1. Chemical analysis of thermally aged cables in nuclear power plantsMarko Pirc, Jurij Avsec, Dijana Vrsaljko, 2025, izvirni znanstveni članek Opis: The paper presents findings on the implementation of various mechanical and chemical diagnostic procedures aimed at enhancing the monitoring of cable insulation conditions in Krško Nuclear Power Plant (NEK). This article introduces the advancement of four novel diagnostic testing methodologies for evaluating the mechanical and chemical properties of cable insulation: Indenter Modulus (IM), Differential Scanning Calorimetry (DSC), Fourier Transform Infrared Spectroscopy (FTIR), Thermogravimetric analysis (TGA) and X-Ray Fluorescence Spectral Analysis (XRF). Experiments were performed on diverse samples of widely used nuclear-qualified cable polymer materials, including Ethylene Propylene Rubber (EPR) and Crosslinked Polyethylene (XLPE), all with a Chlorosulphonated Polyethylene (CSPE) jacket. Samples from various vintages were subjected to additional temperature ageing in many stages, to establish field testing acceptability requirements and assess the remaining lifespan of the polymer insulation. Diagnostic tests were performed and some preliminary results are reported.
Ključne besede: cable, ageing, nuclear, diagnostic testing criteria, chemical condition monitoring
Objavljeno v DKUM: 16.06.2025; Ogledov: 0; Prenosov: 0
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2. A cloud-based system for the optical monitoring of tool conditions during milling through the detection of chip surface size and identification of cutting force trendsUroš Župerl, Krzysztof Stępień, Goran Munđar, Miha Kovačič, 2022, izvirni znanstveni članek Opis: This article presents a cloud-based system for the on-line monitoring of tool conditions in
end milling. The novelty of this research is the developed system that connects the IoT (Internet of
Things) platform for the monitoring of tool conditions in the cloud to the machine tool and optical
system for the detection of cutting chip size. The optical system takes care of the acquisition and
transfer of signals regarding chip size to the IoT application, where they are used as an indicator
for the determination of tool conditions. In addition, the novelty of the presented approach is in
the artificial intelligence integrated into the platform, which monitors a tool’s condition through
identification of the current cutting force trend and protects the tool against excessive loading by
correcting process parameters. The practical significance of the research is that it is a new system for
fast tool condition monitoring, which ensures savings, reduces investment costs due to the use of
a more cost-effective sensor, improves machining efficiency and allows remote process monitoring
on mobile devices. A machining test was performed to verify the feasibility of the monitoring
system. The results show that the developed system with an ANN (artificial neural network) for the
recognition of cutting force patterns successfully detects tool damage and stops the process within
35 ms. This article reports a classification accuracy of 85.3% using an ANN with no error in the
identification of tool breakage, which verifies the effectiveness and practicality of the approach.
Ključne besede: machining, end milling, tool condition monitoring, chip size detection, cutting force trend identification, visual sensor monitoring, cloud manufacturing technologies
Objavljeno v DKUM: 26.03.2025; Ogledov: 0; Prenosov: 3
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3. Tool condition monitoring using machine tool spindle current and long short-term memory neural network model analysisNiko Turšič, Simon Klančnik, 2024, izvirni znanstveni članek Opis: In cutting processes, tool condition affects the quality of the manufactured parts. As such, an essential component to prevent unplanned downtime and to assure machining quality is having information about the state of the cutting tool. The primary function of it is to alert the operator that the tool has reached or is reaching a level of wear beyond which behaviour is unreliable. In this paper, the tool condition is being monitored by analysing the electric current on the main spindle via an artificial intelligence model utilising an LSTM neural network. In the current study, the tool is monitored while working on a cylindrical raw piece made of AA6013 aluminium alloy with a custom polycrystalline diamond tool for the purposes of monitoring the wear of these tools. Spindle current characteristics were obtained using external measuring equipment to not influence the operation of the machine included in a larger production line. As a novel approach, an artificial intelligence model based on an LSTM neural network is utilised for the analysis of the spindle current obtained during a manufacturing cycle and assessing the tool wear range in real time. The neural network was designed and trained to notice significant characteristics of the captured current signal. The conducted research serves as a proof of concept for the use of an LSTM neural network-based model as a method of monitoring the condition of cutting tools.
