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Abstract Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation.

Abstract The insulation paper is a crucial factor for evaluating the insulation status of transformers. The traditional evaluation methods for insulation paper are dissolved gas component content analysis (CO and CO2) and furfural (2‐FAL) content analysis in oil. The detection principle of the former leads to its low accuracy, while the field application effect of the latter is not ideal due to the easy oxidation of furfural and low content. Methanol, result of its good stability and high production compared with other marker products (CO, CO2, 2‐FAL) has been investigated as a novel marker for aging evaluation of transformer insulating paper in recent years. The results of the investigation indicated that there is a good correlation between the content of methanol in oil and the degree of polymerization under laboratory conditions, which reflected the insulation aging state of the transformer insulating paper more accurately. This paper summarizes the current research status of methanol formation mechanism, detection methods and aging evaluation in insulating oil, and points out the key problems to be solved and development prospects, hoping to provide relevant reference for aging evaluation of oil immersed power transformer.

Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation. [ABSTRACT FROM AUTHOR]

The insulation paper is a crucial factor for evaluating the insulation status of transformers. The traditional evaluation methods for insulation paper are dissolved gas component content analysis (CO and CO2) and furfural (2‐FAL) content analysis in oil. The detection principle of the former leads to its low accuracy, while the field application effect of the latter is not ideal due to the easy oxidation of furfural and low content. Methanol, result of its good stability and high production compared with other marker products (CO, CO2, 2‐FAL) has been investigated as a novel marker for aging evaluation of transformer insulating paper in recent years. The results of the investigation indicated that there is a good correlation between the content of methanol in oil and the degree of polymerization under laboratory conditions, which reflected the insulation aging state of the transformer insulating paper more accurately. This paper summarizes the current research status of methanol formation mechanism, detection methods and aging evaluation in insulating oil, and points out the key problems to be solved and development prospects, hoping to provide relevant reference for aging evaluation of oil immersed power transformer. [ABSTRACT FROM AUTHOR]

The condition assessment of oil–paper insulation is a vital issue in transformer management. To investigate the effect of moisture content on the accuracy of the prediction model, accelerated ageing experiments were performed with different initial moisture content (IMC) in laboratory condition. Two types of multi-parameter lifetime prediction models, e.g. with and without consideration of IMC, were presented for comparison purpose. The authors found that a better accuracy for its prediction results could be achieved by establishing the prediction models with the consideration of IMC. Besides, models considering IMC have better economic feasibility than that without considering IMC(non-IMC) as its total cost is cheaper according to life cycle cost (LCC) calculations. In addition, failure probability based on the proposed prediction models was also calculated as a direct indicator for evaluating the oil–paper insulation status. [ABSTRACT FROM AUTHOR]

Nowadays, managers try to use scientific methods to increase the quality of goods, and simultaneously reduce their final prices as well. Condition Monitoring (CM) is one of the methods which has been popular among industrial managers in recent years and of course it has provided many benefits for organizations. Analyzing the vibrations is one of the condition monitoring methods, and by using it we can avoid rework, and reduce the company's costs considerably. To show this effect for efficiency improvement of production process, Mazandaran Wood and Paper Industries has been selected as a case study, and the vibration analysis, one method of the conditions monitoring has been applied. In this research, we have shown how to prevent stoppages and damages of the equipment by in-time awareness and by imposing preventive repairs. Secondly, considerable conservation in consumption of parts and manpower will be achieved for repairing and maintenance of equipment in order to guide the organization toward efficiency improvement.

