedicated diagnostics; one of the most reliable quality indicators of high-voltage insulation systems is partial discharge (PD) measurement. In this paper, an example of the application of a neural network to partial discharge images is presented, which is based on the convolutional neural network (CNN) architecture, and used to recognize the stages of the aging of high-voltage electrical insulation based on PD images. Partial discharge images refer to phase-resolved patterns revealing various discharge stages and forms. The test specimens were aged under high electric stress, and the measurement results were saved continuously within a predefined time period. The four distinguishable classes of the electrical insulation degradation process were defined, mimicking the changes that occurred within the electrical insulation in the specimens (i.e., start, middle, end and noise/disturbance), with the goal of properly recognizing these stages in the untrained image samples. The results reflect the exemplary performance of the CNN and its resilience to manipulations of the network architecture and values of the hyperparameters. Convolutional neural networks seem to be a promising component of future autonomous PD expert systems. [ABSTRACT FROM AUTHOR]

For several decades, the design and manufacture of electrical machines has been considered a technically mature area and, as a result, research and development in the area has been extremely limited, even though this is a crucial technology in the application of electrical energy. Electrical machines are used in over 80% of the world's energy conversion processes—first to create electrical energy, which can be easily transmitted, and second to convert that energy into mechanical form for applications ranging from dishwashers to transportation, and from medical devices to those used for industrial processes. Today, two technologies are changing this. The first is the development of power electronic drives and the second is the introduction of additive manufacturing technology. The latter technology has opened up new areas for innovation and research, and many conventional processes are likely to become obsolete. Considering the overall consumption of electricity by electrical machines, the design freedom granted by the novel production technology gives the opportunity for even more efficient, object-oriented machines to be built, with a lower environmental impact and less raw material consumption. If this technology can be developed to maturity, it would have a significant positive impact on the desired green transition that is being pursued all over the world. [ABSTRACT FROM AUTHOR]

The presented article concerns issues related to the diagnostics of the technical condition of the insulation of electrical machines. It discusses the importance of the operational supervision, maintenance and diagnostics of electrical machine insulation systems. The structure of the insulation system is presented and known solutions for making winding insulation are described. The negative impact of conditions and various exposures on the technical condition of the insulation system is described. Special attention is focused on the review of available diagnostic methods for insulating systems of electrical machines. These methods have been arranged in a systematic order according to the type of tests to be carried out. [ABSTRACT FROM AUTHOR]

The well-known gear tooth defects such as root cracks and flank spalls have been widely investigated in previous studies to model their effects on the time varying mesh stiffness (TVMS) and consequently the dynamic response of motor-gearbox systems. Nevertheless, the effect of assembly errors such as the center distance and the eccentricity has been less considered in past works. Determining the signature of these errors on the system response can help for their early detection and diagnostic to avoid overloading and failure of gears. An original geometric-based method combined with the potential energy method is proposed in this paper to accurately model the effect of these assembly errors on the TVMS of mating spur gear pairs. This is achieved by updating the line of action equation (LOA) at each meshing step using the actual coordinates of gear centers and employing a contact detection algorithm (CDA) to determine the actual contact points coordinates. An electrical model of a three-phase induction machine was then coupled with a dynamic model of a one-stage spur gear system to simulate the effect of assembly errors on the electromechanical response of the motor-gearbox system. The simulation results showed that the center distance error induces a reduction in the TVMS magnitude and the contact ratio, whereas the eccentricity error causes a double modulation of the TVMS magnitude and frequency. In addition, the results showed that assembly errors can be detected and diagnosed by analyzing the system vibration and the motor phase-current. [ABSTRACT FROM AUTHOR]

Accurate component analytical solution is very important to gas path prognostics and diagnostics of a gas turbine. However, due to the highly complex nonlinear behavior of component performance, the nonlinear relationships between various key parameters still should be further studied. For this purpose, a new component analytical solution is proposed to enhance the current adaptation and diagnostics scheme of gas turbines. First, the tuning factors are defined to construct the enhanced component analytical solution and identify the nonlinear behaviors more accurately. Second, a sensitivity analysis for tuning factors is performed to understand the effect of each factor on the shape of component maps. Then, a particle swarm optimization algorithm is used to capture the optimal tuning factors, and then the performance adaptation is implemented. Finally, the proposed method has been validated with normal field data and fouling fault field data of a PGT25+ gas turbine. Compared with two earlier off-design point adaptation methods, the proposed method shows some advantages in performance adaptation and diagnostics. [ABSTRACT FROM AUTHOR]

The contemporary striving for increased speed in rail transport, with simultaneous requirements in terms of travel comfort, makes the safety of rail vehicles one of the priorities in the process of passenger rail transport. It is obvious that safety in this case is determined by: the technical condition of the vehicle, the condition of the railway infrastructure and the conditions of cooperation between the railway vehicle and the track. Railway vehicles running safety tests can be carried out on-line, in real operating conditions, and off-line with the use of mathematical models and simulation methods. The on-line research does not assess the impact of the parameter characterizing the track geometry, i.e., rail inclination, on the driving safety, and the deviation of the shape of wheel and rail running surfaces from the nominal dimensions is rarely diagnosed and taken into account in the tests. The considerations in this article are devoted to the discussion of the effects on the safety of a rail vehicle, resulting from possible changes in the inclination of the rails and deviations of the rail profile from the nominal dimensions. The research, the results of which are presented in the article, were carried out using off-line methods for the wheelset, using mathematical models, but taking into account the forces resulting from the interactions of the bogie frame and the railway wagon body and the real track geometry. [ABSTRACT FROM AUTHOR]

Pipelines are structural elements of many systems. For example, they are used in water supply and heat supply systems, in chemical production facilities, aircraft manufacturing, and in the oil and gas industry. Accidents in piping systems result in significant economic damage. An important factor for ensuring the reliability of energy transportation systems is the assessment of real technical conditions of pipelines. Methods for assessing the state of pipeline systems by their vibro-acoustic parameters are widely used today. Traditionally, the Fourier transform is used to process vibration signals. However, as a rule, the oscillations of the pipe-liquid system are non-linear and non-stationary. This reduces the reliability of devices based on the implementation of classical methods of analysis. The authors used neural network methods for the analysis of vibro-signals, which made it possible to increase the reliability of diagnosing pipeline systems. The present work considers a method of neural network analysis of amplitude-frequency measurements in pipelines to identify the presence of a defect and further clarify its variety. [ABSTRACT FROM AUTHOR]