Using neural networks for the diagnosis of localized defects in ball bearings

Subrahmanyam, M. V. V.; Sujatha, C.

doi:10.1016/s0301-679x(97)00056-x

Cited by 78 publications

(33 citation statements)

References 12 publications

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“…Then provides the optimized weights and thresholds for the BP network and train them again using BP algorithm to get the optimal nonlinear mapping relationship between fault types and fault symptoms. Finally, input a group of current fault feature vector into the BP neural network and output the corresponding fault types to realize failure recognition of mechanical equipment [8]. In this paper, the rolling bearing was seen as research object and through the fault diagnosis based on GA-BP algorithm, the rolling bearing fault types were recognized automatically.…”

Section: The Rolling Bearing Fault Diagnosis Based On Ga-bp Algorithmmentioning

confidence: 99%

Study of Fault Diagnosis for Rolling Bearing Based on GA-BP Algorithm

Wen¹,

Jia²,

Luo³

2017

Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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Abstract. With the development of modern technology, the diagnosis technology for mechanical equipment has attached more attentions. This paper mainly presents the fault diagnosing process for rolling bearing by using the hybrid GA-BP algorithm, which combines genetic algorithm (GA) with error back-propagation algorithm (BP). The weights and thresholds of BP neural network are optimized by using genetic algorithm firstly and then assigned to the BP neural network. With the using of proposed approach, the diagnosis performance of BP neural network can be improved. In this paper, the optimal nonlinear mapping relationship between fault types and fault symptoms of rolling bearings is obtained and then realize failure recognition of rolling bearings though the hybrid GA-BP algorithm. Finally, the results of the test experiment verifies the efficiency and accuracy of the proposed GA-BP algorithm.

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Section: The Rolling Bearing Fault Diagnosis Based On Ga-bp Algorithmmentioning

confidence: 99%

Study of Fault Diagnosis for Rolling Bearing Based on GA-BP Algorithm

Wen¹,

Jia²,

Luo³

2017

Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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“…M. Subrahmanyam and C. Sujatha [7] for example, used different kinds of statistical features of acoustic emission signals for feeding the neural network.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Artificial Neural Networks (ANN) have shown an impressive learning and memory capability. They have been widely used for automatic detection of faults in different ways [7] and in other pattern recognition problems [8]. ANN have the ability to reproduce arbitrary nonlinear functions and are very suitable for complex pattern recognition tasks [9].…”

Section: Introductionmentioning

confidence: 99%

Sound Based Machine Fault Diagnosis System Using Pattern Recognition Techniques

Vununu¹,

Moon²,

Lee³

et al. 2017

Journal of Korea Multimedia Society

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Machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines. Generally, it is very difficult to diagnose a machine fault by conventional methods based on mathematical models because of the complexity of the real world systems and the obvious existence of nonlinear factors. This study develops an automatic machine fault diagnosis system that uses pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The sounds emitted by the operating machine, a drill in this case, are obtained and analyzed for the different operating conditions. The specific machine conditions considered in this research are the undamaged drill and the defected drill with wear. Principal component analysis is first used to reduce the dimensionality of the original sound data. The first principal components are then used as the inputs of a neural network based classifier to separate normal and defected drill sound data. The results show that the proposed PCA-ANN method can be used for the sounds based automated diagnosis system.

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“…Wavelet analysis has also been successfully applied to bearing defect detection [17][18][19][20][21]. In recent years, artificial neural networks and fuzzy logic have also emerged as popular tools for automated fault diagnosis [18,[22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring of Low Speed Bearings: A Comparative Study of the Ultrasound Technique Versus Vibration Measurements

Kim

Tan

Mathew

et al.

Engineering Asset Management

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Bearing failure is often attributed to be one of the major causes of breakdown in industrial rotating machines that operate at high and low speeds. This paper presents results of a comparative experimental study on the application of the ultrasound technique for condition monitoring of low speed rolling element bearings and conventional vibration measurements with seeded faults on inner-race defects. The effectiveness of the ultrasound technique is demonstrated through signal processing techniques; use of statistical parameters derived from the time domain, and enveloped spectra in the frequency domain. The results reveal that the ultrasound technique is more effective in detecting low speed bearings failure than that of the vibration measurement.

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Using neural networks for the diagnosis of localized defects in ball bearings

Cited by 78 publications

References 12 publications

Study of Fault Diagnosis for Rolling Bearing Based on GA-BP Algorithm

Study of Fault Diagnosis for Rolling Bearing Based on GA-BP Algorithm

Sound Based Machine Fault Diagnosis System Using Pattern Recognition Techniques

Condition Monitoring of Low Speed Bearings: A Comparative Study of the Ultrasound Technique Versus Vibration Measurements

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