Industrial collaborative robots play an essential role in smart manufacturing because they improve productivity while also ensuring workplace safety. However, the development of prognostic and health management systems to ensure the reliability of these robots has been a major challenge due to the lack of fault data. This paper proposed a digital twin scheme based on the fusion of the robot kinematic and dynamic models' information down to the powertrains (i.e., the joints motor, and gear) along with the control algorithms and uncertainty accommodation based upon deep learning. The presented digital twin concept has the potential to propel simulation-based fault prediction. We also highlight and discuss challenges and opportunities around the development of the hybrid digital twin for condition monitoring of industrial collaborative robots. [ABSTRACT FROM AUTHOR]

It is important to monitor the water concentration in the insulation of aging power transformers, because over time the insulation becomes wet, due to water ingress from the atmosphere and water formed in chemical reactions involving oxygen. If the water concentration is sufficiently high, the insulation will age prematurely [1] and the transformer may fail if overloaded [2]. In a previous article [3] we reported on the measurement of water concentration in the insulation paper of a power transformer that had been in service for only seven years. The waterconcentration data obtained using on-line water-activity probes were compared with corresponding data obtained from dielectric response measurements. In that work cellulose adsorption isotherms were used to process the water-activity-probe data. However, according to [4] and [5], inaccuracies in the waterconcentration estimates may result from the use of adsorption isotherms. According to [6], these inaccuracies may exceed 200%. The probable main causes are the transformer insulation materials not reaching thermal equilibrium, the water concentration within the insulation not being uniform, and variability in the properties of the insulating materials. [ABSTRACT FROM AUTHOR]

In conventional frequency domain spectroscopy (FDS) measurement, sinusoidal excitation is applied to a composite oil‐paper insulation under test, for a minimum of two cycles over a wide range of frequencies typically starting from 1 kHz to 1 mHz or even upto 0.1 mHz. The frequency sweep down to 1 mHz, is inevitably time consuming especially in lower frequency ranges. Considering the aforesaid fact, the main aim of the present study is to reduce overall FDS measurement time. The insulation under test is subjected to some fraction of one full cycle of excitation, and the corresponding dielectric response current is measured for each fraction. Using least square curve‐fitting technique, response current over two cycles is predicted from which dielectric dissipation factor (tanδ) is calculated. For this contribution, sinusoidal and non‐sinusoidal excitations in form of triangular waveform with five different initial slopes are used as excitation voltage. The technique is experimentally validated on three samples with different preset moisture contents prepared in the laboratory. The dielectric dissipation factor obtained by using the proposed technique is compared with the conventional method using two cycles, which show negligible differences. Most important of all is the overall test time, which can be brought down significantly when compared with conventional FDS measurement. [ABSTRACT FROM AUTHOR]

Conductivity is an important parameter which has definite correlation with the amount of moisture present within the transformer insulation. However, in existing methods conductivity is usually estimated from time domain (PDC) measurement, which is inadvertently noise prone and time consuming. Considering this issue, in this article presents a novel technique for conductivity estimation for moisture sensing of oil-paper insulation using frequency domain spectroscopy measurement (FDS). To this end, FDS measurement of oil-paper insulation was performed from 50mHz to 1kHz to obtain the imaginary complex permittivity $\varepsilon ''$ ($\omega$), which is a function of conductivity ($\sigma$) as well as imaginary complex susceptibility $\chi ''$ ($\omega$). From the knowledge of frequency variation of both $\sigma $ and $\chi ''$ ($\omega$), we developed an electrical equivalent model of the insulation. The equivalent model parameters were optimized until $\varepsilon ''$ ($\omega$) computed from the proposed model and that obtained from original FDS measurement were almost identical. The conductivity values obtained from the equivalent model showed good agreement when compared with PDC measurement. To validate the practicability of the proposed method, experiments were conducted on various test samples and also on a few real life transformers. Most importantly, the measurement time was found to be reduced by 78.5%, enabling diagnosis of transformer insulation at a significantly reduced time. [ABSTRACT FROM AUTHOR]

