The Experts below are selected from a list of 341949 Experts worldwide ranked by ideXlab platform
Tan Tien Nguyen - One of the best experts on this subject based on the ideXlab platform.
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Machine Performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector Machine
Mechanical Systems and Signal Processing, 2012Co-Authors: Van Tung Tran, Hong Thom Pham, Bosuk Yang, Tan Tien NguyenAbstract:Machine Performance degradation assessment and remaining useful life (RUL) prediction are of crucial importance in condition-based maintenance to reduce the maintenance cost and improve the reliability. They provide a potent tool for operators in decision-making by specifying the present Machine state and estimating the remaining time. For this ultimate purpose, a three-stage method for assessing the Machine health degradation and forecasting the RUL is proposed. In the first stage, only the normal operating condition of Machine is used to create identification model for recognizing the dynamic system behavior. Degradation index which is used for indicating the Machine degradation is subsequently created based on the root mean square of residual errors. These errors are the difference between identification model and behavior of system. In the second stage, the Cox’s proportional hazard model is generated to estimate the survival function of the system. In the last stage, support vector Machine, which is one of the remarkable Machine learning techniques, in association with time-series techniques is utilized to forecast the RUL. The data of low methane compressor acquired from condition monitoring routine is used for validating the proposed method. The result shows that the proposed method could be used as a reliable tool to Machine prognostics.
G. Wijk - One of the best experts on this subject based on the ideXlab platform.
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A model of tunnel boring Machine Performance
Geotechnical & Geological Engineering, 1992Co-Authors: G. WijkAbstract:A mathematical model for the Performance of a tunnel boring Machine (TBM) is presented. In particular an equation for the TBM cutter wear is proposed. The rock mechanics are described via simple strength parameters and the Cerchar abrasivity index (CAI). Thus one obtains a complete description of TBM tunnelling with instantaneous and accumulated values for the tunnelling rate, the tool consumption, the costs etc. as output parameters when the tunnelling proceeds through rock with variable properties (strength and CAI). Two different types of cutter tools are considered, namely wedge-shaped tools and constant wear flat tools. For wedge-shaped tools the tunnelling costs are shown to be minimized if old tools are replaced by new tools when a certain wear flat tool has been reached. Similarly there is an optimum design (=wear flat) for the constant wear flat tools.
Serdar Iplikci - One of the best experts on this subject based on the ideXlab platform.
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application of two non linear prediction tools to the estimation of tunnel boring Machine Performance
Engineering Applications of Artificial Intelligence, 2009Co-Authors: Saffet Yagiz, Candan Gokceoglu, Ebru Akcapinar Sezer, Serdar IplikciAbstract:Predicting tunnel boring Machine (TBM) Performance is a crucial issue for the accomplishment of a mechanical tunnel project, excavating via full face tunneling Machine. Many models and equations have previously been introduced to estimate TBM Performance based on properties of both rock and Machine employing various statistical analysis techniques. However, considering the nature of the problem, it is relatively difficult to estimate tunnel boring Machine Performance by linear prediction models. Artificial neural networks (ANNs) and non-linear multiple regression models have great potential for establishing such prediction models. The purpose of the present study is the construction of non-linear multivariable prediction models to estimate TBM Performance as a function of rock properties. For this purpose, rock properties and Machine data were collected from recently completed TBM tunnel project in the City of New York, USA and consequently the database was established to develop Performance prediction models utilizing the ANN and the non-linear multiple regression methods. This paper presents the results of study into the application of the non-linear prediction approaches providing the acceptable precise Performance estimations.
Jonny Holmstrom - One of the best experts on this subject based on the ideXlab platform.
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Designing Ubiquitous Information Environments - Ubiquitous Computing and the Double Immutability of Remote Diagnostics Technology: An Exploration into Six Cases of Remote Diagnostics Technology Use
IFIP — The International Federation for Information Processing, 2009Co-Authors: Katrin Jonsson, Jonny HolmstromAbstract:The aim of this paper is to display the use a specific type of ubiquitous computing technology—remote diagnostics technology—in organizations and, in particular, the way in which the technology is enacted in remote and local maintenance groups. By taking a case study approach, we look into the use of remote diagnostics technology in the maintenance industry. Drawing from actor—network theory, and in particular the notion of double immutability, we argue that we need to establish a stable relationship that uses remote diagnostics technology for monitoring Machine Performance from a remote place while also keeping a level of local responsiveness toward Machine Performance. The stability of the remote diagnostics technology is seemingly effective in that critical data can be collected, diffused, and manipulated. The stability of the network of relations surrounding the technology is, however, yet to emerge. The borders between the central group and the local maintenance workers must be considered and we need to acknowledge that it takes effort to sustain stable networks of relations. We need to establish a new relationship that uses ubiquitous computing technology for monitoring processes and activities from the remote group while also keeping a level of local responsiveness toward Machine Performance. Taken together, the remote and the local group, along with the remote diagnostics technology, constitute a maintenance work collective.
