Artificial Neural Network Model for Monitoring Oil Film Regime in Spur Gear Based on Acoustic Emission Data

Ali, Yasir Hassan; Rahman, Roslan Abd.; Hamzah, Rohana

doi:10.1155/2015/106945

Cited by 10 publications

(10 citation statements)

References 25 publications

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“…The back-propagation process contains two stages which are feed-forward and backpropagation. The initial stage is a feed-forward stage where the network is assigned randomly that will result in an error value [25]. Then the error value will be taken to adjust the weights in the back-propagation stage.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

An intelligent bearing fault diagnosis system: A review

et al. 2019

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Rolling element bearing (REB) is a well-known component that most extensively used in the industry. They operate in extreme condition (high temperature, dirty environment) which may lead to unexpected failure after the certain operation. Faulty on bearing cause severe equipment damage, financial loss and threaten people's life. Development of proper fault diagnosis system of REB capable of preventing unexpected failure from occurs and maintain the machine work in the healthy state. Over a few decades, machine learning is introduced to provide a consistent fault diagnosis result. Hence, this paper reviewed the development of bearing diagnosis method using machine learning models.

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Section: Artificial Neural Networkmentioning

confidence: 99%

An intelligent bearing fault diagnosis system: A review

et al. 2019

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“…In recent years neural computing become a common used method in many fields because of their ability to measure nonlinear relationships in complex processes. Therefore, ANN have been employ in machine faults prediction and classification problems [27,28] ANN structure is an interlinked assembly of individual processing elements called nodes. Each node receives inputs from neighbouring nodes, processes the information with selected transfer function and produces an output to be transmitted to the next node.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Automated Valve Fault Detection Based on Acoustic Emission Parameters and Artificial Neural Network

et al. 2019

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Reciprocating compressor is one of the most popular classes of machines use with wide applications in the industry. However, valve failures in this machine often results unplanned shutdown. Therefore, the effective valve fault detection technique is very necessary to ensure safe operation and to reduce the unplanned shutdown. This paper propose an artificial intelligence (AI) model to detect valve condition in reciprocating compressor based on acoustic emission (AE) parameters measurement and artificial neural network (ANN). A set of experiments were conducted on an industrial reciprocating air compressor with several operational conditions including good valve and faulty valve to acquire AE signal. A fault detection model was then developed from the combination of healthy-faulty data using ANN tool box available in MATLAB. The results of the model validation demonstrated accuracy of valves condition classification exceeding 97%. Eventually, the authors intend to do more efforts for programming this model in smart portable device which can be one of the innovative engineering technologies in the field of machinery condition monitoring in the near future.

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“…21 BP neural network is composed of input, hidden and output layers, and there are several neural nodes in each layer. The neural nodes in every layer connect with the nodes in adjacent layer by the weights and thresholds.…”

Section: ∂ ∂Umentioning

confidence: 99%

“…Due to the strong learning ability and simple structure, artificial neural networks are widely used in many fields, such as forecasting and nonlinear multivariate data analysis. 21 BP neural network is composed of input, hidden and output layers, and there are several neural nodes in each layer. The neural nodes in every layer connect with the nodes in adjacent layer by the weights and thresholds.…”

Section: Neural Network Model For Time-varying Oil Film Stiffness and Dampingmentioning

confidence: 99%

Theoretical and experimental studies on dynamics of double-helical gear system supported by journal bearings

Yin

Chen

2016

Advances in Mechanical Engineering

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The dynamic behaviour of a double-helical gear system supported by journal bearings is theoretically and experimentally investigated in this study. A bending-torsional-axial coupling model for dynamic analysis of double-helical gear system is developed. Influence of the time-varying mesh stiffness and damping is considered. Oil film stiffness and damping of the supporting journal bearing are supposed to be time-varying, and the time-varying oil film stiffness and damping are predicted by a back propagation neural network, which is optimized by genetic algorithm. A double-helical gear-rotor-journal bearing system test rig is also established to carry out the experimental investigations, such as the dynamic transmission errors of gear pairs. The comparisons between theoretical and experimental results show that the timevarying oil film dynamic coefficients of journal bearings are an important internal excitation. The theoretical model with time-varying oil film stiffness and damping can predict the gear dynamics more accurate than the model with time-invariant oil film stiffness and damping, and the neural network optimized by genetic algorithm can obtain the time-varying oil film stiffness and damping efficiently and accurately for the dynamic analysis of double-helical gear system.

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Artificial Neural Network Model for Monitoring Oil Film Regime in Spur Gear Based on Acoustic Emission Data

Cited by 10 publications

References 25 publications

An intelligent bearing fault diagnosis system: A review

An intelligent bearing fault diagnosis system: A review

Automated Valve Fault Detection Based on Acoustic Emission Parameters and Artificial Neural Network

Theoretical and experimental studies on dynamics of double-helical gear system supported by journal bearings

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