Cellulosic insulation materials are very widely used in power transformers, in designs up to 1,500 MVA [1]. Cellulosic insulation is formed of either wood, pressboard, or Kraft paper. The wood and thick sections of pressboard are used to manufacture the base of the transformer; pressboard is also used for the spacers and barriers of the windings, and the paper is wrapped around the current carrying conductors. The water content of this cellulosic insulation must be monitored, because if it becomes too high, a transformer can fail on overload, and will reach the end of its functional life sooner because the paper insulation will age faster [2]. Eventually, the paper will become brittle, and will either tear or lift off the conductor. The water content of paper (WCP) is the ratio of the mass of adsorbed water to the mass of dry paper, usually expressed as a percentage. Water can migrate out of the wood and pressboard and around the system, eventually increasing the WCP. If the WCP becomes too high, the transformer may be dried out by the relevant utility [3]. [ABSTRACT FROM AUTHOR]

This paper proposes a method for estimation of tanδ of cellulosic parts in oil-paper insulation from frequency domain spectroscopy based on equivalent circuit model of oil-paper insulation in real-life transformers. Existing literature shows that overall condition of oil-paper insulation in transformer can be assessed by conventional frequency domain spectroscopy. Also, the condition of oil can be separately and easily predicted through analyzing its dielectric properties by taking out the oil samples from transformer. However, it is quite difficult to assess the dielectric properties of cellulosic parts in oil-paper insulation as it cannot be directly accessed from outside for an operating transformer. Considering the aforesaid fact, this paper presents a segregated equivalent circuit model of composite oil-paper insulation that can separately estimate the tanδ profiles for oil as well as cellulosic parts. The tanδ profiles of cellulosic parts obtained from the equivalent circuit model have been validated experimentally. Results show that the tan? profile obtained from experimental result holds good agreement with that of the proposed model. [ABSTRACT FROM PUBLISHER]

Frequency Domain Spectroscopy is a popular non-invasive method that is widely used for condition assessment of oil-paper insulation in real-life power transformers. Conventional Frequency Domain Spectroscopy is done using sinusoidal excitation over a wide frequency range. In this work, a different approach has been taken to investigate some more possibilities of Frequency Domain Spectroscopy using non-sinusoidal excitation for a better condition assessment of oil-paper insulation. For that, triangular waveshape was chosen as non-sinusoidal excitations whose slope can be changed to vary the harmonic content and amplitudes of constituent components. Samples with preset moisture contents that emulate the oil-paper insulation of real-life transformer have been prepared in the laboratory for the assessment of insulation condition. For a comparative study, both sinusoidal and triangular excitations having same peak as well as time period are applied to the prepared samples under controlled environmental condition and corresponding dielectric response currents are measured. From the applied voltages and corresponding dielectric response currents, dielectric dissipation factors are calculated and compared. It has been observed that use of nonsinusoidal excitation provides better results than sinusoidal excitation in the estimation of paper moisture content. It has also been observed that among the applied non-sinusoidal excitations, a particular waveform provides more accurate information about the paper moisture content. [ABSTRACT FROM AUTHOR]

When a wheel passes over a rail surface irregularity, the resulting vehicle excitations lead to the application of additional system forces to both the track and the vehicle. These forces contribute to an accelerated track geometry deterioration, which in turn results in increased costs. In a recent paper, a clear correlation between the presence of rail irregularities and poor track geometry quality was demonstrated. Rail surface irregularities thereby were quantified by raw data of a chord-based optical measurement system mounted on the regular track recording vehicle in Austria. This paper deals with deconvolution of the recorded data in order to guarantee irregularity quantification without any distortion. Two different deconvolution approaches are developed and validated by additional measurements. Using the deconvoluted data, previously published evaluations were repeated, and the impact of using deconvoluted data instead of chord values was analysed. The correlation between short-wave effects and track geometry quality can not only be confirmed; it is even stronger than predicted by the chord data. The results of the analysis demonstrate that irregularities with amplitudes exceeding 0.08 mm contribute to an accelerated deterioration in track geometry. Amplitudes of a greater severity result in track geometry levels that are up to 120% inferior to the average. [ABSTRACT FROM AUTHOR]