Ključne besede: tool condition monitoring, artificial intelligence, LSTM neural network
Objavljeno v DKUM: 22.04.2024; Ogledov: 181; Prenosov: 38
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4. International Conference Fluid Power 2019 : Conference Proceedings2019 Opis: The International Fluid Power Conference is a two day event, intended for all those professionally-involved with hydraulic or pneumatic power devices and for all those, wishing to be informed about the ‘state of the art’, new discoveries and innovations within the field of hydraulics and pneumatics. The gathering of experts at this conference in Maribor has been a tradition since 1995, and is organised by the Faculty of Mechanical Engineering at the University of Maribor, in Slovenia. Fluid Power conferences are organised every second year and cover those principal technical events within the field of fluid power technologies in Slovenia, and throughout this region of Europe. This year's conference is taking place on the 19th and 20th September in Maribor.
Ključne besede: fluid power technology, components and systems, control systems, fluids, maintenance and condition monitoring
Objavljeno v DKUM: 24.02.2020; Ogledov: 1383; Prenosov: 48
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5. On-line condition monitoring and evaluation of remaining useful lifetimes for mineral hydraulic and turbine oilsVito Tič, Darko Lovrec, 2017, znanstvena monografija Opis: Condition monitoring of hydraulic and turbine oils, and especially their remaining useful life, is becoming an important strategic business advantage for plant owners, which benefits in environment protection and cost reduction.
This monograph discusses the problem of condition monitoring of hydraulic fluids throughout their life-cycles. Particular emphasis is placed on assessing mineral-based oils’ conditions and their remaining useful lifetimes quantitatively. Practicality and usefulness are vital when developing and deploying various methods for condition monitoring systems within industrial environments. Therefore, it is important to know the oil degradation mechanisms and influencing factors, commonly used laboratory methods and oil ageing tests, as well as, for on-line condition monitoring system design, the available sensors with all their characteristics and restrictions.
The proposed approach is based on a novel method for testing the durability and oxidation stability of different hydraulic and turbine oils. The developed mathematical model is based on data from previously conducted oil-ageing tests for the assessment of an oil's condition and its remaining useful lifetime. The method can also be used for comparison of different oils and selection of a more adequate oil with high oxidation stability and long service-lifetime.
Ključne besede: Hydraulic and turbine oils, degradation mechanisms, on-line condition monitoring, test methods, remaining useful lifetime
Objavljeno v DKUM: 22.12.2017; Ogledov: 1777; Prenosov: 439
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6. Real-time cutting tool condition monitoring in millingFranc Čuš, Uroš Župerl, 2011, izvirni znanstveni članek Opis: Reliable tool wear monitoring system is one of the important aspects for achieving a self-adjusting manufacturing system. The original contribution of the research is the developed monitoring system that can detect tool breakage in real time by using a combination of neural decision system and ANFIS tool wear estimator. The principal presumption was that force signals contain the most useful information for determining the tool condition. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. ANFIS method seeks to provide a linguistic model for the estimation of tool wear from the knowledge embedded in the artificial neural network. The ANFIS method uses the relationship between flank wear and the resultant cutting force to estimate tool wear. A series of experiments were conducted to determine the relationship between flank wear and cutting force as well as cutting parameters. Speed, feed, depth of cutting, time and cuttingforces were used as input parameters and flank wear width and tool state were output parameters. The forces were measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge was monitored by using a tool maker's microscope. The experimental force and wear data were utilized to train the developed simulation environment based on ANFIS modelling. The artificial neural network, was also used to discriminate different malfunction states from measured signals. By developed tool monitoring system (TCM) the machining process can be on-line monitored and stopped for tool change based on a pre-set tool-wear limit. The fundamental limitation of research was to develop a single sensor monitoring system, reliable as commercially available system, but 80% cheaper than multisensor approach.
Ključne besede: end-milling, tool condition monitoring, wear estimation, ANFIS
Objavljeno v DKUM: 10.07.2015; Ogledov: 1945; Prenosov: 127
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7. Fault detection of an industrial heat-exchanger : a model-based approachDejan Dragan, 2011, izvirni znanstveni članek Opis: One of the key issues in modelling for fault detection is how to accommodate the level of detail of the model description in order to suit the diagnostic requirements. The paper addresses a two-stage modelling concept to an industrial heat exchanger, which is located in a tyre factory. Modelling relies on combination of prior knowledge and recorded data. During the identification procedure, the estimates of continuous model parameters are calculated by the least squares method and the state variable filters (SVF). It is shown that the estimates are largely invariant of the bandwidth of the SVFs. This greatly reduces the overall modelling effort and makes the whole concept applicable even to less experienced users. The main issues of the modelling procedure are stressed. Based on the process model a simple detection system is derived. An excerpt of the results obtained on operating records is given.
