On-line condition monitoring of railway equipment using neural networks

Fararooy, S.; Allan, J.

doi:10.1049/ic:19950983

Cited by 7 publications

(6 citation statements)

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“…For example, in power generators, the integrity of insulating materials is critical, and thus identifying measurable quantities that capture insulator degradation, designing and integrating sensing capability, and using monitoring information to diagnose insulation health are active areas of research (Feser et al, 1995). Martin (1994), Fararooy and Allan (1995), Dimla (1999), and Thorsen and Dalva (1999), provide surveys of similar research in other application areas, including cutting tools, high-voltage induction motors, railway equipment, and machine tools.…”

Section: Literature Reviewmentioning

confidence: 99%

Residual-life distributions from component degradation signals: A Bayesian approach

et al. 2005

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Real-time condition monitoring is becoming an important tool in maintenance decision-making. Condition monitoring is the process of collecting real-time sensor information from a functioning device in order to reason about the health of the device. To make effective use of condition information, it is useful to characterize a device degradation signal, a quantity computed from condition information that captures the current state of the device and provides information on how that condition is likely to evolve in the future. If properly modeled, the degradation signal can be used to compute a residual-life distribution for the device being monitored, which can then be used in decision models. In this work, we develop Bayesian updating methods that use real-time condition monitoring information to update the stochastic parameters of exponential degradation models. We use these degradation models to develop a closed-form residual-life distribution for the monitored device. Finally, we apply these degradation and residual-life models to degradation signals obtained through the accelerated testing of bearings.

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Section: Literature Reviewmentioning

confidence: 99%

Residual-life distributions from component degradation signals: A Bayesian approach

et al. 2005

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“…Significant research exists in areas such as power transformers (Feser et al, 1995), cutting tools (Dimla, 1999), high-voltage induction motors (Thorsen and Dalva, 1999), railway equipment (Fararooy and Allan, 1995) and machine tools (Martin, 1994). Most of this work is diagnostic in nature.…”

Section: Literature Reviewmentioning

confidence: 99%

Residual-life estimation for components with non-symmetric priors

et al. 2009

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Condition monitoring uses sensory signals to assess the health of engineering systems. A degradation model is a mathematical characterization of the evolution of a condition signal. Our recent research focuses on using degradation models to compute residuallife distributions for degrading components. Residual-life distributions are important for providing probabilistic estimates of failure time for use in maintenance planning and spare parts inventory management. To obtain residual-life distributions, our earlier work assumed the degradation model's stochastic parameters to be normally distributed. This paper investigates the performance of these residual-life distributions when the underlying normality assumptions are not satisfied. The paper also develops methods for estimating residual-life when the stochastic parameters of the degradation model follow more general distributions.

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“…Table III gives the input vector that can be taken as training samples (nos. [1][2][3][4][5] and an unknown input vector(no. 6).…”

Section: B Samples Of Medium-voltage Vacuum Cb's Mechanical Conditiomentioning

confidence: 99%

“…I T is known that for the routine time-based maintenance of circuit breakers (CBs), in between maintenance outages, it is never clear if the CB functions exactly as desired, so this kind of maintenance is expensive, inefficient, and ineffective [1], [2]. However, with increasing awareness of the benefits of condition-based maintenance, it can be expected that there will be advantages to be obtained from monitoring the condition of the CB in order to ensure that maintenance is carried out only when it is really needed [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Mechanical Condition Recognition of Medium-Voltage Vacuum Circuit Breaker Based on Mechanism Dynamic Features Simulation and ANN

Rong

Wang

et al. 2005

IEEE Trans. Power Delivery

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A new research method is proposed for the medium-voltage (MV) vacuum circuit breaker's (CB's) mechanical condition monitoring, which combines the mechanism dynamic features simulation and mechanical condition recognition algorithm based on artificial neural networks (ANNs). This method includes three steps: First, the relations between eigenvalues and mechanical failures of a vacuum circuit breaker (CB) through simulation instead of measurement are obtained. In this paper, the mechanism dynamic features of a vacuum CB in failure are simulated; the simulation results indicate that the parameter that can be monitored-main angle-has different characters for different mechanism failures. Second, the eigenvalues for different failure conditions are described by three parameters. Third, mechanical condition recognition of the MV vacuum CB by an algorithm based on ANN is realized. It is concluded by the work mentioned above, both the known mechanical condition type and the new mechanical condition type of the medium-voltage vacuum CB can be recognized with predetermined reliability.

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On-line condition monitoring of railway equipment using neural networks

Cited by 7 publications

References 0 publications

Residual-life distributions from component degradation signals: A Bayesian approach

Residual-life distributions from component degradation signals: A Bayesian approach

Residual-life estimation for components with non-symmetric priors

Mechanical Condition Recognition of Medium-Voltage Vacuum Circuit Breaker Based on Mechanism Dynamic Features Simulation and ANN

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