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ubiquitous computing and the double immutability of remote diagnostics technology an exploration into six cases of remote diagnostics technology use
Designing Ubiquitous Information Environments, 2005Co-Authors: Katrin Jonsson, Jonny HolmstromAbstract:The aim of this paper is to display the use a specific type of ubiquitous computing technology—remote diagnostics technology—in organizations and, in particular, the way in which the technology is enacted in remote and local maintenance groups. By taking a case study approach, we look into the use of remote diagnostics technology in the maintenance industry. Drawing from actor—network theory, and in particular the notion of double immutability, we argue that we need to establish a stable relationship that uses remote diagnostics technology for monitoring Machine Performance from a remote place while also keeping a level of local responsiveness toward Machine Performance. The stability of the remote diagnostics technology is seemingly effective in that critical data can be collected, diffused, and manipulated. The stability of the network of relations surrounding the technology is, however, yet to emerge. The borders between the central group and the local maintenance workers must be considered and we need to acknowledge that it takes effort to sustain stable networks of relations. We need to establish a new relationship that uses ubiquitous computing technology for monitoring processes and activities from the remote group while also keeping a level of local responsiveness toward Machine Performance. Taken together, the remote and the local group, along with the remote diagnostics technology, constitute a maintenance work collective.
J P Sharma - One of the best experts on this subject based on the ideXlab platform.
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Exploiting phase fluctuations to improve Machine Performance monitoring
IEEE Transactions on Automation Science and Engineering, 2007Co-Authors: Srikumar Venugopal, Roy Anthony Wagstaff, J P SharmaAbstract:Machines are an integral and important part of modern life. Machine condition monitoring is of vital importance to modern industry in its quest for higher reliability, quality, and efficiency. A new signal-processing technique for Machine Performance monitoring is presented. This new technique exploits fluctuations in phase angles of Machine rotational frequency signals to determine their dynamic temporal coherence. Temporal coherence is the key to automatically identify a fault condition and assess its severity. The exploitation of temporal coherence also provides increased spectral resolution and signal-to-noise ratio (SNR). Tests were conducted on an edger trimmer ball bearing assembly that was subjected to different levels of fault conditions, such as a hairline crack on the outer race and sand contamination in the bearing. An electric fan motor with bearing faults was also tested. The fault identification capabilities of incoherent power averaging are compared with those of the new coherence processors. Some rotational signals could not be identified in the average power spectra and, therefore, the average power could not be used for monitoring these signals. However, they were easily identified in the spectra of the new processors, with their increased SNR gain, and were used successfully for Machine Performance monitoring and diagnostics. This new processing capability for quantifying and exploiting the dynamic temporal coherences of a Machine's signals provides a valuable capability for detecting existing and developing faults, and for monitoring their progress. This is also true during the startup and shutdown phases of Machines, when their speeds and corresponding rotational frequencies are changing. Note to Practitioners - General guidance for monitoring a Machine's Performance is included below. Calculate the coherence parameter phi and establish the base-line temporal coherences of a Machine operating in good condition for future reference, or for a s-\n-\nimilar Machine that is known to be in good operating condition. Continuously monitor the Machine's rotational signals via accelerometer or microphone, measuring the temporal coherences using the FFT-derived coherence parameter phi. Calculate/display the temporal coherence time histories of phi for the rotational signals. Analyze the phi coherence patterns. A constant thickness, continuously nonrandom, phi temporal coherence time history pattern, indicates fault-free Machine operation. A decreasing spread in the phi time history pattern with time indicates increasing temporal coherence of a rotational frequency component due to a mechanical fault. The frequency indicates which Machine component is faulty. Possible problem: a fault associated with a bearing (e.g., a crack in a bearing race). A broadening phi time history pattern of a rotational frequency component indicates decreasing temporal coherence. Possible problems: need for maintenance, worn out lubrication, or foreign matter (dust, grit, sand) in the bearing
