Prognosis of Defect Propagation Based on Recurrent Neural Networks

Malhi, A.; Yan, Ruqiang; Gao, Robert X.

doi:10.1109/tim.2010.2078296

Cited by 262 publications

(106 citation statements)

References 29 publications

Supporting

Mentioning

105

Contrasting

Unclassified

Order By: Relevance

“…The output of ANN is RUL prediction or performance degradation assessment, which is used for conducting effective maintenance strategies. ANNs widely used in fault prediction include BPNN [91][92][93][94][95], radial basis function network (RBFN), and RNN [96]. Ahmadzadeh, et al [94], proposed a three-layer feedforward BPNN for RUL estimation of grinding mill liners, which considered degeneration and condition monitoring data as the inputs of ANN, and used RUL as the output of ANN.…”

Section: Annmentioning

confidence: 99%

“…Rodriguez, et al [95], presented ANN (six input layers, three hidden layers, and one output layer) to predict and simulate the behavior of life-cycle assessment in blades of steam turbines. In view of the shortcomings of traditional incremental training methods in long-term prediction, Malhi, et al [96], proposed an RNN based on competitive learning method to improve the accuracy in long-term prediction of rolling bearings. Mahamad, et al [97], used feedforward neural network and the LevenbergMarquardt training algorithm to predict the RUL of rolling bearings.…”

Section: Annmentioning

confidence: 99%

See 1 more Smart Citation

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

show abstract

Section: Annmentioning

confidence: 99%

Section: Annmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

show abstract

“…The inputs to the nodes of a feed-forward network, such as multilayer perceptron (MLP) with back-propagation network (BP) training algorithm, rely only on the output of the preceding layer under the current iteration [41]. For a dynamic network, such as recurrent neural network (RNN), general regression neural network (GRNN), or time delay network [129], the nodes' inputs also depend on information from previous iterations. These networks are supervised learning algorithms, which require the actual outputs for training.…”

Section: Artificial Intelligence-based Data-driven Modelsmentioning

confidence: 99%

Cloud-enabled prognosis for manufacturing

et al. 2015

View full text Add to dashboard Cite

Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes across spatial boundaries. These activities can improve accuracy and reliability in predicting resource needs and allocation, maintenance scheduling, and remaining service life of equipment. As an emerging infrastructure, cloud computing provides new opportunities to achieve the goals of advanced manufacturing. This paper reviews the historical development of prognosis theories and techniques and projects their future growth enabled by the emerging cloud infrastructure. Techniques for cloud computing are highlighted, as well as the influence of these techniques on the paradigm of cloud-enabled prognosis for manufacturing. Finally, this paper discusses the envisioned architecture and associated challenges of cloud-enabled prognosis for manufacturing

show abstract

“…The statistics extracted from vibration signal are the most common features used as the degradation indication, such as kurtosis, RMS and peakto-peak value, etc [1,2]. But some of these features can only give an apparent fault warning at specific phase.…”

Section: Introductionmentioning

confidence: 99%

An effective health indicator based on two dimensional hidden Markov model

Ming

Liu

et al. 2017

J Mech Sci Technol

View full text Add to dashboard Cite

It is still a great challenge to find an effective degradation indication for health degradation assessment. For that purpose, many computational algorithms have been motivated or suggested. These algorithms commonly use the multiple statistic-based features obtained through calculating the vibration signal to build health indicator which does not consider the internal relevancy among the features. For the purpose of building an effective degradation indicator with pronounced tendency, a novel method is proposed. With the ability of keeping the local structure of data set, Locality preserving projections (LPP) is used to reduce the dimension of the general feature set with the main information remained. Since 2-Dimensional Hidden Markov model (2-D HMM) not only take the relevance between the individuals of fault features into consideration but also capture the global characteristics of the multiple features, 2-D HMM based negative log likelihood probability is built as a new degradation index. The experimental results indicate that the proposed indicator show great abilities to make degradation performance of the bearing and is sensitive to weak defects.

show abstract

Prognosis of Defect Propagation Based on Recurrent Neural Networks

Cited by 262 publications

References 29 publications

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Cloud-enabled prognosis for manufacturing

An effective health indicator based on two dimensional hidden Markov model

Contact Info

Product

Resources

About