Open AccessJournal Article
An empirical application of linear regression method and fir network for fault diagnosis in nonlinear time series
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TL;DR: A fault diagnosis scheme for nonlinear time series recorded in normal and abnormal conditions is described, applied to two fault diagnosis problems using acoustic and vibration data obtained from rotating parts of an automobile and a boring tool, respectively.
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Abstract: A fault diagnosis scheme for nonlinear time series recorded in normal and abnormal conditions is described. The fault is first detected from regression lines of the raw time series. Model for the normal condition time series is estimated using a Finite Impulse Response (FIR) neural network. The trained network is then used for filtering of abnormal condition time series. The fault is further confirmed/ analyzed using the regression lines of the predicted normal and inverse-filtered abnormal conditions time series. The described scheme is applied to two fault diagnosis problems using acoustic and vibration data obtained from rotating parts of an automobile and a boring tool, respectively
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Citations
An Empirical Study on Fault Diagnosis for Nonlinear Time Series using Linear Regression Method and FIR Network
TL;DR: A fault diagnosis scheme for nonlinear time series is employed for parts and tool breakage diagnosis problems and Finite Impulse Response network is used to estimate the unknown system for the normal condition data and to filter the abnormal condition data.
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Machine learning prediction for covid 19 pandemic in india
TL;DR: The result of the prediction shows that COVID-19 ailment could be conveyed through water and air ecological variables and so preventives measures such as social distancing, wearing of mask and hand gloves, staying at home can help to avert the circulation of the sickness thereby resulting in reduced active cases and even mortality.
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Learning internal representations by error propagation
David E. Rumelhart,Geoffrey E. Hinton,Ronald J. Williams +2 more
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TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
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Learning internal representations by error propagation
David E. Rumelhart,Geoffrey E. Hinton,Ronald J. Williams +2 more
- 03 Jan 1986
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
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Phoneme recognition using time-delay neural networks
Alex Waibel,Toshiyuki Hanazawa,Geoffrey E. Hinton,Kiyohiro Shikano,Kevin J. Lang +4 more
- 01 Jan 1995
TL;DR: The authors present a time-delay neural network (TDNN) approach to phoneme recognition which is characterized by two important properties: using a three-layer arrangement of simple computing units, a hierarchy can be constructed that allows for the formation of arbitrary nonlinear decision surfaces, which the TDNN learns automatically using error backpropagation.
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Phoneme recognition using time-delay neural networks
TL;DR: In this article, the authors presented a time-delay neural network (TDNN) approach to phoneme recognition, which is characterized by two important properties: (1) using a three-layer arrangement of simple computing units, a hierarchy can be constructed that allows for the formation of arbitrary nonlinear decision surfaces, which the TDNN learns automatically using error backpropagation; and (2) the time delay arrangement enables the network to discover acoustic-phonetic features and the temporal relationships between them independently of position in time and therefore not blurred by temporal shifts in the input