Third-Order Spectral Techniques for the Diagnosis of Motor Bearing Condition Using Artificial Neural Networks

Yang, Da-Wei; Stronach, A.F.; MacConnell, P.F.; Penman, J.

doi:10.1006/mssp.2001.1469

Cited by 96 publications

(41 citation statements)

References 26 publications

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“…Among the most commonly deployed ICM techniques has been artificial neural networks (ANNs), which can be used for detecting both anomalous sensor readings and diagnosing these as specific faults. 7,8 Using only SCADA data, Zaher et al successfully applied ANNs to detect anomalous temperature readings within a turbine's gearbox and cooling oil, 9 while Kusiak and Li also applied ANNs for diagnosing the severity of a fault. 10 Yan demonstrated that the use of Random Forests (RF) 11 for classifying faults outperformed conventional decision tree classifiers and support vector machines, as well as producing comparable performance to ANNs.…”

Section: Technology Reviewmentioning

confidence: 99%

Intelligent integrated maintenance for wind power generation

et al. 2015

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A novel architecture and system for the provision of Reliability Centred Maintenance (RCM) for offshore wind power generation is presented. The architecture was developed by conducting a bottom-up analysis of the data required to support RCM within this specific industry, combined with a top-down analysis of the required maintenance functionality. The architecture and system consists of three integrated modules for intelligent condition monitoring, reliability and maintenance modelling, and maintenance scheduling that provide a scalable solution for performing dynamic, efficient and cost-effective preventative maintenance management within this extremely demanding renewable energy generation sector. The system demonstrates for the first time the integration of state-of-the-art advanced mathematical techniques: Random Forests, dynamic Bayesian networks and memetic algorithms in the development of an intelligent autonomous solution. The results from the application of the intelligent integrated system illustrated the automated detection of faults within a wind farm consisting of over 100 turbines, the modelling and updating of the turbines' survivability and creation of a hierarchy of maintenance actions, and the optimizing of the maintenance schedule with a view to maximizing the availability and revenue generation of the turbines.

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Section: Technology Reviewmentioning

confidence: 99%

Intelligent integrated maintenance for wind power generation

et al. 2015

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“…Feed forward neural network (FFNN) structure is widely used neural network structure in machine fault diagnosis [57][58][59][60]. Multilayer perceptron with the back propagation (BP) training algorithm, which is a special FFNN, is also employed for pattern recognition and classification as well as machine fault diagnostics [61][62][63]. However, the BP neural networks have two main limitations which are difficult to determine the network structure and the number of nodes; and slow convergence of the training process.…”

Section: Pattern Recognition-based Approachesmentioning

confidence: 99%

Machine Fault Diagnosis and Prognosis: The State of The Art

Tran¹,

Yang²

2009

International Journal of Fluid Machinery and Systems

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Machine fault diagnostic and prognostic techniques have been the considerable subjects of condition-based maintenance system in the recent time due to the potential advantages that could be gained from reducing downtime, decreasing maintenance costs, and increasing machine availability. For the past few years, research on machine fault diagnosis and prognosis has been developing rapidly. These publications covered in the wide range of statistical approaches to model-based approaches. With the aim of synthesizing and providing the information of these researches for researcher's community, this paper attempts to summarize and classify the recent published techniques in diagnosis and prognosis of rotating machinery. Furthermore, it also discusses the opportunities as well as the challenges for conducting advance research in the field of machine prognosis.

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“…They have been applied among others for applications in nuclear power plants [42], power distribution [54], for different industrial applications of motor bearings [37], [60], [33] and electric machines [50]. The existing research in the field of railway systems focuses among others on condition monitoring, fault detection and fault diagnostics [46], [16], [32], [19].…”

Section: Introductionmentioning

confidence: 99%

Predicting component reliability and level of degradation with complex-valued neural networks

Fink

Zio

Weidmann

2014

Reliability Engineering & System Safety

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. Predicting component reliability and level of degradation with complex-valued neural networks. Reliability Engineering and System Safety, Elsevier, 2014, 121, pp.198-206 AbstractIn this paper, multilayer feedforward neural networks based on multi-valued neurons (MLMVN), a specific type of complex valued neural networks, are proposed to be applied to reliability and degradation prediction problems, formulated as time series. MLMVN have demonstrated their ability to extract complex dynamic patterns from time series data for mid-and long-term predictions in several applications and benchmark studies. To the authors' knowledge, it is the first time that MLMVN are applied for reliability and degradation prediction. MLMVN is applied to a case study of predicting the level of degradation of railway track turnouts using real data. The performance of the algorithms is first evaluated using benchmark study data. The results obtained in the reliability prediction study of the benchmark data show that MLMVN outperforms other machine learning algorithms in terms of prediction precision and is also able to perform multi-step ahead predictions, as opposed to the previously best performing benchmark studies which only performed up to two-step ahead predictions. For the railway turnout application, MLMVN confirm the good performance in the long-term prediction of degradation and does not show accumulating errors for multi-step ahead predictions.

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Third-Order Spectral Techniques for the Diagnosis of Motor Bearing Condition Using Artificial Neural Networks

Cited by 96 publications

References 26 publications

Intelligent integrated maintenance for wind power generation

Intelligent integrated maintenance for wind power generation

Machine Fault Diagnosis and Prognosis: The State of The Art

Predicting component reliability and level of degradation with complex-valued neural networks

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