Artificial Neural Network Based Fault Diagnostics of Rolling Element Bearings Using Time-Domain Features

Samanta, Biswanath; Al-Balushi, K.R.

doi:10.1006/mssp.2001.1462

Cited by 622 publications

(304 citation statements)

References 12 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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“…Results of countless researches have proved the effectiveness of using signal characteristic parameters to distinguish interested fault related information from noise corrupted signal. Significant progress has been made in related research [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Shock Pulse Index and Its Application in the Fault Diagnosis of Rolling Element Bearings

Sun¹,

Yuhe²,

Lin³

2017

Preprint

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Properties of time domain parameters of the vibration signal have been extensively studied for the fault diagnosis of rolling element bearings (REB). Parameters like kurtosis and Envelope Harmonic-to-Noise Ratio are most widely applied in this field and some important progress has been made. However, since only one-sided information is contained in these parameters respectively, problems still exist in practice when the signals collected are of complicated structure and/or contaminated by strong background noises. A new parameter, named Shock pulse index (SPI), is proposed in this paper. It integrates the mutual advantage of both parameters above and can help effectively identify fault related impulse components under the interference of strong background noises, unrelated harmonic components and random impulses. The SPI optimizes the parameters of Maximum Correlated Kurtosis Deconvolution (MCKD), which is used to filter the signals under consideration. Finally, the interested transient information contained in the filtered signal can be highlighted through demodulation with Teager Energy Operator (TEO). Fault related impulse components can therefore be extracted accurately. Simulations and experiment analyses verify the effectiveness and correctness of the SPI.

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Artificial Neural Network Based Fault Diagnostics of Rolling Element Bearings Using Time-Domain Features

Cited by 622 publications

References 12 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

Shock Pulse Index and Its Application in the Fault Diagnosis of Rolling Element Bearings

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