The water concentration in transformer paper insulation must be monitored because, if it becomes too high, the paper insulation will age rapidly [1]-[4] and the transformer may fail on overload [5]-[9]. A failure can occur on overload if the temperature of the paper rises sufficiently to cause evolution of water bubbles. If these bubbles cause dielectric failure of the oil gap, the transformer may fail catastrophically. Paper insulation becomes brittle as it ages, and eventually becomes too brittle to act as an effective insulator. Oxygen increases the aging rate because the acids created by chemical reactions occurring in the oil, paper, and oxygen react further with water [10]-[12]. A study of the thermal aging of Kraft paper found that wet oxygenated paper degrades 40 times faster than the same paper heated in a dry oxygen-free environment [4]. The paper within a transformer is likely to degrade fastest at the top of the winding, where it is hottest [13]. Since the water concentration in the paper significantly influences its aging, it is important that the water concentration be measured accurately at the top of the winding. [ABSTRACT FROM PUBLISHER]

Frequency domain spectroscopy (FDS) is being used to assess the insulation condition of oil–paper power transformers. However, it has to date only been implemented on de‐energised transformers, which requires the transformers to be shut down for an extended period and may cause significant costs. To solve this issue, a newly improved monitoring method based on the FDS principle is proposed to implement the dielectric measurement on energised transformers. Moreover, a chirp waveform excitation and its novel processing method are introduced. Compared with the conventional FDS results, dielectric results from the energised insulation system have higher tanδ values because of the increased losses. To further understand the insulation ageing process, the effects of the geometric capacitance are removed from the measured imaginary admittance of the insulation system to enhance the ageing signature. The resulting imaginary admittance is then shown to correlate well with the central time constant in return voltage measurements results. The proposed methods address the issues on techniques used on energised transformers and provide a clue for on‐line FDS diagnostic application. [ABSTRACT FROM AUTHOR]

It is necessary to monitor the water content of the paper insulation (WCP) within a power transformer because, if it becomes too large, the paper insulation may age prematurely or the transformer may fail on overload. It is nearly always unfeasible to extract a paper insulation sample from a transformer for a laboratory measurement of its water content because it involves removing part of the insulation. [ABSTRACT FROM AUTHOR]

In this paper, research was conducted on anomaly detection of wheel flats. In the railway sector, conducting tests with actual railway vehicles is challenging due to safety concerns for passengers and maintenance issues as it is a public industry. Therefore, dynamics software was utilized. Next, STFT (short-time Fourier transform) was performed to create spectrogram images. In the case of railway vehicles, control, monitoring, and communication are performed through TCMS, but complex analysis and data processing are difficult because there are no devices such as GPUs. Furthermore, there are memory limitations. Therefore, in this paper, the relatively lightweight models LeNet-5, ResNet-20, and MobileNet-V3 were selected for deep learning experiments. At this time, the LeNet-5 and MobileNet-V3 models were modified from the basic architecture. Since railway vehicles are given preventive maintenance, it is difficult to obtain fault data. Therefore, semi-supervised learning was also performed. At this time, the Deep One Class Classification paper was referenced. The evaluation results indicated that the modified LeNet-5 and MobileNet-V3 models achieved approximately 97% and 96% accuracy, respectively. At this point, the LeNet-5 model showed a training time of 12 min faster than the MobileNet-V3 model. In addition, the semi-supervised learning results showed a significant outcome of approximately 94% accuracy when considering the railway maintenance environment. In conclusion, considering the railway vehicle maintenance environment and device specifications, it was inferred that the relatively simple and lightweight LeNet-5 model can be effectively utilized while using small images. [ABSTRACT FROM AUTHOR]

