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1.
Detecting and analysing condition of hydraulic oils with on-line sensors
Vito Tič, Darko Lovrec, 2011, original scientific article

Abstract: 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.
Keywords: oil ageing, condition monitoring, physical properties, chemical properties
Published: 01.06.2012; Views: 713; Downloads: 13
URL Full text (0,00 KB)

2.
Intelligent cutting tool condition monitoring in milling
Uroš Župerl, Franc Čuš, Jože Balič, 2011, original scientific article

Abstract: 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.
Keywords: tool condition monitoring, TCM, wear, tool deflection, ANFIS, neural network, end-milling
Published: 01.06.2012; Views: 681; Downloads: 13
URL Full text (0,00 KB)

3.
Bluetooth platform for wireless measurements using industrial sensors
Kristian Les, Tadej Tašner, Darko Lovrec, 2013, original scientific article

Abstract: 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.
Keywords: sensors, wireless, bluetooth, data acquisition, condition monitoring
Published: 10.07.2015; Views: 196; Downloads: 12
.pdf Full text (1,02 MB)

4.
Fault detection of an industrial heat-exchanger
Dejan Dragan, 2011, original scientific article

Abstract: 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.
Keywords: 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
Published: 10.07.2015; Views: 468; Downloads: 5
URL Full text (0,00 KB)

5.
Real-time cutting tool condition monitoring in milling
Uroš Župerl, Franc Čuš, 2011, original scientific article

Abstract: 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.
Keywords: end-milling, tool condition monitoring, wear estimation, ANFIS
Published: 10.07.2015; Views: 506; Downloads: 10
URL Full text (0,00 KB)

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