Ključne besede: industrijski prenosniki toplote, zaznavanje napak, nadzor procesov, odkrivanje napak na osnovi modela, modeliranje, identifikacija, industrial heat exchanger, fault detection, condition monitoring, model-based detection, modelling, identification
Objavljeno v DKUM: 10.07.2015; Ogledov: 1878; Prenosov: 31
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8. Bluetooth platform for wireless measurements using industrial sensorsTadej Tašner, Kristian Les, Darko Lovrec, 2013, izvirni znanstveni članek Opis: The past decade has seen significant advancement in the field of mobile devices. Various smart devices such as cellular phones, tablets and PDAs have become universal tools in our everyday lives. Their versatility is based on their computing power, portability and their integration with other devices and services such as the World Wide Web. However, these smart devices have an even wider usability spectrum. They can also be used for wireless industrial measurements using existing sensors. The wireless connectivity of existing industrial sensors is achieved by equipping them with a Bluetooth module, which digitizes the data and passes it to any Bluetooth capable smart device for further processing, evaluation and logging. This paper describes the specially designed Bluetooth platform for wireless measurements all the way from the basic concept, through hardware, firmware and software implementation, to the sample tests and measurements.
Ključne besede: sensors, wireless, bluetooth, data acquisition, condition monitoring
Objavljeno v DKUM: 10.07.2015; Ogledov: 1284; Prenosov: 423
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9. Intelligent cutting tool condition monitoring in millingUroš Župerl, Franc Čuš, Jože Balič, 2011, izvirni znanstveni članek Opis: Purpose: of this paper is to present a tool condition monitoring (TCM) system that can detect tool breakage in real time by using a combination of neural decision system, ANFIS tool wear estimator and machining error compensation module. Design/methodology/approach: The principal presumption was that the force signals contain the most useful information for determining the tool condition. Therefore, ANFIS method is used to extract the features of tool states from cutting force signals. The trained ANFIS model of tool wear is then merged with a neural network for identifying tool wear condition (fresh, worn). Findings: The overall machining error is predicted with very high accuracy by using the deflection module and a large percentage of it is eliminated through the proposed error compensation process. Research limitations/implications: This study also briefly presents a compensation method in milling in order to take into account tool deflection during cutting condition optimization or tool-path generation. The results indicate that surface errors due to tool deflections can be reduced by 65-78%. Practical implications: The fundamental limitation of research was to develop a single-sensor monitoring system, reliable as commercially available system, but much cheaper than multi-sensor approach. Originality/value: A neural network is used in TCM as a decision making system to discriminate different malfunction states from measured signals.
Ključne besede: tool condition monitoring, TCM, wear, tool deflection, ANFIS, neural network, end-milling
Objavljeno v DKUM: 01.06.2012; Ogledov: 1716; Prenosov: 47
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10. Detecting and analysing condition of hydraulic oils with on-line sensorsVito Tič, Darko Lovrec, 2011, izvirni znanstveni članek Opis: On-line condition monitoring of the entire system or individual components can be used for detection of impending system break-down. The on-line monitoring of the state of hydraulic system and fluid plays a decisive role in general on-line condition monitoring. Friction, wear, leakage and excessive temperatures all have impact on lubricating properties of the oil. Apart from this, the oil its self is prone to aging and deterioration processes, which can also result in corrosion and equipment failures. The oil condition can be understood as a fingerprint of the condition of the complete system. Due to a widespread availability of robust and cost-effective on-line sensors for measuring various fluid properties, latest developments deal with on-line oil condition monitoring to determine the condition of hydraulic system and fluid. This allows for maintenance work to be carried out based on the detected system condition.
Ključne besede: oil ageing, condition monitoring, physical properties, chemical properties
Objavljeno v DKUM: 01.06.2012; Ogledov: 1659; Prenosov: 27
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