Frequency domain spectroscopy is a potential tool for non-invasive condition assessment of oil-paper insulation in power equipment. On the other hand, it is well known that the results of frequency domain spectroscopy are affected predominantly by temperature. Moreover, the voltage used for frequency domain spectroscopy is sinusoidal is nature. But the actual insulation system at site is stressed by voltage that may deviate significantly from sinusoid during operation. Considering these two aforesaid facts, in this paper, the response of oil-paper insulation has been studied at different temperatures using both sinusoidal as well as non-sinusoidal excitation. An experimental sample has been prepared in the laboratory that emulates the composite insulation of oil immersed type power transformer at site. The results of frequency response using sinusoidal voltage waveform at different temperature show good agreement with the conventional results that validates the scheme of the experimental setup. Some more information is obtained from the response of non-sinusoidal excitation that could help to get a better interpretation of the actual physical condition of oil-paper composite insulation. [ABSTRACT FROM AUTHOR]

The paper industry is a highly automated industry that includes many different production steps, in which a variety of machine components are used. In a paper machine, where the pulp is being transformed into paper, rotating components such as bearing-mounted rollers play an important part in driving the wire with the pulp through the process. In this type of industry with a serial layout, the failure of a single roller or bearing could lead to the stoppage of several production steps, with costly consequences as a result. To ensure and optimise asset availability, a condition-based maintenance (CBM) strategy could be implemented. However, CBM is dependent on an appropriate condition monitoring (CM) technique to detect a physical phenomenon that defines the state of critical components or systems. For the development of CM techniques, it is therefore important to understand and model the physical behaviour of the system in question. In this paper, the behaviour of a roller in a paper machine is analysed using the finite element method (FEM). The physical model was compared with vibration measurements collected from an online monitoring system and an experimental modal analysis. [ABSTRACT FROM AUTHOR]

This paper describes the need for wireless piston telemetry together with elements of the design, development and operation of such a system. A specific feature of the system is the multiplicity of operating modes enabled by two-way communication. The system has been demonstrated to work with thermocouples and accelerometers embedded in the piston of a very small engine at speeds of over 2000 rev min-l. The piston-mounted components can be fitted to a piston as small as 80 mm diameter, and size reductions are anticipated with improvements in implementation technologies. Typical results are quoted in the paper. Patents for this system are pending. Copyright © 2000 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

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In recent years, the way that maintenance is carried out has evolved due to the incorporation of digital tools and Industry 4.0 concepts. By connecting to and communicating with their production system, companies can now gather information about the current and future health of the equipment, enabling more efficient control through a process called predictive maintenance (PdM). The goal of PdM is to reduce unplanned downtimes and proactively address maintenance needs before failures occur. However, it can be challenging for industrial practitioners to implement an intelligent maintenance system that effectively manages data. This paper presents a methodology for developing and implementing a PdM system in the automotive industry, using open standards and scalable data management capabilities. The platform is validated through the presentation of two industry use cases. [ABSTRACT FROM AUTHOR]

This paper presents the design of a wideband Hilbert fractal antenna for the purpose of detecting and classifying different common partial discharge (PD) types in an oil-paper insulated system. Three common types of PDs are considered for the multi-class classification problem, namely, PD from a sharp point to ground plane, surface discharge, and PD from a void in the insulation. The different PD types showed variation in the detected frequency contents. The collected samples were processed using pattern recognition techniques to identify their corresponding PD types. A recognition rate of 95% was achieved when K-nearest neighbors was used as the classifier. [ABSTRACT FROM PUBLISHER]

The adoption of prognostics for critical assets has the potential to advance asset management in the power industry significantly. Whereas diagnostic techniques can identify the presence of incipient faults, prognostics aims to predict the future state of a given asset [1], [2]. Prognostics can therefore be used to estimate the remaining useful life (RUL) of the asset, and help plan maintenance while minimizing the risk of failure in service. Prognostics requires a good model of the process of deterioration, from inception through to failure [1]?[3]. Deterioration may be due to aging, as in the case of paper insulation, or it may be due to a fault. Regardless of the cause of deterioration, prognostics is useful only if the deterioration is slow enough that maintenance (repair or replacement) can be scheduled during the predicted RUL. Thus, prognostics is not superior to diagnostics if the deterioration is so rapid that failure cannot be prevented. [ABSTRACT FROM PUBLISHER]